Welcome to Monet Café. I'm artist Susan Jenkins and today's tutorial is of some sheep in this lovely snowy landscape. Let's dive right in and start talking about these products. There's only two products in this lesson. One is the Sennelier. I love this brand of pastels. Half-stick set of 40 sticks of pastels. These are half-stick sizes. I actually love the sizes, love the colors. I'll be talking more about this set. Also you can find my product review video for this set of Sennelier pastels in my Amazon shop. This is a wealth of information for you within my shop where you can click on different idealists and also if you click the videos tab it will take you to my product review videos. You can learn so much about different soft pastel products and in this particular video and all of the videos you can watch the video and you can click on the product links right beneath it and it just so happens that this set of soft pastels is on a really good sale on Amazon. I do make a small little commission on this but it doesn't affect your price at all and this set is currently 55% off, only $43 approximately. It's like a dollar a stick. That's pretty good. The other product is a sheet of unsanded pastel paper made by Kanson Métants. It comes in various colors and as of the making of this video it was on sale on Amazon for only around $12. So the whole pad of paper and the pastels were less than $60. Proving you can get started with soft pastels on a limited budget. And I'll be using a beautiful orange color from this pad. I wanted to choose a warm color because the snow had some cooler colors in the scene. The reference image is from unsplash.com a wonderful site to get copyright free reference images. And let's jump right into tip number one the sketch. We want to get our sketch accurate but not tight. Anytime we're painting an animal or a person it is really good to get some accuracy and also I like working from similar proportions. In other words your reference image is the same size as your painting image. Let me show you a quick tip on how to do that. I'm working from an iPhone but most phones have photo editing software so pull up your photo click the edit button and if you're using an iPhone like me you're going to see that little crop icon tool. I've circled it in red right here.  You can click around on some of the different proportions you can also move the photo within that frame and see kind of how you want to crop your image. Now I wanted to do an 8 by 10 but you may notice there's not an 8 by 10 option in the list. So a 7 by 5 is equivalent to an 8 by 10 so that's the size that I chose but it's neat that you have all of these options you can even do a free form and crop it however you want and once you're finished and you get the size and the proportion that you want you simply click done and your photo is saved to your phone iPad or whatever device you might be working on and then once you have that crop and proportion measurement saved in your phone then it's time to get your surface ready with equivalent measurements. I love using those mats like mats you put in a picture it's 8 by 10 on the outside and 7 by 5 on the inside. For the sketch I'm using a white charcoal pencil made by Derwent. Since the surface was a little darker in value I thought the white charcoal would work well and charcoal works really well when you combine it with soft pastel. Now here's where I want to reiterate the first part of tip number one which was we want to create our sketch accurate but not tight and I particularly don't like the grid method if you're familiar with that it's where you make a grid on your reference image and you make a grid on your painting surface and you literally are drawing each little square and it that's really great when you're drawing something large but I find drawing in that method or sketching that method creates an image that feels tight and not painterly so what I do you saw me make just little halfway marks on each side of my drawing surface and that allows me to get an idea of where things are in general not getting so tight and the more you do this the better you get but this is also an example of why having your reference image proportions and your painting image proportions the same is going to help you with the sketch immensely and that's because everything is relative your spacing and your proportions were are going to be the same even if your drawing image looks a lot larger than if you're looking at it on your phone the proportions are all still the same I am constantly looking at negative shapes while I'm painting and where things fall in relation to the perimeters of the surface for example you can ignore what I'm sketching right here but if you look back at the reference image you can see there's a little triangular shape at the lower left corner that's the space between the one sheep the main sheep and the other sheep and so that really helps me to look at all those shapes and I try to tune out what I'm painting and rather I just look at the big shapes and their spatial relationships to each other I do find when I'm painting animals or people that the time it takes me to finish a painting is substantially longer than a landscape painting and it is because I want to get accuracy and not tight I want to keep it painterly but it will quickly look amateurish if you have certain things off when it comes to the human face or even animal faces and forms and before we move on to the pastel painting portion I'd love it if you would like this video and subscribe to my channel and also too the footage from here forward on my patreon page will be a majority of real-time content if you would like to become a patron on my patreon page it's real easy patreon.com Susan Jenkins and now it's time to apply the pastels and move on to tip number two tip number two is so important with soft pastel painting a reminder to keep a light touch resist that urge to apply too much pressure it will reduce your ability to layer with soft pastels and when you're working on an unsanded surface as in this case with the Canson-Mittant surface you have limited layers already I find that I can get three to five layers on unsanded surfaces and have them still retain their vibrancy much like painting with oil or acrylic we use the technique of layering when we paint with soft pastels it gives a beautiful not only effect of colors interacting with each other layering on top but it also helps us to create a painting that feels more true to life in other words if we were to paint not using any layers at all it would literally be like how you color in a coloring book you know color this section that section and the result is very artificial and not true to life so try to keep in mind to keep a light touch and it will give you the ability to have more layering potential regardless of what surface you're working on by the way I'm working on the back side of this Canson surface the front side of it if you take it out of the pad has a little bit of a pattern it's like a honeycomb type of pattern it can look very neat and textural but the back side is a little bit more smooth all right now I am moving on to tip number three and tip number three is a reminder to work the hole this is also called blocking in and basically it's a good idea to block in large sections and areas and avoid and resist that urge to get too detailed on any one area so I'm trying to work the hole here even though I will pause at certain times and get a bit more detailed just to get my accuracy of my drawing and my sheep correct for the most part I continue to try and get the large shapes in working the hole for the most part to the end of the painting you do have the ability to do a little blending like you saw me do with my finger just a bit ago and there were some areas in this painting that I did kind of softly blend I felt like his fur under his or her chin there was going to be a dark value and a little more subdued someone to soften it up a bit but you'll see that many of the strokes that I'm making are not blended at all I let the pastels blend themselves as I work and I also use directional strokes which leads us to tip number four back to my coloring book example we're not just filling in color by making directional strokes we are literally giving form and three-dimensional qualities to the elements that we're painting as in the sheep I'm not only looking at the direction of how the fur is laying on top of the sheep I'm also in my mind imagining my hand as if it was literally curving around the form and shape of the sheep that I'm painting now this works also with landscape painting with flowers we're trying to connect our mind with how the element is moving or how things are laying on top of one another and giving your strokes that feeling of movement gesture and direction I have most things blocked in at this stage except for the other sheep that are in the background they're basically going to be darker values but I did want to take pause and start working more on the face of my main focal point sheep I mean really if you don't get this right this is going to be the element that you really want to be the focal point so there are things that I want to slow down I know you're seeing the speed version of it but me personally to slow down and make sure I get these elements right this isn't this is a bonus tip here I'm not including this as one of the tips but keep in mind a couple of things one is you got to get your eyes right when you're working on an animal or a person that's going to be the showstopper for any type of painting that has a person or an animal I mean you don't have to over detail it but you want the accuracy of the eye placement and the eye shape and the shadows in the eye as well another bonus tip is to remember that you don't have to use the exact colors that you see for example I have a lot of the shadowy colors in this sheep that are not really what I'm seeing in the reference photo this is a question I get all the time like how did you know to use that kind of magenta color there or why did you use those teals in the shadows kind of under his sides of his face and it has to do with a couple of things one is you're not bound by the color you see in your reference image as long as you get the value close or similar value just means the degree of lightness or darkness so I knew that because I had take for example some of the teals that you see kind of in between his two eyes and kind of underneath his cheek there I knew the value was close to what I saw in the reference image so I knew I could grab that kind of neat bluish teal color and use it and it's going to create a painting that is more artistic and fun with color and also it is going to allow you to not be stuck to having having to have every single color in the rainbow in other words you can work with a limited little 40 set like this the whole painting was done with this one little set of pastels and another thing I really like about using one set of pastels I mean as long as it has a decent amount of color and values is that you will get exciting color results you're forced to have to choose by value versus color alone another thing to keep in mind when choosing your colors is to use temperature as a priority I know that in shadowy areas colors are going to be cooler and that's going to lean a little more towards blues and purples on the color wheel so you can use your artistic license with those things now I'm getting in these background sheep and I used a black I don't often use black it's I feel like there are more creative ways to create a dark by layering other colors or using a really dark dark purple but because I was limited with this little set I didn't want to use that very dark green in the set that's at the lower left of the set so I decided to use the black and do a little layering on top of it to kind of change the color see with soft pastels we really can blend colors to a degree not quite like oil and acrylic but by layering colors you you create new colors and more exciting colors often so I'm just getting in these cute little shape of the sheep in the background keeping in mind they do not need a lot of detail because they are not the focal point and they're in the distance things lose detail as they move further into the distance and if you started to give detail to anything too far away that's another thing that's going to quickly cause your painting to feel a bit more amateurish so these are just rules of nature and physics and the more that you learn about how our world works the more you will improve as an artist and I always like to think of being an artist is like being a student of nature it's a beautiful thing that happens we learn to really analyze and enjoy and explore the world more and now time for tip number five which is we typically layer dark to light with soft pastel in general in general we apply the darker values first as you can see as I've been working the sheep I put down those darkest kind of black colors underneath and then I'm gradually layering the lighter values on top and also this is because this is how the world works typically things deeper like in the fur or underneath are going to be darker and then the sunlight hits on the outside of objects making the lighter values appear on top so that's a really simple rule with painting that's going to help you immensely and now I want you to sit back relax enjoy the painting process to some music but also keep in mind the tips that I've talked about so far and observe how I'm using those tips in this painting and don't go away because final tip number six is at the end and it is of utmost importance I can't stress it enough so enjoy and I'll be back I must say I really enjoyed creating this painting often painting can be an experience and not just something that's laborious and thinking about your final project or painting I like to create a moment put on some music light a candle get a cup of tea and make an experience out of it and that leads me to the very important tip number six which is don't forget to have fun don't be too hard on yourself if you're a beginner and just getting started or if you've been away from painting for a while and you're in a little bit of an artistic rut it's best just to have some fun do some studies and don't even worry if your painting is going to be something you'll share on social media and everyone will see it and applaud much of art is just enjoying the experience and I pray that is something that you will do and if you recreate from this painting I hope you'll share it and if you do you can tag me on instagram at Susan Jenkins artist and on facebook at the art of Susan Jenkins if you're a patron of mine you will be getting the real-time footage and lots of other goodies thank you so much for joining me in today's lesson I hope it blessed you and it would bless me if you would consider subscribing to my channel liking this video and if you would like extra content or just to support Monet Cafe consider becoming a patron of mine on my patreon page all right artist until next time god bless and happy painting


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This is the first of a few lectures on elliptic functions. I'm not following any particular textbook, but Cambridge University Press has recently reprinted the classic book Course on Modern Analysis by Whitaker and Watson, and this contains a rather in-depth collection of chapters on elliptic functions. So first of all, what is an elliptic function? Well, a periodic function is a function like sine of x. So you know sine of x plus 2 pi is equal to sine of x, and here this 2 pi is the period. It's also got other periods, so obviously 4 pi, 6 pi, and so on are also periods. But all the periods are essentially multiples of 2 pi, and you can ask, can you have a function with two independent periods, say the periods are omega 1 and omega 2? Well, at first sight it seems you can't really, because if omega 1 over omega 2 is rational, then you can just take the greatest common denominator, greatest common divisor of these, and there's essentially only one period generating them. If it's an irrational real, this means the periods are dense, and this means the only function with all those periods are essentially constant if they're continuous. So there doesn't seem to be any way of having two independent periods. Well, that's if you're talking about functions of a real variable. So what we're going to do is to talk about functions of a complex variable. So we're going to be looking at elliptic functions f of z for z complex such that f of z plus omega 1 is equal to f of z, and f of z plus omega 2 is equal to f of z, where the ratio omega 2 over omega 1 is not real. As if it is real, nothing very interesting happens. Unfortunately, one of the main problems in the theory of elliptic functions is that notation for elliptic functions is a complete mess and not standardized at all. So in every author who writes about elliptic functions invents that own system of notation. So I'm using omega 1 and omega 2 for two of the periods, but other books will use all sorts of different notation for this. So the periods form a sort of lattice. So if you take 0 here, and omega 1 might be this complex number, and omega 2 might be a complex number here, and then all the other integral multiples of omega 1 and omega 2.  So we get a sort of grid in the plane. And this means that the elliptic function is going to be the same if you shift by any one of these lattice points. So I have here a picture of an elliptic function. This is from the book by Janke and Emder. And here's an elliptic function that we might discuss later. It's one of Jacobi's elliptic functions. You can see it's sort of periodic in this direction. It's also periodic in that direction. So what I want to do this lecture is basically find all elliptic functions. So the first question is, can we find any holomorphic elliptic functions? And the answer is these all have to be constants, so they're not very interesting. And the reason for this is that f is bounded in fundamental domain. So a fundamental domain is going to be a region that looks a bit like this. And this fundamental domain is compact, so f being continuous is bounded in it. So it's bounded in a fundamental domain. And since it's periodic, it must be bounded everywhere, because every point is equivalent to a point in the fundamental domain. And if a holomorphic function is bounded on the whole plane, it must be constant. And for this, you use Lueville's theorem from complex analysis, which says any bounded holomorphic function has to be constant. So we should we should we should allow poles. So f is going to be meromorphic. It's just mean that it's allowed some poles. So the next question is, how can we find examples of elliptic functions? Well, there's a very general method. If you've got a group g acting on some vector space v, and you want to find something in a vector space fixed by g. So you want to find a vector of gv equals v for all g in v in g. There's a very simple way of doing this, you just take any element v of the vector space and just sum over all things in g acting on. And this is fixed by g if this series converges. So this is the catch. It doesn't always work because this group g might be infinite and then it's not clear if this converges. Now what we're going to do is to take g to be our lattice, which is a sum of two copies of z under addition. And we're going to take v to be meromorphic functions. And the action is just translation. If lambda is an element of the lattice, then and and we've got some function f, then we can change f of z to f of z plus lambda. And you can easily check this gives us a group action. So what we want to do is to take sum over all lambda in the lattice l. So that's lambda equals m lambda 1 plus n lambda 2 of f of z plus lambda. So let's call this big f of z. And there's a pretty good chance that this might be elliptic as long as this converges. Well, we've got this convergence condition. And in order for this to converge, the terms must at least tend to zero. So we must have f of z tends to zero as the absolute value of z tends to infinity, because otherwise this series isn't going to converge. So there are some obvious examples of functions like this. We can try f of z is one over z to the n for some integer n. So let's have a look at the first few of these and see if it converges. So let's try f of z was one over z and check for convergence. So we're taking sum over lambda in lambda of one over z minus lambda z plus lambda. I don't really care. So in order to test this for convergence, what we do is we take the complex plane and we draw a lot of circles of radius one, two, three and so on. And then count the number of terms of lambda inside each circle. And there are going to be about some constant times n terms in the nth circle or the nth ring. And the terms are all going to be about one over n in absolute value. So lambda is going to be approximately n and lambda is very large. So it's much larger than z. So the sum we get looks like sum over n greater than equal to one of n over n. And this obviously diverges rather badly. It doesn't work if you try this function. Well, let's try f of z was one over z squared. Well, here we get the same argument except the terms are about one over n squared. So we get sum of n over n squared. And this still diverges, but only just. It's right on the borderline of divergence. You know, this is this is the harmonic series, which, which does tend to infinity, but very slowly. And if you change this exponent n to anything bigger than two, this will converge. So we can try f of z equals one over z cubed. And here the series sum over one over z minus lambda cubed now converges and gives and gives an example of an elliptic function. So you can see it's got a pole at every point of lambda. And this is this is why elliptic functions sort of look like this. You can see there are all these poles here and the poles kind of live on some sort of lattice. This picture actually has poles of order one at every point rather than order three. And we'll talk about that later. So so we've got plenty of elliptic functions. It turns out that the most convenient function to use is not this one here, but this function here. Well, there's a bit of a problem with it because it diverges. So it doesn't actually give us an elliptic function, but it's so close to converging that we can make it converge by by just nudging it a little bit. So let's figure out why why it doesn't converge. Well, the problem is that if we take one over z minus lambda squared, this is going to be approximately one over lambda squared. And the sum of one over lambda squared diverges. However, it's if you expand further, it's sort of one over lambda squared plus two z over lambda cubed plus something over lambda to the four and so on. And for these bits, there's no problem. The terms for one over lambda cubed and one over lambda four and so on can converge if you sum them over all lambda. So the thing we have to worry about is this bad term here. We notice this is just a constant. So what we can do is we can just subtract off this constant from all the terms and we get sum over lambda in lambda of one over z minus lambda squared minus one over lambda squared. And what we've done is this is formally periodic but isn't periodic because it doesn't converge. So what we've done is we've sort of subtracted an infinite constant from it to make it converge. Well, there's still one more problem because this is infinite if lambda equals zero. So we omit this constant if lambda equals zero. So the sum we get is a sum over all these things for lambda in the lattice except when lambda is zero, we just omit this term. And this is the this is the famous Weierstrass p function which is defined by this sum here. Weierstrass used a capital letter p from some funny script alphabet and every letter of the script alphabet seems to be lost except for p which lives on as this as this strange sort of fossil in the mathematical literature. So well the problem is is p periodic because you see it's not at all obvious that it is periodic. I mean these terms are not invariant under changing z to z plus an element of lambda because there's this sort of funny infinite constant and we have to worry about this a bit but I mean we can see that it is in fact periodic and this follows from the first from these two facts. First of all if we take p of z and differentiate it it's equal to minus two times sum over one over z minus lambda cubed which is periodic. So it's derivative is periodic. This doesn't mean that the function itself is periodic because you can get functions with periodic derivative that aren't periodic for instance you would have a function that sort of keeps going up in bumps like that. However p has another property that p of z is even so p of z is equal to p of minus c and that's kind of obvious if you look at this because the terms if you change z to minus c and then change lambda to minus lambda you just get the same terms and if a function is periodic and even so if it's if the derivative is periodic and the function is even you can easily check a little exercise this that means the function is periodic because you know p of z plus lambda must be equal to c of lambda plus p of z for some c depending on lambda and if you just look at the term for minus lambda and do a little bit of algebra you can see that this the fact that this is even implies c of lambda must actually be zero. So we found a periodic function. In fact I can draw and I have an explicit picture of Weierstrich's function. It looks like this. You can see the pole is a little bit fatter than the poles in the previous picture because these were poles of order one and this is a pole of order two which is kind of a bit bigger and heftier. So we found one periodic function. We found several periodic functions because we can take various other inverse powers of z. So let's show that we've in fact found all periodic functions or at least we can do fairly simple manipulations to these to find all all periodic functions because we notice that if fc is periodic then so is f of z plus any constant c and so is p of f of z over q of f of z where p and q are polynomials. We need p and q to be polynomials so this is still meromorphic. So if we've got one periodic function we can get lots of others by these simple tricks and we now want to show that we can in fact get all periodic functions just by applying this method here to the ones we've already found. So first of all we can get rid of of poles not on the lattice l and here if you've got a periodic function we just multiply it by p of z minus p of c if f is a pole at z each c or maybe you need to raise it to some power to get rid of a pole. So by multiplying f by by polynomials in viastrisis function we can get rid of all poles that are not in the lattice l and we just left with an elliptic function whose only poles are an l. Next we we can make f even so f of z is equal to an even function even periodic function plus an odd periodic function the usual way you write a function is the sum of an even and odd function and the odd one is equal to p prime of z times an even function. So this is the derivative of viastrisis function which you can easily see is an odd function so if you divide an odd function by an odd function you get an even function. So it's enough to do an even periodic function so we we can assume that f is even the only poles are on l and now if we look at viastrisis function p of z it's equal to one over z squared plus some other terms I don't care about so if you raise it to the power of n it's equal to one over z to the two n plus something or other so taking f of z minus a linear combination of p e prime and so on we can kill the pole at zero so so I can assume f has no poles because we've got rid of them all and by what we said earlier any elliptic function with no poles at all is constant so what we've shown is that any elliptic function is some elliptic function whose only pole is on the lattice points divided by a polynomial in p and we've shown that any elliptic function whose only poles are at lattice points is a polynomial in p plus the derivative of p times a polynomial in p so we've got a complete description of all elliptic functions so let's write that this out explicitly elliptic functions with poles on l can be written are are just all polynomials in x and y where x is equal to the viastris function y is equal to the viastris function differentiated except we've got to question out by a relation and you notice that if we take the viastris function derivative and square this is even and it looks like z to the minus six plus something so it's equal to sorry four z to the minus six so it's equal to four times p cubed plus something times p squared plus something times p plus some constant because any even function whose only poles are at lattice points is a polynomial in p this term is in fact zero as you can check without too much difficulty and these are called minus g2 and minus g3 um for no particularly good reason so um we have to quotient out by x q 4x cubed minus g um 2x minus g3 minus y squared so here we have a explicit description of all elliptic functions the ones with poles only and l are just this polynomial ring quotient out by this ideal and the ones with poles anywhere are just um are given by the field of quotients of this ring here so so so we we know all elliptic functions in a in a reasonably explicit way um this actually gives us a sort of um isomorphism um if we take the complex numbers and modulo out by this lattice so we identify any two complex numbers if they differ by an element of this lattice then we can actually identify this with the curve consisting of um numbers such that y squared equals 4x cubed minus um g2x minus g3 so this is um some sort of a curve in the plane which might look something like this uh should say c over l minus l minus the zero because we're mapping the point z to the point p of z e prime of z is even the point x y so here z is a point of the of the complex numbers or the complex numbers modulo l minus zero um so in other words um the the the space of complex numbers modulo lattice simply can be identified with a cubic curve uh in the plane um well there's a question that um um might have occurred to you which is we managed to get an elliptic function of the pole of order two um at um lattice points why can't we get an elliptic function with a pole of order one at lattice points by taking this series sum over one over z minus lambda and um getting rid of the divergent terms just as we did with with the other one so um if we take one let's take one over z plus lambda so this is going to be approximately one over lambda minus c over lambda squared plus c squared over lambda cubed and so on and these terms here are small and these terms here cause a problem so let's just subtract them as we did before and take sum over lambda of one over z plus lambda and then subtract these difficult terms plus c over lambda squared and this now converges and so we get a function which is sometimes called the zeta function it's rather confusing it's it's the viastri zeta function which has nothing to do with the reamon zeta function and let's see if this is an elliptic function well first of all zeta prime of z is um minus um um the viastrice function so it's periodic um and this means zeta of z plus a period is equal to zeta of z plus um some constant which is traditionally um has a factor of two in there for obscure historical reasons and similarly this can be zeta of z plus another constant eight of one and eight of two and the question is are these a zero well in the case of viastrice's p function managed to prove that corresponding constant was zero because p was even however you can easily check that zeta of z is odd and this doesn't imply that this vanishes because it's perfectly easy to find odd functions which satisfy relations like this and in fact these constants can't both be zero um you can see this as follows um there's actually a relation between these um constants which is that um two pi i is equal to the integral over c of zeta of z z here um c is going to be the integral round fundamental domains and not omega one omega two and so on and we better avoid the um poles on the um fundamental domain so I'm actually going to integrate round something it looks a bit like this and this integral is equal to two pi i because this is two pi i times the um sum of the residues um and um we can also work out the integral explicitly because the integral along the bottom of the top almost cancel out by almost periodicity and the integral along the top bottom in the top turns out to be essentially um omega one times eight of two and similarly these two integrals here almost cancel out apart from the factor of omega two times eight of one and so what would this turns out to be if you keep careful tracking the signs is two eight one times omega two minus two eight two times omega one this gives le genre's relation between these um these four numbers here so you see eight of one and eight two can't possibly both be zero because this this factor here is non-zero um if you look this up in books by the way you will sometimes find this differs by factors of two from um formulas you sometimes find and that's because periods are sometimes taking to be two omega one rather than omega one as i've taken them here um i just finished with a little historical note about why elliptic functions are called elliptic functions they don't actually very much to do with ellipses and the name comes from elliptic integrals which are integrals of the form dt over square root of a cubic or quartic polynomial in t and you get integrals like this if you try and work out the arc length of an ellipse which is where the name elliptic comes from um if it's if there's a quadratic polynomial here you can easily do this in terms of arc signs and arc cosines and so on um but if this as degree greater than two you can't usually do that um however you can do it using elliptic functions so suppose i want to integrate um say dx over the square root of four x cubed minus g two x minus g three so here we've got a cubic in x what we can do is we can write x is p of z you remember that p and z satisfies a funny differential equation so p prime z squared is equal to four x cubed minus g two x minus g three where x is p so this just turns out to be the integral of dx um divided by p prime of of um z um so this is um d p prime of z so d p of z over p p prime of z um which is is is is just dz so um this mess here just turns out to be really just a differential dz and this means you can sort of integrate this and if you do this you find z is actually equal to the integral from p of um z to infinity of this integral here dx over square root of four x cubed minus g two x minus g three so you see that the elliptic function actually appears in a slightly funny way it's not the value of this integral from from something to something else it's what you have to put in to this interval to get z um and elliptic intervals were originally discovered in this rather roundabout way and this caused tremendous complications because defining them as the inverse of some function is really rather a mess and this function you're defining them as an inverse of is a rather messy function of branch points and so on and everywhere so um it's when you're doing elliptic functions it's probably best to forget about the the historical origins of them because that's that's that's just a rather unnecessarily complicated way of doing things um okay the next lecture will be more about properties of the via strice elliptic function


Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Officer of Data Diversity. We'd like to thank you for joining the current installment of the Monthly Data Diversity Webinar Series, Real World Data Governance with Bob Siner. Today Bob will discuss the impact of data governance on data literacy sponsored today by Irwin by Quest. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just to note that Zoom defaults the chat to send just the panelists, but you may absolutely switch that to network with everyone. For questions, we'll be collecting them via the Q&A section or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag RWDG. For questions, to find the chat or the Q&A panels, you may click those icons in the bottom middle of your screen to activate those features. And as always, we will be sending a follow-up email within two business days containing links to the slides, the recording of this session, and anything else requested throughout the webinar. Now let me turn it over to Sue for a brief word from our sponsor Irwin by Quest. Sue, hello and welcome. Hi, thank you for having me. I think everybody's really in line for a treat today with the presentation that Bob is going to present. I was just kind of looking over some of the slides and I've not seen it really communicated before the difference between data governance and data literacy and the impact that one has on the other. So some really good information coming up. I wanted to start real briefly with a survey that we have been working with IDC on and on the next slide, Shannon. Just to tell you a little bit about what we see going on in the marketplace. The last survey was a series of questions having to do with what do the end users need to improve upon their data and make better data decisions. And their expectations were that they would be able to see where the data was coming from understanding the scores of the data itself and the impacts on the downstream elements that the data is flowing to understand it in business context. So having that data literacy across many different places of the organization, not just leaving it up to one part of the organization to create those standards and understand the data, but to understand the data across the entire enterprise. And then finally, they also expect to understand where to go to get this data but we're still seeing that as a major problem. So, the other question that we asked them was where do we need the most improvement in being able to find the answers to your data, and they equated back to the top four expectations. So, when they needed to be able to find their expectations were not being met, and really needed a lot of improvements along these lines. Next slide. So from our perspective, how can data intelligence, the overall data intelligence program help you with data literacy with data governance. And from our perspective data literacy starts with easy to use and easy to configure visuals on the data itself. So being able to go to a really good user interface that's very familiar has some Amazon like organizations has familiar search screens to really help and aid and increase the user adoption. These visuals like data lineage that support both high level understanding of the scope of where that data is coming from as well as down to the column level will help you really quickly validate the data that's coming is the data coming from an authenticated source is something that has been governed. Do we have a common understanding of this data. So having these visuals online in a system at their fingertips is really helping them solve the data intelligence problem. And then behind the scenes, everything that that's appearing in those screens is automated so being able to automate the data management operations in general is something that's really helping our users. Scale out that data literacy across the organization. So being able to scan metadata for the inventory of your data landscape and and being able to see data quality results in that information is my bad data being propagated to another business unit. It really tells a good story between understanding how good and bad data is propagating, and it's also helping you detect where you need to go do some forensics on the data, where you might be having some issues. And the other part of automation is also rationalizing the business to the technical pieces of the data itself and your business assets. So being able to use something like AI and connecting with data models will help you get you on to a clear path to understanding the business impact to the data and vice versa. Next slide. Yep. This is the right side. We want to from a Irwin perspective. I'm just going to briefly tell you a little bit. Irwin has started and it's and it has made its name from a data modeling perspective over there on the left hand side. Thank you. Where you where we're understanding our business assets in evolve and how they're modeled out as well as the data models your logical physical and conceptual data models so that you can start to really categorize your new data bricks or snowflake environment and start with good healthy data standards. Our data intelligence suite is integrated with those great data models and I'm going to talk about that on the next slide, but it's giving you a business flow into the discovery assets through a catalog, as well as understanding the data quality at an different level and also bringing in the data literacy aspects so that you can reach a common understanding and have more controlled freedom in the usage of that data because you have transparency down into how that information is flowing where it's sourced from and how it's been defined. My last slide is on what makes us different from an Irwin perspective. As I said, we started 30 years ago inside of the Irwin data modeling space as well as the business assets and processes space. And we feel like having Irwin models or any modeling tool, you're getting the best and the brightest in a room to talk data structure and to set those standards from day one. So to be able to use and accelerate a data intelligence program using that great those great data standards already is helping folks get up and running in their V.I. program much quicker. And we've also defined critical data elements P I take sensitive data and the logical in the business information right into the data lineage so you can start to see it from a business perspective. So from our perspective we're helping you accelerate your data intelligence program with the highest caliber of enterprise data standards that are available to you already in your organization. So, with that, I'm going to turn it back to Shannon who's going to give it back to Rob to talk about data literacy and data governance so thank you for your time. Thank you, Sue and thanks to a room by quest for sponsoring today's webinar and helping to make these webinars happen. And if you have questions for Sue or about Irwin feel free to submit them in the Q&A as panel as Sue will likewise be joining us for the Q&A portion at the end of the webinar today. Thank you to our speaker for the series Bob Siner Bob is the president and principal of K I K consulting and educational services, and the publisher of the data administration newsletter T Dan calm Bob specializes in non invasive data governance data stewardship and metadata management solutions. And with that, I'll give the floor to Bob to start his presentation Hello, and welcome. Hi man. Hi Sue. Hi everybody. Thank you so much for the the nice introduction and Sue nice for. Thank you very much for saying the nice things that you said about my slides as kind of a preview to the session. We're going to give today. And of course it's always great to be able to speak to you once a month. This is a really hot topic. This is a topic that you know we schedule some of the topics for the webinar series in advance and they all seem to be hitting at a time. That they're really hot. And this is a very hot topic right now data literacy, and as Sue was saying there's a lot of organizations that there's not a lot of consistency or understanding as to how the two relate or what is doing, whose responsibility is data governance whose responsibility is data literacy. So I'm glad that we have this as the topic for today. So before I start into the webinar. As always I'd like to just share a little bit of some of the things that I'm doing. As you can see I'm very engaged in the data management data governance industry. Next month and it's very interesting that Sue talked so much about data intelligence, because next month we're going to talk about using data governance to build data intelligence so I don't know if that's a term that you're using in your organization yet but it is starting to be used by organizations. I've got some upcoming events that I'll be speaking at the one that is the newest on the list is I'll be speaking at the DGI Q East conference in Washington DC in December. I talk a lot about non invasive data governance so if you're interested in learning more about non invasive data governance, my book is available in multiple different formats multiple different languages now. There are also online learning plans available through data diversity on non invasive data governance non invasive metadata governance and glossaries dictionaries and catalogs. And please go visit the data administration newsletter and a my consulting business and I'm also working at Carnegie Mellon University as as I have time to be able to do that. It's really interesting so this webinar today I really want to get some information from you during this webinar as well and I can't see the chat right now but I'm curious as to, you know what some of you will say to some of the questions that I'm just going to randomly ask but in this webinar, the first thing that I want to do is describe the relationship between data governance and data literacy. I want to talk about ways that data governance can be used to help to enable data literacy if we have determined that they're not the same thing and what the differences between these things are. A lot of organizations are trying to figure out where data literacy should reside in the organization so who should own data literacy, just like since many of you are data governance practitioners you've asked the question, who is, who is the great owner of your data governance program well we need to be asking the same thing about data literacy. We need to include since you'll find here very quickly the data literacy has a lot to do with the understanding of the data and the ability to be able to use the data. And there's a lot of understanding that needs to be provided to the organization that data governance needs to be one of those topics that you're including when you're trying to make your organization data literate. There's a direct impact of data governance on data literacy. I like to start my webinars by just real quickly going through some basic definitions, and then we certainly want to define what data literacy is here in a minute. These definitions keep getting shorter and shorter as I tried to apply them to the context of what we're talking about in today's webinar, but data governance being the execution and enforcement of authority. So following of the rules that we're not being asked to follow or being told that we need to follow. There's the formal accountability for data being data stewardship, and you've heard me say many times that potentially everybody in the organization is a data steward, if a person has a relationship to the data as a definer producer or user of the data, and they're being held formally accountable for how they're defining or how they're producing or how they're using the data. They are a data steward so basically anybody and everybody could be a data steward, but then the definition of metadata is really applicable to the context of what we're talking about today. So metadata is data that improves both the business and technical understanding of the data. And in order to become literate as an organization, when it comes to data. It means that we're going to need to understand the data of the organization. So, when I did was I went out and I got four definitions of data literacy, and I'll just share them real quickly with you there's the Gardner definition. There's the, the data literacy project I didn't know there was a data literacy project they have a definition of data literacy. I got a more random one that I picked from a list that I had, and then one that came from data diversity. If you look at them the words that I highlighted in red are really what's most important here. So data literacy and you may have seen some of these definitions before data literacy is the ability to understand share common knowledge and have meaningful conversations about the data. And then kind of jump down to the third bullet because it says competencies involved in working with the data. So in order to become data literate we need to have competencies involved in how people are going to be able to work with the data. And then the last definition came from a data diversity article from two people, one of whom went to the college that my daughter went to. So I found that to be quite interesting but data literacy is the ability to read, work with, analyze, and argue with data. I love that term of arguing with data, but it really comes down to understanding of the data. So really the focus of literary literacy is not only, it's not only on the understanding but it's getting people to be able to work with that understanding and work with the data. So, it's interesting when somebody is said to be literate. Here are some of the words that are used to describe those people they're well read, they're knowledgeable, they're culture that's kind of an interesting one but their culture this is a synonym for the word literate. Some of the words I don't use often. Literary, informed, scholarly, all of those things are how you would describe somebody to be data literate. The one thing that I saw missing in all of the synonyms for the word literate was the ability to be understood. And I actually reached out to several friends and I talked to them about, do you need to be understandable in order to be data literate. And I would say that that is a valid addition to the list of words that could be used to describe literate. We need to be understandable. So not only do we need to understand the data but we need to be able to present that data in such a way that it's understandable to other people in the organization. So we're not just pointing at one person being data literate, we're talking about the entire organization being data literate. So let's start out by defining the relationship between data governance and data literacy. And, you know, the first thing I need to say is that the people that are putting data governance programs together they don't have the time for data literacy. I don't mean that in a rude way I mean it's just that they don't have the resources they don't have the time to handle data, data literacy as well as getting a data governance program in place I'll talk a little bit more about that in a minute. So part of the relationship between governance and literacy is formalizing behavior and accountability for the data so that we can improve the understanding of the data, and that is basically the definition of data governance and data stewardship. And we'll talk about the most important question of most important word in the English language being the word why we'll talk about how data literacy requires a focused effort just like data governance and how just like data governance. So data literacy needs to partner with corporate communications, if that's not where it resides in the organization in the first place. So when I say, and it's interesting so here you'll see a music reference on this slide. And I've done this in the last several webinars, but you know there's a great song called no time and I'm saying data governance has no time for data literacy and the reason why I say that is that it requires resources to address data literacy from an organizational perspective, somebody's going to have to have the responsibility for it. And most often, at least from what I've seen in the organizations that I've worked with they don't have a ton of resources to sitting around waiting to be their data governance team their data governance office. They're oftentimes understaffed in fact many organizations talk about building out their capacity to govern data better as they add people and they add the capabilities to what they're doing so that that's really important data governance has other things to focus on. They can certainly partner with data literacy or those people that are putting the data literacy program together. So they certainly should consider partnering with skilled communicators. And what would be really helpful is if we could seek some common and effective messaging and instruments to to help or the organization to become more data literate and look for what those commonalities are and present data governance and data literacy as different but how they work together and how they are important to each other. So the as we're defining the relationship between data governance and data literacy, I said data governance is the formalization of behavior. Data stewardship is the formalization of accountability, well maybe data literacy is formalizing the understanding to assure that we have the appropriate behavior and the appropriate accountability. Data literacy in itself can can oftentimes be dependent on how well data is being governed within the organization. So we're again we're talking about the relationship between data governance and data literacy. Is it possible to become a very data literate organization if you are working with ungoverned data. I'm going to ask you to ask yourselves and I'd be curious as to what you have to say, because, you know, govern data is well defined it's well understood. How do you become data literate if you don't have definition, and you don't have understanding of the data. So that there is a very tight relationship obviously between governance and literacy, but they're not exactly the same thing. I started looking see I kept seeing over and over again as I was looking for more information about data literacy that they kept talking about the word why, and I guess you know the way industry in general looks about looks at literacy is people want to be able to answer the question why. That's going to be through their understanding and their understanding of the availability of the data, the understanding of the data itself, and a good friend of mine, a fellow person who also speaks at a lot of diversity events events to net McGilvery has her five wide technique. So once you ask the question why to really understand the essence of what is needed in somebody's knowledge when it comes to something. They'll answer the question but then ask the question why again in a why again and again and then you're eventually they may get tired of you asking why that you're going to get to the true essence of what needs to be analyzed and what we need to govern data around and where we need to be data literate. So, there are certain questions, besides for just the question why that are going to lead to literacy and actually, as I was looking back at these slides it's not even just the questions leading to literacy. It's the answers to these questions that will really lead to literacy in the organization, people want to know what data exists. They want to know how the data is defined how the data is produced, how the data get it to be the way that it is in the resource that I'm looking at, especially if I want to have confidence in that data I need to know the needs. I need to know the needs I need to know where that data came from. Where is the data where's the metadata I know that Sue talked about that before. Who's responsible for the data what are the rules for the data, how can I use the data, you know, what do I need in order to fully trust the data in order to make decisions from the data that I trust. So, that that's really important again while defining the relationship between governance and literacy. There are certain things that we as data governance practitioners can focus on doing, providing the answers to those questions that are on the screen right now, in order to help make people in the organization data literate. I hope that makes sense to you. If you have other questions. Throw them in the chat or throw them somewhere to send them to me or something because I'd love to kind of build up a list of what are the questions that really need to be answered in order to lead to a data literate organization. So again when we talk about the relationship between data governance and data literacy, I always taught I use the word resolute a lot there needs to be a resolute effort to have a data governance program because a data governance program is not going to execute and enforce itself. So just like that with data literacy you know looking at this picture on the right. You know that fish had to have a plan as to how it was going to get from the small bowl into the bigger bowl. So just like data governance, we need to have a very intentional effort within the organization in order for us to become data literate. It's not going to have it happen magically. I wish I had magic pixie dust for that or we could send it just sprinkle it over everybody and all of a sudden everybody becomes literate, but to be honest with you, in order to have a data literacy program. And I've kind of already made the case that it's not going to necessarily reside in data governance although data governance would be a partner to it. There has to be somebody who's guiding the data literacy effort. There needs to be somebody who put together a plan and then somebody to actually execute on that plan. So again data literacy we're not going going to go from being data illiterate, whatever that means to becoming data literate without having a plan and without executing on that plan, somebody has to gauge the effectiveness of the program. They have to include stewardship when it comes to becoming literacy literate people in the organization need to understand that their relationship to the data whether it's as a definer producer or user. It needs to be helpfully accountable for that relationship so stewardship is certainly a part of literacy stewardship also needs to be a big part of data governance obviously. And there's somebody, there has to be somebody in the organization that's going to govern the literacy, the literacy effort in the organization, and I put on here as a joke because you're seeing everything as a service. It's going to be within organizations and I don't know if anybody's talking about this yet. They could talk about data literacy as a service as well. And another relationship between these two is that, and I always talk about how data governance should really partner with the, if your organization has corporate communications people, people that are truly skilled at getting the message out and getting to the appropriate audiences. They should become a partner of the data governance program. They should also again if it's if your data literacy program doesn't reside in your corporate communications group. It's important to partner with that corporate communication. It is that important that we need to get corporate communications involved. Because what are they good at their good at the messaging they're good at telling the stories. I had a client recently, who said that they wanted to innovate to literate to literate really within their organization. They talked about using something that there was the hub of knowledge in the organization, it could be the data catalog, it could be the place where people go to get their understanding of the data that they need to use to do their daily job. They wanted to know how about place a catalog for places for people to go. They decided they also wanted to not be very invasive in getting people to be literate they did not use the term non invasive data literacy but they couldn't.  So now let's talk about ways that data governance can enable data literacy. And one way is through formalization of accountability. Another way is I'm going to go back to the definitions that I shared earlier, but I also talked about having a commonality of goals between data governance and data literacy, and then take a quick look at the commonality of the journey between the two of these looking at them as partners as kind of working together. So, when it comes to formalizing accountability and I know that I had the definitions before and I'm going to have them on the next slide as well. But we need to consider. First of all, what does it mean to hold people formally accountable for what they're doing with the data for how they're defining the data, providing good business definition, how they're producing the data, you know, paying attention to how they're producing the data, how they're producing the quality of the production, and then certainly the use of the data. So if we go back and we look at the definitions that I provided from Gardner to the literacy project, you know, all of these different resources, things that we should be asking ourselves about our organization is think about how getting people in the organization to be formally accountable for the definitions that they're providing of the data. How is that going to enable data literacy within our organization. Again, if we're going to go back to understanding of the data, the understanding is going to come from the metadata, the definition, the stuff in the data catalog, the stuff that's going to be used to drive your data intelligence. So just think about within your organization, you know, how, if we can formalize and we can improve the way that the data is defined, what impact is that going to have on our data literacy. What are you trying to consider again within your organization. How is holding people formally accountable for how they're producing the data, going to enable literacy. So those people who are on the front lines those people who are entering in data those people who are changing data, you know, everybody needs to know that any impact that they have on the production of the data is going to, you know, hopefully have a positive impact on business outcomes that come from that data. But we need to consider how holding people formally accountable for data production is going to improve the quality of the data, which is going to improve the confidence in the data, which is going to improve the literacy of the organization around that data. And then the same thing holds true for data usage. I mean, if we can get people to use the data understand how to use the data, and we can hold them formally accountable for that think about how that's going to enable literacy within your organization. You know, but there's one thing also to consider when you think about these things within your organization that having formalized accountability for the definition for the production for the usage of data does not necessarily assure you that you're going to have a data in your organization. There's other things that need to be done besides having good definition, we need to make certain we can get that definition into the hands of people that need it so that they can truly use the data, the way they want to use the data. And again, just in a ways to predict a governance to enable data literacy, go back to those underlying words in the definitions of data literacy in these different places that I pulled the definitions from and say, you know, what can we do what can our data governance can do that will improve the understanding, you know, improve our ability to have a common language and to have meaningful conversations around the data. You know, any of these definitions you could pick them apart and put just, I suggest if you're moving forward with the data literacy program in your organization that you want to make certain that it aligns with however you're defining data literacy in your organization. So, some additional ways that data governance can enable data literacy is they can assure that the understanding of the data the context the metadata that it's high quality that it's governed I know you've heard me say if you've attended these webinars before the metadata is not going to govern itself. Somebody has to govern the metadata just like the data, but to data governance can help to make sure that the data and the context and the metadata is of high quality it is being governed, and it is being made available, assure that the common knowledge of the data is being captured and shared. Again, don't want to read through all of these. But again, I think it's a good list of if you're looking for ways that we can connect data governance to data literacy. Assuring the commonality of business language one of those definitions talked about it as the being data literacy is the second language of the organization. Assure competency and definition production usage, assure capabilities to do all those things that the definition spelled out, which is people need to find the data. They need to be able to read it work with it analyze argue with it all those types of things. Let's see so let's talk a little bit about the commonality of goals well I think in order to her organization to become data literate. They're going to need to execute and enforce authority because again if everybody is left to do their own thing. You know again you may follow a federated model versus a centralized or a more authoritative model. But at the end of the day however you're doing it at least some level of authority needs to be executed and needs to be enforced in order to become data literate. So that is my definition of data governance so in order to get to the point where we have truly governed data, we need to execute and enforce authority, we need to formalize accountability again my definition of stewardship. So, there's a commonality in the goals of literacy is to get everybody in the organization potentially to understand their impact that they have on the data the impact that they have that their data impact has on the organization so formalizing accountability is a common goal protecting sensitive information. That seems to be at the top of the list for many organizations, improving quality understanding discover ability access to the data. These are all the goals of data governance and you know what if we're going to become more literate as an organization. We need to improve the quality we need to improve the understanding, we need to improve how easy it is for people to find the data that they need, or even to learn about data that they didn't know about that exists within the organization, improving access. The goals of data governance and the goals of data literacy are similar I won't say they're exactly the same. But they, they are side by side they are hip to hip basically. That's the commonality of the journey. Yeah, the common other journeys literacy really benefits from having governed data to help to improve people's confidence and understanding in the data governance benefits from literacy if people understand the data, and they understand that they play a role in the improvement of how we're executing and enforcing authority over that data governance can benefit from literacy as well. They both benefit from stewardship. They both benefit from metadata. These are our core pieces of our journey, when it comes to implementing a good data environment, a good data landscape within our organization. So they've got commonality of journey, they've got commonality of goals. Let's find ways to connect these things together. And so when I say that they both benefit from the metadata. And if you've been attending these webinars in the past, you know that I say it a lot is that the metadata is not going to govern itself so you have to do the same things with the metadata, as you do with the, with the data of the organization, people need to know what metadata exists and how to use that metadata in order to become more data literate. Okay, so let's talk about where data governance, or where data literacy, I'm sorry, should reside in the organization, or the appropriate ownership and I don't have a single answer for you. So just bear with me for a second. We'll talk about a best practice approach. We'll talk about where potentially your data literacy program should live corporate communications data storytelling. Let's jump through these. So the best practices when it comes to data governance, the very first best practice used by 100% of my clients is that senior leadership support sponsor and understand the activities of data governance. Because without that you're going to be at risk at some point. Well the same thing holds true for senior leadership need to support sponsor and understand why you need to become literate. So the value is that's going to come from becoming data literate. And then, you know, a second best practice or another best practice organizations use a lot is, we need to place this in the appropriate place within the organization, there is not a single place. It could be in it, it could be in communications, it could be under the chief data officer, it could be a lot of different places in your organization. So the criteria I use for the best practices that they have to be practical and doable, and that will be at risk if we don't achieve these things. If senior leadership at some point doesn't understand or support or sponsor data literacy, your data literacy program is going to become at risk data literacy will not improve on its own. It's been just a minute here I know we've got about 15 minutes left before I turn it back to Shannon for some Q&A, but I'm having a problem with my monitors where I can't see the Q&A but I just be curious I will go back and look at the chat but answer a question for me just real quickly if you would. Do you have a formal effort within your organization to improve data literacy. If you do have a have an effort underway to improve data literacy where in the organization or who in the organization is responsible for your data literacy program, and I really wish I could see the results right now. And here's some examples of what I'm looking for as results. And again I'm going to alert use this information, not publicly, it's just I'm very curious as to how many organizations have data literacy programs in place. So you can say yes, it's owned by change management. No, I wish we had one, or yes it's corporate communications. You know I'd be very curious when I go back to see where does your data data literacy program exist if you have one. Because I think what you're probably going to see is that it's not the same place in every organization. I'm not necessarily saying that that is the place for it but I say that for it to reside in the organization, but I say we need to partner with corporate communications for reasons that I shared before. These are people that are skilled with messaging. They can do enterprise campaigns to help to improve the literacy of the organization, the data literacy of the organization. So I often talk about putting together a communication plan that includes orientation communications onboarding communications when you ask somebody to do something, and then ongoing communications. I think you're going to need the same things for data literacy, as you would have in place for data governance you need to orient people to the concept, because they're not familiar with what data literacy means. You're going to onboard them when you're asking them to do something, and then you need to have ongoing communication to keep them data literate.  You need to continue to provide the understanding and the knowledge to them. So the, another thing to think about is, you might want to consider mapping your data literacy program to your enterprise strategy, or if you have an enterprise data strategy, mapping it to the enterprise data strategy. And keep in mind that, you know, when organizations these days are creating data strategies, they're including data governance as a piece of that overall roadmap as part of that strategy. I think we need to start including data literacy as part of the roadmap. Some organizations maybe ahead of the game and be doing that but more organizations that are putting together enterprise data strategies are starting to include literacy as part of that. So you might see in the language that's being provided in your organization that you want to become data first you want to become data literate, you want to become data centric or digitally transformed, all of these things. If your goal is to become a data first organization, you're going to need to have people become more literate than they are now about the data that you have in your organization. When it comes to data storytelling, I can't remember who said this first and where I got it from. So, if somebody knows, please let me know. There's really two types of storytelling when it comes to the data, and they're very different from each other. There's stories about the data. There's stories about what we can do with the data what happened because of the data, or I'm sorry those are stories about the data about the quality of the data of the ownership of the data, and then there's stories with data, and that would be what type of business impact and we have. We make have some added value business outcomes within our organizations, so you can have stories about the data, and that's what a lot of a lot of us data practitioners are really good at is telling stories about why data is important and why it is an asset and why it needs to be important and why we need to have metadata, but then we're not as skilled at in order for us to help the organization to become more data literate, we need to be able to tell stories with the data as well. And that is, and that really requires having some business knowledge, working with business people who can articulate where the quality of the data matters where the time to access of the data really matters within the organization. So, you know, ask yourself the question what does data story telling have to do with data literacy, all the more that we can tell stories about the data in such a way that's understandable to the organization, the better the more literally will become. You know, the better we can tell stories with the data, I think that's even going to be more important. It's going to tell stories with the data, people can understand why we need to improve quality, why we need to improve understanding why we need to improve accessibility to data. So, another topic, let's talk about including governance as a topic that's discussed when we start to talk about data literacy. So we'll talk about governance as an enabler of data literacy we talked about that a little bit already traditional orientation communications, the fact again that Stuart will look at some other data, data literacy topics as well. But when it comes to data governance as an enabler of data literacy, governance is in place to execute and enforce authority over the management of the data. Governance can also be in place to execute and enforce authority over the management of the metadata, so that the metadata improves the understanding, and thus the literacy of the organization governance is the formalizer of accountability. I mean, because otherwise, and I've heard this from so many organizations that we don't hold people accountable or that there is no accountability for data. Not your head up and down if that's one of your organizations because it might be might be your organization, but somebody needs to take on that responsibility of formalizing accountability for the management of data. And so how are we going to improve the understanding the definition production and use if we can't hold people accountable. And how are we going to become data literate if we don't have good definition production and usage of the data. So data governance is truly an enabler of data literacy is a provider of the understanding is all things basically to enable data literacy within your organization so remember these synonyms that I used earlier, including the one understandable that wasn't there, but that I added. We weren't are trying to enable people in the organization to become well read knowledgeable culture, literary, all of these types of things. When it comes to the data, the, the lifeblood the, the, the oil that basically runs through the pipelines in the organization. We need governance as an enabler of data literacy. When I talk about communications. Excuse me. The three O's of data communication of data governance communication can tie in as I said before, to data literacy, there's orientation onboarding and on billing communication. It needs to include data governance communications and it needs to be very intentional, and it needs to be repeated. I can't tell you how many organizations have told me that it takes, and I've noticed this is it takes being told something a few times, in order to really understand it.  Now it's going to be an ongoing adventure and ongoing journey to get people in the organization to be data literate and it has to be intentional. Somebody is needs to be focusing on it, because just like data governance if you take all the resources away. It's going to be very difficult to be successful with the data governance program. It needs to be intentional has to be repeatable. There need to be resources that are applied to it like we talked about before. So what I suggest as a way to include governance as a literary topic maybe you want to consider bundling data governance data stewardship data literacy and data leadership together. Because I think those things all together are going to really help the organization to as an outcome become more data literate. So let's talk about how everybody is a data steward a data steward is somebody that's how formally accountable for what they do with data. And what they do with data is they define it they produce it and they use it they may do some other things but typically those other activities fall under definition production and use. And we need to understand that potentially everybody in the organization either defines and or produces and or uses data. So potentially, if we want to cover the entire organization. Everybody's a data steward. So we're going to need to communicate with everybody about data governance, we're going to have to make everybody data literate. We've got a lot of work to do. This is not going to be solved in a day. The Roman Empire wasn't built in a day or a week or a month this is going to take time to do it but the only way to do it is to start now. So the data stewards based on their relationship to the data, take a stewardship approach or stewardship method to really implement the governance program. And what are some of the other data literacy topics. Well, you can work in the people aspect of governance the people aspect of literacy, the process aspect of governance and of literacy technology of both. But accountability is going to be one of those key, key topics that need to be shared as part of this communication about data literacy. So the last topic that I want to share with you and then we'll turn it over to Shannon for some questions. Is we want to talk about the direct impact of data governance on data literacy look at that poor dinosaur in that picture with the, the media are coming at him that's good. He's waiting for a direct impact. So we'll talk about the formal governance of data results in and I'll outline a list of things that formal governance will result in business outcomes. Through partnership we'll talk about that I'm not going to really focus on the lack there of just think about the opposite of everything I'm going to say here in a minute. And then everything goes with data governance so let's go go through these and turn it back to Shannon formal governance of data results in these things. Accountability, it results in confidence documentation because if you know I don't know how well your data environment is documented today, but formal governance can assure that documentation is being addressed. Literacy of the data quality of the data so formal governance can result in a lot of things in your organization. So if we think about like if we had a if this was an equation and instead of having those look like minus signs, there were plus signs down the left hand side, we all added it up and put a line under that. I think the word would be understanding. I mean we're going to build understanding of data within our organization we're going to build literacy for data within the organization by holding people accountable by giving them confidence in the data by documenting the heck out of the data by giving them literacy and providing quality, giving them confidence that the data is of high quality. Look for positive outcomes through partnerships across your organization, look for outcomes that are related to the confidence. The confidence that people have from data definition, the confidence that people have from data production and confidence that people have in knowing how to use the data and what the rules are associated with the data and how data needs to be handled. So look for one of the direct impacts of governance on literacy is positive business outcomes that come through confidence from definition production and usage of the data. And the almost the last slide here. I can honestly remember in the last six months that I have said that so many different things can only be successful with data governance. And the fact is that it's true. So data literacy I've said is it can only really be successful if you have governed data. But I've said that about quality and metadata and mesh and fabric and I'm sure I'm missing others. But, you know, I guess one of the points being made by our real world data governance webinar series is that data governance. It's going to go into all of these things, and it's going to be very, it's going to be very difficult to become a data literate organization, unless you have some well governed data within the organization or to become literate for the data that is governed within the organization, I think those two go hand in hand. So just think about that as you move your programs forward. So today wouldn't I talk about I talked about the relationship between governance and literacy. The ways that governance can enable literacy where where it should could exist within an organization. There's no single answer for that. We talked about including governance as part of our data literacy message to the organization, and then the impact that data governance has on data literacy. So thank you very much for sitting through the webinar today and with that, Shannon, I'm going to turn it back over to you. Thank you so much. As always, if you have questions for Bob or Sue, feel free to submit them in the Q&A section. There's a lot of great questions coming in. But just to know if we don't get a chance to get to your question today, do feel free to keep submitting them in as we will get the answers to you in the follow up email. And to answer those commonly asked questions, just a reminder, I will send a follow up email by end of day Monday with a link to the slides and links to the recording as well as anything else requested from this webinar to all registrants. So diving in here. I work in higher ed where literacy has a very specific meaning also where the higher up the food chain you are the less data literate you are likely to be generally generalization but roll with me here. How do you address the sensitivity around the concept of being data illiterate particularly with the executive suite. Wow, I was going to let Sue answer that question first but I guess I'll take it if that's a tough question. So obviously in higher ed where where I would assume that be being literate has a specific meaning, you may not want to, you might not want to confuse things you might want to call it something else, but the idea is again to get people understanding when you say that literacy decreases as you move up the food chain in the organization. That may be because people who are down in the dirt, know the data better or they know where to go to get information about the data, as you get up the food chain to higher levels they may not have access to these tools. One thing that we can do to improve that is to provide some of these access, some of the access to some of the the information about the data to improve people's understanding to improve their literacy at all levels, not just the operational and tactical levels. That's my answer. So you have anything to say about that. I think that's really good and that's where my train of thought was going to was towards. I've seen a lot of clever ways of doing surveys and questionnaires and things like that, that kind of drive folks to, to go to their knowledge base for the answers and and socialize what some of those answers to the data questions are. Perfect. So, how do we measure data literacy. Measure data literacy. Yeah, because we're going to be asked to measure data literacy. Well, you know, the way I'd address that first is once you figured out how you're going to measure it, take a measurement right away so you have a place to a benchmark, you have a starting point so you can actually measure. So, but the question is, how do we actually measure it themselves. You know what, you may just want to pull people within the organization and ask them. When it comes to certain things I wouldn't ask them just point blank how data literate are you, they won't understand, you know, break it down into bite sized chunks, and ask them or how confident are you in the data how, you know, do you know where do you get the data, you know, you can also use those questionnaires to gather other information but specifically for data literacy, you know, ask people how date how literate they feel, and then apply some some technology some people some process some technology, and then measure it again. That's what my thought is I mean I, I haven't been asked to measure literacy yet. So you have any thoughts on that as well. That's the same thing that's what I was thinking as well but and I really like what you said about having a baseline of where you're at today, how knowledgeable are your end users when they're looking at reports and trying to siphon through data and understanding the data that they're seeing. If you do it now versus after you've started an initial data intelligence program that's where I can start to see folks really become much more literate, much more mature, and then to also have some sort of maturity process that you're seeing would also help, help in that arena would I guess to. Yeah, I would think you could evaluate yourself on a, on a, like a CMMI capability maturity management on a level one to five. And you can say here and this is where we want to go. And there's the steps that we're going to take to get from being a, you know, 1.2 to being a 2.8 in the next year. You know, here's the steps that we're going to take to do that. So, why would you define as the top five key competencies for data literacy to be data literate.  No, I don't have. Well, I would say, I would say it's great to have, it's great to have you here on the call because I can, I can divert to you. The, you know, metadata. So that's the question again Sharon because I want to make sure that we're honing in on those five. What would you define as a top five key competencies for a data literacy program to be considered data literate is metadata competency. I think I guess I'm going to make it a competency because it's not defined for us, you know, having metadata, having ownership or accountability as you know I may know I don't like the word ownership because it implies it's mine.  You know, executive competency would be that your executives have an understanding as to why all this stuff that we're doing why are we so nervous about the data that we have. What's the outcomes that the organization can expect from paying attention to solving these things. I don't know there's three. So you have any to add to that. I mean, I'm a big proponent of data lineage and being able to understand data across business lines. So to be able to validate where the information is coming from whether it's in your department or outside of your department and knowing exactly where it's leaving the organization. And then I would say, critical data elements and PI information being able to understand where that critical data is and what the impact of it is if I change it. And if I if I create more or if I delete it. I would say understanding your critical data. Okay, they're sharing there's five. Out the top of my head. That wasn't fair because it came from Valerie Logan and I bet Valerie already has five core competencies. Well, it's interesting though that would either of you back it up to all the way to just adding getting people on the same page about what the definition of data governance is. I don't think that that I don't know if that's a competency but it's, it certainly needs to be communicated. And it's the, you don't want to throw a definition that's going to raise more questions than are answered. Can you talk about the connection between data literacy and data democratization. Oh so data democratization if I understand it correctly is putting really the power of using the data in the hands of you I can't really speak to data democratization too well. But I think that so it was a relationship between literacy or between governance and data democratization. So I think that's the relationship between literacy, literacy, literacy. Well, I mean, you've got a relationship between governance and literacy so that's there so and so I would say the governance of the the relationship between governance and literacy together. You know, I think you should look at it from that side and see how do they support you becoming more data democratized. I think that Valerie just posted posted data literacy enables responsible data democratization so okay that's that says it in a nutshell right there. Yep, transparency. Right. And I always trip over that word it's. democratize something or right yeah yeah. At least one more question here and we aren't governing just to govern so our definition of data governance is people process and tech to ensure data is an asset. You also can't expect people to be in compliance with data governance programs if they don't understand data so literacy is part of the data governance program thoughts on that. Yeah, well I mean when you say that data governance is people process and technology you're right there's process and there's technology, any process itself is a form of governance because it's setting the state the steps of the things that you're supposed to do. I would say that data governance focuses mostly on the people on the behavioral side on getting them to become more understanding of what's available to them. I don't know if I, I mean I guess if you can explain why you're including process and technology in your definition and I think that you can do that. That's a fine definition, but it's really focused on people it's really when data put data governance right in the middle of the demo wheel. Because the accountability and people aspect of all of those different disciplines in the demo wheel are going to relate are going to relate to data governance. So, I think it's more focused on the day on the people than the process and the technology. I don't assume any different thoughts. I mean, I look at data literacy as connecting the dots and connecting. Yeah, connecting those people that understand it from one point of view with the people that maybe understand it way down the chain, the people who are working on those legacy environments and the people that are producing the reports, connecting their worlds together from the, the written explanation of that data. I wonder who the glory was that you are the Valerie was that you were talking about and then I just opened the chat and I see who you're talking about. Well that is perfect timing is that brings us right to the top of the hour thank you both so much for your great presentations today and thank you so much sir when my quest for sponsoring today's webinar to help making these webinars happen. And of course special thanks to all of our attendees who are so engaged I love always love the chat and the questions again we'll get the rest of the questions answered for you in the follow up email, which will go out by end of day Monday, also including links to the slides and the recording. Thank you all. Hope you all have a great day Bob Sue Thank you so much. Thank you. Thank you.