Analytics Transition for your Company

Transcript: Fundamentals of Analytics Transition for your Company 

Sarah:     05:10

Hello, everyone and welcome. Thank you for joining us today for the Zencos webinar, the Fundamentals of Analytics Transition for your Company. My name is Sarah Septoff, and I will be your host for today’s presentation. Our presenters today are Tricia Aanderud and Chris St Jeor. Tricia is the director of the data visualization practice. She works closely with customers to help them transform their data into meaningful reports and dashboards. Tricia is the author of several SAS books.

Chris St Jeor has over five years building supervised and unsupervised machine learning models to help businesses become data-driven and to further enhance their decision making processes as part of the data science team at Zencos. Chris helps build state of the art analytics solutions.

Just a reminder, this is not intended to be a sales presentation. We are simply sharing what we have learned from working with our customers over the years.

During the presentation, we will be asking you to participate with poll questions. The polls will be put on your screen and you’ll have approximately 30 seconds to provide an answer. Once the poll is launched, it will appear on your screen and after 30 seconds is up, I will be sharing your results. Once the results are shared, remember to close out of the poll or it will stay on your screen. Let’s try a sample poll question now.

Where would you rate your organization’s data and analytics maturity?

<Runs Poll>

If you look at the results, it looks like most of you feel like you are at level two right now. There is some, some variation.

We are about to get started, and I’m sure everyone here has noticed the anchors interest in analytics. Specifically, how can I move my company up the analytics ladder in order to succeed? It’s important to evaluate where you are today, the challenges that lie ahead and what exactly I head means for you and your team. Now, Tricia, I know that you and Chris had done extensive work researching success strategies and helped several companies move up the analytics maturity model. Why don’t you tell us more about the work you guys have done?

Tricia:     08:20

I think Sarah, you first need to talk about what the analytic maturity model is and it’s very simply just a way to measure the stages that organizations go through to use data and analytics effectively. So basically it goes from the very technical term of Zilcho to being completely data-driven. Now, as you can imagine, the better business outcomes live in level four and level five areas. But how do we know that? So there’s been various studies done and one study that looked at different companies said that when they were at the fourth level of maturity, they were often on the top company lists. So when they were ranking higher among their peers as being better. Also, when you look at analytics, maturity and financial performance, there is a positive correlation and us as Data professionals love to see positive correlations. But another part of this is how many people are in the lower levels?

Tricia:     09:15

How many companies would you estimate in the lower levels? Well, I’m going to tell you that it is in the disappointing 87%. This was on a recent survey that Gartner did in December. They asked the companies to rate themselves. So I’m asking you that if the better results are in the higher levels, then why are so many companies dragging behind? We actually have some thoughts about what’s causing it and some ideas on how you can move forward. And that’s what we’re going to talk about today.

Tricia:     09:44

First of all, we’ll review the analytics maturity model and we’ll talk about the common blockers, what happens, and then we’ll talk about some successful strategies. So the analytics maturity model, if you’re not familiar, there’s an SEI organization and they have the capability maturity model. And what happened was in the 1970s the US government was having issues with the software.

Tricia:     10:08

The projects were not being delivered on time and they didn’t have high quality. And that’s a unique thing where I know that doesn’t happen to any other companies or anything. That’s just something very odd, right? But what they found out was they were trying to figure out how can we solve this? So they asked Carnegie Mellon University to study the issue. They spent several years studying it. And what they found was that successful software projects were produced by companies with mature processes.

There’s a level one where they’re just in the initial stages. There’s level two where they’re kind of got some management going on. There’s level three where there are some definitions, but then when they get to level four they started managing it and optimizing it and they also noted this is where the better outcomes were, but they said that a lot of times what happened is organizations would get stuck in level two because management was just giving lip service and they weren’t serious about moving forward because this is an involved process.

Tricia:     11:08

Now we looked at this and what we were trying to do is figure out, well, what is the basic thing that’s going on here? And if I were to say, let’s talk about a maturity model applied to the software learning process. If I was going to learn to code, how does it work? Well, I start off with a tactical stage and in the tactical stage I’m trying to understand what words do I use? What is the syntax if I simply just want to produce a data set, how do I do that? And it’s all very tactical and it’s the same when I got on to talk a little bit more about this with analytics maturity, you’ll recognize the tactical stage. The next stage is strategic. So if I am learning how to code, I’m going to be looking at, okay, there are different ways that I can go about getting a result.

Tricia:     11:51

What’s the best way to do it and what are know when, what situation does it work the best in? And then there’s this optimized stage and this is after I have a really good understanding of the language, then I can go down and I can say, okay, what is the very best way to do a data step? What is the very best way to do PROC SQL? And I think about how do I optimize these steps? And that’s the same thing that happens with the analytics maturity model.

One Saturday morning, Chris and read who also helped with this presentation, we got together and we look at the eight different models that are available out there now. I think a lot of people are more familiar with the Gartner model, but there are several other ones. And we were talking about what have we seen our customers struggle with and what is most likely to happen because we wanted to have our own.

Tricia:     12:42

So let’s talk about what that looks like now. We do have some questionnaires later on available to you. So don’t have to worry about taking big, heavy notes.

Tricia:     12:53

The chaos stage first annual recognize the chaos stage is because it is completely riddled with the spreadsheets and hoc processes. You can’t get the same information twice. So if I wanted to produce a report, can Joe and Sally both produced the same report and they can’t. And there’s a lot of, incorrect information, it’s misaligned. It’s, what do we say? He’s chaos. But as the organization grows and they get out of this stage, what will happen is they’ll go into a development stage and the line managers typically realize they need tighter controls over their processes because they’re trying to meet customer demands. So they want to manage customer interactions, like order management, trouble tickets, how many salespeople do we have, what, whom are they talking to?

Tricia:     13:42

And they’re probably not given a lot of consideration to how the data is stored or accessed, but they’re getting tactical reports and these are reports that help them understand like, oh, this ticket is overdue or we haven’t sent this order out. It’s all around operations management. This doesn’t mean there is no analytics going on in the organization, but there’s very little. So they’re just still trying to figure out how to put everything together. And typically what will happen is its organization, because they typically are the ones handling the databases. We’ll decide we’ve got to do this in a different way. And the company as they grow will move into an alignment stage. They’ll want standard reporting. They will want one way to go get every bit of the information and they’ll say, well, okay, we are workers are spending too much time moving data between databases.

Tricia:     14:31

We’re not producing any information that’s good. So they want to resolve that. The group that handles it the best is typically they’re going to be chosen as the one who sets the standard, the one that’s the most mature. Now there’s a couple of other little things that happen at this time. Business intelligence competency centers we’ll get formed and this is where they want to have the standard approach and they pick a few people out of the organization to work into this work in this organization and their ideas to link business to strategy. There could be an analytics advocate that appears at this time and that could be from it or the business, but the ideas that we’ve all to get an alignment, we’ve all got a more walk the same way integration, this is where the enterprise starts taking notice and alignment before had been about just getting departments in order, but the entire enterprise starts looking at things.

Tricia:     15:28

Senior management is going to start thinking about how do we use data to the competitive. They definitely will have a data governance policy in place here. And this is also where predictive analytics or machine learning techniques start entering into decision support. And the workers are going to be trained to make a decision with Data, with data’s, what’s going to happen. And they’re trying to predict, improve and improve. They’re trying to understand not only what happened or why it happened, but how do we control it? How do we make it better? The next step after that is going to be the optimized state. Now at this point, the organization definitely has a C-Suite or chief analytics officer, or a chief data officer. They’re going to seek out opportunities to incorporate analytics. There’s going to be a mindset in each department that they need.

Tricia:     16:17

Analytics workers are going to ask for data to make decisions. Also, the company, any vendors that work with them, they’re going to say, we have to have data from you so that we understand if you’re actually good or not. So that’s how you climb the ladder to analytics maturity. And you don’t have to be afraid of it. But now let’s do a quick evaluation. I’m going to ask you again where you think you are and let’s see if it’s changed any at all. So now I’ve launched the polling. Now where would you rate your organization’s maturity?

Tricia:     16:56

[You have about] 15 seconds. Has it changed? Do you think maybe you are, not as high as you thought you were, or maybe you’re better than you thought you were? Not everyone’s voted so I’m waiting for some other boats to come in. I’m going to give you two more seconds. All right, let’s see what we got. Okay. I think before there was more of you that thought you were in the chaos stage or the alignment. And now we’re clearly all in level two where we think we’re developing our organization. And that sounds about right to me.

Tricia:     17:35

Let’s talk about the blockers, just access because that’s really what we’re interested in. And when we started naming off the blockers, we said, well, they really kind of come in three areas, leadership, data and people. And I’m going to talk about leadership. And Chris is gonna talk about data and people. So leadership, you got to have a strategy. And I am often amazed at how many companies actually purchase very fancy software like SAS Viya or other software and they have no idea what they want to do with it. We’ve had customers call us and say, all right, we got the software, come tell us what to do. And it’s kind of strange if you think about it because data can answer so many questions and it can help in so many ways, but you’ve got to know what question you’re trying to ask.

Tricia:     18:23

And if you think about it, that’s probably why a Gartner saw so many companies who are on these lower levels because they just don’t understand what to do or they don’t have buy-in. Now, one of the things that you have to think about is like sometimes it’s like walking around in the dark, right? When you don’t know what you’re doing. And I think that’s level two and three. That’s where I think a lot of things are happening where, yeah, it is walking around in the dark. You don’t know what you’re doing and it tries to take the lead on this. But even in a 2018 survey of top organizations, 55% of them rated. They’re a, it is not being in alignment with the business or just not even understanding what they’re doing you, the business has to know what’s causing the pain and they have to know what they don’t understand.

Tricia:     19:08

Otherwise you’re just a wandering around hoping something good will happen from no effort on your part. But my grandma always told me nothing good happens after midnight. And this is true. So how do you overcome it? Well, successful companies have a strategy and here’s what’s important about a strategy. It forces decision making and priority setting. Otherwise you’re in that situation where it is trying to make decisions when they don’t understand what’s going on. So the strategy, the most important thing is going to do is just force this decision making and what you’ve got to get out of his management too often wants to do that to just stamp of approval instead of actually being involved. And that’s what hurts companies when that review and to approve, instead of a priority setting, our analysts talk about the elements of a data and analytics strategy, first of all.

Tricia:     19:57

And this is going to sound like a stupid thing for me to say. It’s gotta be tied to a business outcome. And that’s because the organization has to understand what they intended, she achieve, how they want to complete it, and what the target date is for completing that. And it’s gotta be relevant. Typically it’s better if it’s a customer, a focused goal.

So what is important to us? What are we trying to achieve? And one other thing I’ll say about the data strategies, this should not be a 30-page document. It’s fine if it’s a one-page document, but everyone agrees that this is what we’re trying to accomplish. The next thing you need is a, a modern toolset, you know, how will we manage the data? And this is something where you have to think about three years out how you’re gonna manage it.

Tricia:     20:40

Because as companies start this process, the data starts growing. And companies have a lot of databases. They may have Salesforce, they may have Oracle, Hadoop, even Microsoft Access. You need a tool that can bring all of those data sets together. And I’m a SAS proponent, so I’m going to say a tool like SAS Viya is excellent for that, but there are other tools that work as well. You want to have one central location where the data is available from so everyone knows how to get that. And you also want to have one that allows for future enhancements. So if you want to later add machine learning or AI or IoT, you’ve got a tool that supports that and technology takes time. And if people got to be aware that you know, you’re not just going to pick it up overnight, and that’s kind of what’s meant by a staff readiness.

Tricia:     21:26

A lot of times we find that the staff is not prepared for the change. They’re not data literate, they don’t understand what a bar chart is. They don’t understand how to read a line chart, they don’t even understand it. A Pie chart, giving them bad information and senior management has to be ready to get the staff ready. Alrighty.

Chris:     21:47

Blocker number two, there’s no buy-in and it literally looks like a tug of war management is going one way and the staff is pulling them the other way. And maybe as part of the data staff you recognize yourself in this picture and there are different ways this goes on. So a lot of times what I find happening is leaders want to trust their gut over the data and they just had this big issue with trying to move further with the data. They just maybe, you know, what we’ve always done in the past has worked so they don’t want to do that.

Tricia:     22:19

The other thing that happens is maybe the leader just doesn’t understand statistics when they don’t understand, you know, they haven’t had it since college or maybe they didn’t even have it in college. And when you start talking about machine learning, I mean it just sounds like something so scary when it doesn’t have to be one of the people, even after they look at all the power of the data, they are going to start putting up some different kinds of resistance. So they might say things like, oh well even Harvard business review says that companies are not data-driven anymore and it’s not as big of a thing as it used to be. So you have to learn to see resistance in all of its states. But what is it that actually causes the resistance? And I think it’s fear. I think it comes down to people just being afraid of making a change and people being afraid of what that change means.

Tricia:     23:11

They may have alike an organizational structure built up and the data and analytics is going to disrupt that. And disruption is happening all over the different industries. So I understand that fear. So you need some strategies to kind of overcome the fear if the problem is that they just don’t understand.

Chris:     23:29

One thing I like to recommend as an analytics coach now and analytics coaches useful for several reasons and they’re particularly good at the level two to level three transition. If you think about a coach, a coach is someone who’s going to come in and work with an entire team to win games and to win season. Someone who’s got the big picture in mind, someone who is trying to make everything better and the first thing a coach would do, like if I went into an organization, I would level set them. I would want everyone to understand here’s what statistics mean, here’s how you use machine learning.

Tricia:     24:01

Here’s what the data does for a company. Help people understand what it means. I know the business value of data, so you have to translate that cause when you particularly got someone who’s got a gut feeling with one thing I like to do is say, okay, tell me what your gut says about this situation and then show them what the data says. If the data supports what they’re saying, then that’s great. It also, it should give them some confidence that the data can get the right answer, but I also like to add a forecasting component and say, what do you think’s going to happen in six months or a year? Here’s what the data says is going to happen and they can’t always do that as well and they have to recognize their weakness so they can make way for some of the newer strategies.

Tricia:     24:44

Another important part of the coaches, you’ve got no political loyalty because one of the things that happen with an internal person is a, you’re seen as being a member of a certain team and being part of that, but the coach can not have any political loyalty. The coaches loyalty is to the company succeeding, not to individual players succeeding. The other thing that coach does is that set, it can’t just be something where you’re looking at it one day. You have to sit through several months, several meetings and look at data and help the leadership team understand. Is the data being presented to you a whole, is it a leading, any questions unanswered? Is there any way that a person could trick you with that data? And that’s important to know. And it’s important for leaders to have thoughts in their mind that they can spot bad ad analytics because we all know that it’s out there.

Tricia:     25:35

So another strategy, think about if you do a proof of concept. So look for small scale projects with achievable results. And when I say the small scale, I mean something where you can get a result within a month and you know that the data exists and you can see something where you’re going to get a high return on investment. I would look for areas where the company spends too much money or something where it’s close to the customer. Maybe you can show the company a better way to invest the money, but if you can find something where it really changes the bottom line, you’re going to start getting the buy-in. You’re looking for, Alrighty. So the leaderships commendations are getting an analytic strategy, use an analytics coach and show a return on investment to get buy-in.

Sarah:     26:24

Thank you. Tricia. Looks like we have some questions in the queue. I’m going to go ahead and ask you one. When you are creating your [analytic] strategy, what kind of questions do you ask to get started?

Tricia:     26:38

That’s a good question because what I see a lot of organizations doing is they say, well how do we save money? And that question is just too big. Try to get your scope down like a, how do we get better outcomes for our customers is a good question because once you get customer focus, a lot of things in the company will change.

What would improve the productivity of each worker? That’s another good question to ask. Like if we wanted people to be better at their job, what would we have to do? So, great question. Thanks

Sarah:     27:08

Tricia. I agree. Clearly, leadership is a big area in the data maturity model.

Chris will be discussing is data. Chris, do you want to take it away?

Chris:     27:17

All right. So what Sarah mentioned, data is one of the areas when we go in and start working with our customers. Data is a huge area where we see a lot of blockers put in place. Now, data presents a lot of opportunities and challenges all along the analytics spectrum curve, right?

Chris:     27:36

So as you’re trying to move up the analytic maturity model, data can be one of those big, big roadblocks that you run into. And one thing that a lot of our customers don’t realize is that the farther you get along the analytics maturity model with doubt without addressing these data issues, the bigger those problems become that because they simply compound over time.

Chris:     27:55

So investing time and resources early can really save a lot of painful pains and heartbreaks down the road. So the first blocker with data that we run into is bad data, right? There’s a lot of roadblocks around bad data. In fact, Gartner tried to put some numbers around this and how they wanted to figure out how much does the bad data costs the average company each year. So before we give away the big mystery, let’s take a second. Let’s do another poll question. So nd pull it up for you real fast and um

Chris:     28:30

One second here. All right, so launching the poll. So the question is companies estimate poor data quality cost them x amount each year. So go ahead and take your votes. I’m about 20,000, a million, 7.5 million, 15 million or the all-encompassing eleventy-billion amount.

Chris:     28:56

All right, so looks like we still have a few more coming in. Give you about five more seconds here.

Chris:     29:08

All right, perfect. All right, so let’s take a look at the results. Okay. So about 40% of you think it costs 7.5 million, 20% the eleventy billion. So that must be very costly for you. Please come talk to us. We have some solutions for you.

Chris:     29:32

The size of the average company loses about $15 million a year, right? Those are high costs. And how that cost manifests themselves. Route your bad data. Well, let’s take a look. We have a few different ways that we’ve found that bad data can hurt. So the first way that these dirty data costs manifest themselves is through increased operating costs. We also see it through decreased revenues, missed opportunities, reduction in cash flow, and then finally increased penalties, fines, or other charges. If any of you remember what happened to wells Fargo a few years ago, all of those types of problems can be avoided if you take care of your data and have a good grasp on what is actually taking place. In fact, I also worked with a customer recently. There is huge health care provider and when we went in, one of the first things they told us is we don’t understand the why we do well, we don’t understand why we do poorly.

Chris:     30:28

We just keep doing what we’re doing. And so first step was to get a grasp on their data problem because they weren’t able to analyze it in a meaningful way. And very quickly in just a few weeks, we were able to find some massive opportunities for growth where they were already, they had some great things put in place. They just weren’t capitalizing on them. And so these are the types of opportunities that you can miss when you don’t understand your data.

Chris:     30:55

So what, what is the recommendation? How, how can we fix this problem? Well, you need to get control of your data and steer it back in the right direction. Forgive the picture Pun, but oh, we have three steps that you need to take in order to get control of your data and steer it back in the right direction. First is to create a team, right?

Chris:     31:16

And it’s not the size of the team that matters at all. It’s, it’s the people that you bring onto the team because you need to bring in the people with the domain expertise and also the people who are invested in this data which are going to be using it for good and trying to find those actionable insights for you. So you need to have a diverse team so that all the stakeholders are represented. And then once you have that team in place, kind of like what Tricia said earlier, focus on how you use data and find some early wins, right? So this is a really good approach is to focus on data that are customer-specific, right? Projects, where you can either increase you, are understanding of who your customer is or improve the customer experience, right? So try to focus on that high use data, specifically, customer based and the finally automate the process, right?

Chris:     32:06

As if you’re trying to move up the analytics maturity model. It’s vital that you get rid of user error and that you just simply create some automated processes in place because it’s going to free up your talented people to actually start exploring the data and, and digging up those actionable insights for you. And so I’m going about it this way can really create a good strategy for success and create that type of understanding and buy-in from your executives and they’re going to be champions of you and help drive your progress forward. And then finally basically to sum this all up is treat your data as an asset or it will remain a liability, right? And this is the slide that you’re going to want a tweet. So go ahead and take your screenshots every one and send it out to the Twitter-verse.

Chris:     32:56

So that’s our first blocker though, right? Is that dirty data. Now the second blocker that we see in the data world is siloed data and I’m sure everyone here has experienced with siloed data and it keeps you up at night. I’m sure that it even infiltrates your dreams at night and giving you nightmares. But for those of you that don’t have experience with solid data, it is exactly what it sounds like. Siloed data. You get these walls in between different datasets that basically provided or prevent eager, easy aggregation or joining of the datasets. And so you need to focus on breaking down these silos. Now we see data silos and a number of different ways. Um, os isn’t always a bad thing. Oftentimes they’re legitimate reasons to have silos put in place such as protecting customer private data, right? Other times we see that they exist simply because there hasn’t been a need up until now to break down those silos.

Chris:     33:50

All right, but what are the different types of silos and how can we address these issues? Well, let’s take a look. The first type of solid that we see when we’re working with our customers, our structural silos, right? We saw the silos exist because code was developed for a specific need for a specific situation, but those situations or needs and no longer in place. And so breaking down these silos are going to be very valuable. The second our political right, most a lot of people have experienced with these groups might be protective of data within their domain. Unmind of like with the consumer data trying to protect personal information. And so they put up these data silos to prevent other organizations from getting access to their data. All within the same business. The other type is growth, right? And this is a common one as well.

Chris:     34:38

In fact, we went in to work with an insurance provider and four years before we had come on board, they had acquired a smaller insurance company and they had been working over those four years to bring that data together to try to merge those data sources. And by the time we had left, they had expected it to take another four years in order to finish the task. And so growth is a big area where you see these data silos naturally come into place. And then the final one or mender lock-in silos, right? No. vendors know and recognize that access to data is power. And so they’ll actually work to limit the access to data within their applications. And this can especially be true of software as a service solution. And so these are the type of silos that we’ve seen when we go in to work with our customers.

Chris:     35:27

So what’s the, what’s the best type of success strategy that we can put into place? Well, when we go come in to a customer site, we often hear this idea of let’s create a data lake, tear down the silos, get rid of them, and throw all the data into this massive data lake so that we can get this beautiful pristine location where all the data works harmoniously together. Well, the problem is that when you break down those silos without any sort of strategy or ETL processes in place instead of a data lake, you often get a data swamp and nobody wants to live in a data swamp unless maybe you live in Louisiana. But seriously though, breaking down these silos without having a quality strategy and ETL process in place can be devastating. It can end up just creating a lot of bad data that you can’t put to any kind of use because you can’t trust the results.

Chris:     36:16

So what’s the actual best solution for this?

Chris:     36:20

Well, again, find a proof of concept, right? Specifically, you want to focus on high-value projects, especially, especially ones that are going to have a fairly quick turnaround. Okay. You don’t want to take on a project that’s going to take you four years to complete because there’s going to be burnt out and management isn’t going to want to continue to be that sponsor for you. And then finally you also need to focus on utilizing data across data silos is critical when trying to how to manage the value in and breaking down the silos because you need to show them that there’s a lot of great cross-pollination that can happen within the data. To find projects, it’s not going to take much time to show some good return on your investment. So we’ve talked about leadership as one of the big blockers and some of the steps to overcome those blockers. We’ve talked about data and specifically removed data quality issues and how to break down data silos. Now, Sarah, it looks like we have some more questions.

Sarah:     37:20

Chris, it looks like there is a question I’m in, in the queue for you. You’ve said high-quality projects several times. Do you have an example of that, Chris?

Chris:     37:30

Yeah, actually I do. So that’s a good question. And how do you identify those high-quality projects that can be tricky. Again, you’re trying to find low hanging fruit and example of where we did this was we went in and we worked with an online advertiser who handled the marketing for a lot of large automotive companies like Ford Hoda, things like that. And so their problem was that they go in and they contract with these car companies to get them. So many hits are so many viewed advertisements each month. And so what would happen is that their problem is twofold. One is if the over-delivered on the number of hits that they got, then that was money left on the table because the auto companies didn’t have to pay for those extra advertisements that they’re receiving. But on the flip side of the underdelivered, then they had to give money back to the car companies.

Chris:     38:25

Right. And so it was, it was this double-edged sword where they’re losing out on both ends. And so what we did was we went in and looked at their data and they weren’t looking at their data holistically, have these data silos in place. So we, what we did was we brought, we figured out how can we bring all of this data together in a logical way. And then in a matter of a few weeks, once we broke down the silos, we were actually able to generate some really powerful time series forecasting models for them. And what we ended up doing is in less than a month, we were able to decrease the error rate on the number of advertisements that they’d get from each campaign by over 10%. And so that, those are huge cost savings opportunities that required very little effort from an implementation standpoint. And so those are the types of projects that you want to look for. It looks for low hanging fruit. Okay.

Sarah:     39:14

Wow. That was a really great example of Christ. Let’s go ahead and jump to the third block or do you want to going to start it on that?

Chris:     39:20

Up to this point, I mean, we’ve talked about the importance of strategy. You talked about the importance of data. So what’s the point of talking about people? I mean, from what it sounds like to me up until this point, if you have a quality strategy in place and you’ve got the data in place, the people don’t really matter. You can put a potato in the place of a person and everything’s going to go along swimmingly. Well actually that’s not true, right? Because people and skillsets that such a critical asset to invest in when trying to move up the analytics maturity model because you need the people capable of finding those analytical insights and turning those insights into action. Action, right? And so you need to invest in your people and your skillsets. Now inevitably, as you do this, you do run into issues.

Chris:     40:05

So the first blocker that we run into when you’re talking about people in skillsets is the culture issue, right? Culture problems present themselves in a number of ways. Oh, you can get stubbornness to adopt new ideas. This hen you’re bringing on a new project or PR or product, this fear that my skill sets are becoming obsolete. And so there’s this kind of resistance to change or this idea that the current projects just the flavor of the month. And so this passive resistance until the fad fades away or a statistician, I get this a lot. My Gut knows better than your math nerd. So, so you see these cultural issues pop up quite a few ways, but you have to address it. And one of my favorite quotes on, on culture is from Peter Drucker. No, I know I’m a millennial, but even I know who Peter Drucker is and he says that culture eats strategy for breakfast.

Chris:     41:01

And we all know that breakfast is the most important meal of the day. So you need to address your culture. Another really great example is Tony Shay from Zappos. He says that for organizations, culture is destiny. And nobody embodies this better than Tony Shay. For those of you that aren’t familiar with Zappos, they are an online shoe retailer. And when Tony was getting his company started, he firmly believed that customer service was going to be what was going to drive his company forward. He believed this to be true and so what he did was he took all of his marketing campaign and his budget and he pumped it directly into customer service training so that every single person that was hired by Zappos had to go through several weeks of customer service training. Every single person went through the same training, they all got paid for their time, but then when the training was over, he upped it up and said, I will give you 2000 extra dollars to just simply walk away.

Chris:     42:03

Why did he do that? Because he knew that he needed to build that culture that was going to buy in or get people on board that was drinking his Koolaid, right? Because he knew that his customer service was what was going to drive him forward. And that is why today if you are out in La and you’re stuck and you can’t find your way to the zoo, you call Zappos, they’re going to get you from point a to point B. No questions asked whether or not you bought shoes from them. That is a type of service that they provide. And so the culture is critical when you’re trying to move up the analytics maturity model. You need to get people to buy into your vision. So how do you do this? Well, with the analytics coach now, I cannot stress this enough everyone, and I mean everyone needs a John Gruden in their life, right?

Chris:     42:52

Those of you that aren’t familiar with Jon Gruden, he’s been the face of professional football for a while. He was a face of Sunday night football. He’s won super bowls. He’s currently the head coach of the Oakland Raiders and he just has that iconic face. And that voice can, I mean, I would just love to be a fly on the wall in a conference where he just interrupts and said, man, I tell you what, that’s one heck of a p-value, right? Like everyone needs that in their life. But the reason that I like the Gruden example so much is that he does something so well. He takes what you’re watching on the field. It can be very chaotic and messy, but he takes everything that’s happening on the football field and he breaks that information down into bite-size pieces that even the most layman football viewer can interpret and understand and be able to follow what’s happening on the field that they’re watching.

Chris:     43:41

And that’s what you need out of a good analytics coach. You don’t just need somebody to create buy-in from the management level. You need somebody that can break down the vision into those bite-size morsels and explain it to your people, to your talent to help get them on board and buying into your vision. And so that you can take progressive steps up the analytics maturity model.

Chris:     44:05

Now the second blocker. So first blockers culture, the second blocker are those sweet, sweet skills. And I’m not talking about nunchuck skills or bow hunting skills. I am talking about technical skills. Now let’s pause and take a quick break or take a quick poll question. When you’re talking about analytic skills, what as pull up this poll here, what you believe is the most important. Okay, so true or false, a good strategy is to seek a Unicorn data science to scientists for your organization. So go ahead, click away with your fingers on your mouses and let’s take a look. What do you believe is most important?

Chris:     44:45

So it’s true, false or it’s complicated. All right, so we have a number of votes in wait for the last few. All right, can you hear jeopardy music playing in my head?

Chris:     45:02

All right, let’s take a look. Awesome. All right. You guys, you’re smart cookies. That is false. Or maybe it’s also complicated, right? It’s kind of a tweener because while Unicorns, I mean, who doesn’t love a good Unicorn? There are these beautiful, magical beasts that just solve all of your problems. Now, for those of you that don’t know what a data science Unicorn is, it’s essentially that person that encompasses the end to end skillset. Somebody who is able to understand the data management and ETL processes, somebody who’s able to do the mathematical computations to find those acts, those analytical insights. And then finally that person who can also disseminate that information, right? Do the data visualization end because your analytics is only as powerful as your ability to put it into action, right? And so that’s what the data science Unicorn is. Now there’s a problem with the data science Unicorn.

Chris:     45:57

A lot of companies, we go in and talk to them and they were like, if only we could find one or two unicorns to solve, solve all of our problems and we’d be golden. Well, well that’s true for a minute. There’s kind of this unicorn paradox because while they do fit that perfect blend all in one person, you’re not going to keep a Unicorn tied down. They are free spirits. They know that they are valuable and the grass is always greener on the other side. And so after a year or two of them just churning out these amazing results for you isn’t going to inevitably be this day where they’re going to come to and say, I’m sorry, but our blessed for marriages over, I’m leaving you. And that’s devastating because what happens is after you spent

Chris:     46:36

So much time and resources in this person to be that Jack of all trades, the moment they leave, they creating massive brain drain for your company. And so you need to avoid that. So instead of looking for those magical beasts out there, focus on creating a team, right? You, so build out that end to end solution within your data science team. Look for the people who have the data engineering skills, the database skills. Look for the people who can do the data science of programming the statistics, and then find those people who can generate those insights and disseminate the actionable insights down to the management level so that they understand what, what to do next. Most importantly, look for people who are lifelong learners as you’re doing this because, well, I’m, Unicorns are great. You don’t have to hire them off the streets, you can actually build them or grow them in house.

Chris:     47:28

So look those people, when you bring on a data engineer, somebody who wants to also understand the statistics and the data storytelling, because as you build out the team, we all know that teamwork makes the dream work. And so as you end up building out these people or this team of lifelong learners, what ends up happening is that you build up this team of people who not just consisting of one or two people, but they become this unicorn or heard of lifelong learners who have this thirst and a hunger to analyze everything and they’re like the white walkers coming down from the north who were just going to destroy all problems in their path, right? So you end up creating this team of Unicorns that are going to help take you to that next level and upward and onward to infinity and beyond as Buzz Lightyear likes to say now.

Chris:     48:18

So we’ve covered leadership, we’ve covered data, and we’ve covered people, right? Bring in that coach to address the culture issues. Build out a team of people that can solve your problems, not just looking for magical beings to be your end-all solution. Now finally, as we’ve been going through this, I hope that you’ve kind of taken a look internally, right? So we asked you, where do you think you’re on the analytics maturity model? What would help though you’ve provided some quality insights and that we’ve addressed some of these problems. There’s that, you’ve taken a minute to think about what are some of the problems that I have today in my company that I could fix tomorrow with some of the information that’s been shared. All right. And so that’s the end of my presentation and then I’ll hand it over to Sarah.

Sarah:     49:00

Thank you Chris. And thank you for joining. As previously discussed, we will be sending an email following this webinar with this checklist for your organization if this content is sparked your interest. If you’re interested in learning more about our company, please come check out our website or follow us on Twitter or LinkedIn. We’ve got a lot of great content and we’d be happy to share it with you. Next month we’re presenting a special webcast with our partners, SAS, Ocearch has been assisting with unraveling the mystery of great white sharks lifestyle on join Tricia, Jamie and Ocearch founder Chris Fisher as they discuss how to tell a persuasive data story with an unpopular main character. All right, now we’re going to go ahead with our Q and A section. Be sure to type any questions you have into your zoom control panel. It looks like we have right so below the picture you can see a little icon for a chat and that’s where we will see your questions. It looks like the first question, the Q is for Chris. Chris, how do you get an analytics center of excellence started?

Chris:     50:13

Great question. And that is kind of the question as we go through with our customers and trying to address where they’re at on the current analytics maturity model because most of them don’t have that decent of an analytics center of Excellence in place. Now we’ve kind of hit on this quite a few times as we’ve been going through the presentation. It all comes down to creating early returns on investments. So as you, as you go through and address where you’re at currently, look for that low hanging fruit, right? So kind of like the example that I gave earlier of that online marketing company, they needed to be able to improve their forecasts, right? They needed to get better predictions of how many advertisements they should promise. And so look for those problems that are already readily available, right? It doesn’t require a whole lot of work because what you need to do is you need to make management champions of you and you do that by finding those projects that are going to have a direct impact, especially ones that are customer based and customer-facing. Those are ones that management loves to solve. And so as you do that, you begin to build up some trust and investment and you end up creating this hunger and thirst for the type of problems that you can solve. And so that’s how you need to take that first step. Don’t bite off more than you can chew, handle something that’s manageable within the first few months.

Sarah:     51:39

Great. Thanks for answering that. Chris. I’m going to ask the next question for Tricia. Is it possible to be on several levels of the analytics maturity model at once?

Tricia:     51:49

That’s possible, sometimes it’s not this clear you’re in one stage or you’re not at one stage. There are several parts of the organization. It may be different stages. So you may have just really excellent data and you understand how to use it. The staff is there, but sometimes the leadership team’s still isn’t there. So it feels like you’re somewhere between four and three at times. But no, it doesn’t all happen at once. And it does take several years. So just be prepared. So I mean, several customers have come to us and said, okay, we want to be at level five and next year. And they’re level one. It’s like, no, it’s just, it just doesn’t work that way. It’s the entire organization coming to maturity.

Sarah:     52:29

Thanks Tricia. I’m going to ask one last question. Does the coach always have to be external?

Tricia:     52:37

No. usually on level one, two and three, the coaches, usually an internal person, and I said earlier, it could be someone from the business, it could be someone from the IT organization, but it has to be someone who really gets analytics at a real gut level and they’ve seen it transform organizations and they understand what it can do for their organization. When you start transitioning out of three and four, it does help to have an external person there. And the reason it helps is that for whatever reason, organizational behaviors, what I’ll call it, organizations will trust an outsider even if they say the exact same thing that the insiders say, but the external person doesn’t have the political alliance. And that is what is so essential to part of this and how you

Sarah:     53:26

Get that person, how you get the team to follow that person. So it’s a mix of both of those. Great. thank you for answering that question in Tricia. Well, that’s all the time we have for today. A link to the recording of this webinar will be available in a follow-up email as well as on our website. Our presenters will email out any answers to any questions that they weren’t able to answer in today’s presentation. I want to thank everyone for joining today. We appreciate you being here and we look forward to seeing you next time.