Making Data Work For You: Turning Data Into Insights Webinar - American Technology Consulting

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Making Data Work For You: Turning Data Into Insights Webinar

Nick Reddin

Published October 31, 2019

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Link to webinar:  Making Data Work For You: Turning Data Into Insights

The power of data can be extremely valuable and influential to an organization that knows how to use it strategically. However, sorting though a mountain of information to find the needle in the haystack that dramatically and accurately aids a company with successful decision making takes careful consideration and thoughtful implementation.

ATC's Nick Reddin talks with Jason Greer, a strategist and owner of Higher Standard Consulting, about using data to gather critical insights that can help your business reach various objectives.

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Introduction

Nick Reddin: Good morning everybody. Appreciate you attending our webinar today. Today we're going to be talking about how to make data work for you. Turning data into insight. My name is Nick Reddin. I'm Vice President for ATC. I've been doing this for quite a long time, almost longer than I care to count. I'm very excited to be here today. And with me is Jason Greer. He's the owner and results strategist at Higher Standard Consulting. Jason, 19 years of experience in information technology. 14 years of the Six Sigma practitioner. He's traveled nationwide helping companies understand the value of internal and external data. He specialized in process improvement and process design, business intelligence, design, financial modeling, business metric design and data governance. 

Jason is a constant speaker on this topic in particular, and he's a great guest for us to have today. So Jason, welcome to the webinar. 

Jason Greer: Well, thank you. It's great to be here. 

Interested in being a speaker on our webinar? Let us know!

Nick Reddin: Excellent. So a little bit about ATC. So we are a consulting firm focused on business solutions. We operate in a number of verticals, business intelligence being one of those verticals with a strong focus on data reporting, dashboarding, and then also a lot of focus around process automation and then the future of work. And with that we're going to kind of get right into things today as we talk about data in analytics in particular. So today there's three takeaways that we're going to look at: conducting rear view mirror assessments, using data to drive changes here and now, and then understanding signals to shape the future. So Jason, these are the three things that we're going to be unpacking today. And just kind of from a general perspective, how important are these things? 

Jason Greer: If we look at it from a really simplistic perspective, data really has three values. We can use that data to look backwards and understand where our business has come from. If you look at the middle one, it's more about understanding the here and now. That's where we get into control methods and trying to understand in real time what our processes are doing. And then if we look at the one on the right, we're looking into the future. We hear the buzzwords for artificial intelligence, machine learning, but it's how can we start to try to use our past results to understand what the future may hold for us. 

Nick Reddin: Perfect. Now you call this graphic the route of effective change. How did you come up with this framework? 

Jason Greer: So there is an old quote by a guy named Will Rogers that says, "Good judgment comes from experience. And a lot of that comes from bad judgment." So, in my case, I've made many mistakes along the way actually. And so I can't say this was a certain epiphany other than trying to learn from every project that we worked on. And you know, being in this space for many years now, I would say really then heavily involved in data for about 14 to 15 years. And we've learned a lot and made a lot of mistakes and, hopefully, make less mistakes as we go along. 

Nick Reddin: So like anything, this is just something you've been refining over time? 

Jason Greer: That's right. 

Data Analysis is About Asking the Right Questions

Nick Reddin: The circles get bigger, smaller, and moved around. So talk to me about design thinking. I know that that's a piece that is something that's near and dear to you as a part of this. 

Jason Greer: Yeah. So really where this methodology came from is this idea that when I first started consulting about nine years ago, we started going into clients and I started noticing a trend, and when the IT department would meet me at the door and give me the proverbial keys to the database and say, "Go help yourself to 20 million rows of data and tell us what we need to know about our business." What I found with those projects is we were less successful than when the CEO or when the executive showed up at the door and sat down with us for two or three hours and really laid out how their business was running. 

What the frustrations they had were, what their perceived risks were and really what they wanted data to do for them. At the same time I was studying design thinking quite a bit and started to see the correlation. If you think about design thinking, it really is about understanding that voice of the customer and understanding exactly what their needs are. I liked a couple of these quotes and really started driving down to how do we make this, how do we make this concept of data analysis more repeatable, and this is how we came up with it. So if you look at that first quote, how do we listen and observe and understand, sympathize, empathize, synthesize and gleam those insights and turn that into something where I can walk into any organization, whether it's a cheese factory or a hospital, and ask questions that get us to that point. 

Nick Reddin: So when you say making the invisible visible, which is a great quote from from Hillman Curtis, talking about the data, talking about thousands and thousands of rows, then the design and the output becomes a paramount importance. 

Jason Greer: Yeah. I actually stumbled into a lot of the business intelligence work that I was doing. And I came in and they said, Hey, do you know business intelligence? And I thought, well, I've been in this industrial design space for quite awhile with Six Sigma and lean. And I thought, I know what that is and very quickly realized there's a whole different study of how do we take 10 million rows of data and try to gather those insights in a way that actually influenced the organization in a positive way. So this quote was big. It really drove me to figure out what that was. 

Nick Reddin: So when you talk to companies about this and when you first go in and they've got an idea of what you're going to do, how you're going to do it, or even how they want you to do it, and you start to talk to them and probably in a little bit of a different flavor than what they're used to is there a lot of pushback from them when you start to say, "Hey, this is really going to be the strategy we need to adopt here?" 

Jason Greer: Yeah. You know, I would say a lot of organizations, they think more is better. They've got some misconceived notions about what data can do for them I think in a lot of ways. And so that organization, so I'll give you an example. I had one organization where they had come in and said, "Hey, take a look at all the metrics for gathering." And if I remember it, it was about 168 charts that they handed out to at the director meeting every day or every two weeks, sorry. And in that meeting, they would go around the table, and I'm sitting in the back of the room watching this and we'd give to the director of marketing and they'd say, "Well, if you look at chart 57, you can clearly see is my pet project because it is the most important one and we should drop everything to do what I want to do." And then we moved to the next person, they'd say, "But if you look at chart 83, you can clearly see that the business needs to go in this direction. "And what started to become clear to me is that we can take data and we can glean a lot of insights, but if we don't have the right question to start with, we're going to make some really poor choices. And, I tell people, give me some data. I'll give you a hundred charts by Friday and I can almost guarantee most of them will mislead you and take you in the wrong direction. So it's not just about the data, it's about asking the right questions and understanding what actions we want to get to in our business. 

Complexity is the Enemy

Nick Reddin: Sure. So one of the things I've heard you say is complexity is the enemy of action. That's a pretty powerful statement. What do you mean by that? 

Jason Greer: Let me show you a few examples. So as somebody coming in, brand new to an organization and sometimes every three weeks I was in a completely different industry. What we really were trying to focus on were the insights, so we've asked a question and now it's a matter of putting on our... getting to the thought of what insights will help us answer that question. So I pulled a few random charts off of Google here and what I was finding was, sure we can go develop and business intelligence is to a point where the graphs and charts you can create are amazing. But at the end of the day, if I were to take these and put them in front of a group or a panel of people, could they all understand it so that we knew what action we needed to drive our business. 

So when we look at it from a complexity perspective, I'm going to skip actually one slide down. But this was really the mantra that I was using. So if we can't explain it simply, then we don't understand it well enough. And that's Albert Einstein. And so from that perspective, you'll see that whenever I'm designing and in the business intelligence world, I'd probably create ten charts for every one that I show to the client simply because as I look at it, I realized that insight doesn't clearly and simply help me address the question that we're after. 

What is a Good Outcome From Analytics?

Nick Reddin: So, you know, in construction, a good outcome happens when you measure twice and cut once. What would you say is a good outcome for analytics? 

Jason Greer: It can be a lot of things to a lot of different organizations. Let me give you an example. One of my clients was a community college at one point. And I was new into business intelligence and we were using a really powerful tool. And the idea was, is how do we make this where the client just says "wow?" So we went before the board of directors and I had spent about 80 hours developing what I thought was this really cool dashboard where you'd click on things and they would rotate and you could ask a question and I would rotate back and show you a chart. And I was very proud of it. And as we're going through that, one of the questions that one of the professors had was, "I would love to just see if you could tell me all the students that got an A in Math, but then the next semester or any other subsequent semester, they get a C in English." 

And what I realized is that I had built all this complexity and I didn't really understand the outcome that we were trying to get to. So I went back to the drawing board and we created this analytic that allowed us to better understand and help clients drill down into the data and they could do unions and intersects and all kinds of things that allowed them to get to the questions that they were really after. So from an outcome perspective, I'm really after how do we understand the psychology? How do we get a group of people to understand the data so that we make good choices with our business? So here's another quote from Eli Goldratt. If any of you are fans of the book he wrote backI believe in the late eighties, called "The Goal." 

A lot of people had to read that in grad school and the quote is, "Tell me how you measure me and I'll tell you how I behave." And so it's real easy for us to go out and develop, use data to develop dashboards and metrics and understand an organization. But if we're not doing it and thinking about the psychology of how that's going to affect people, we may actually do more harm than good. So I'm not sure if you have ever seen or maybe in your past life, I would say that I haven't seen too many of these signs around lately. But every once in awhile you'll see a banner at an organization and it'll say, "252 days since our last accident" or something like that. I think that the methodology of saying, “hey, we're being safe and our organization has a great idea, but if you can also think of the negative consequences of that. I've seen it where its, hey we're going to have a pizza party when we get to day 300." 

Well, we get to day 299 and somebody slips and falls or somebody gets cut. And what is that person going to do? Are they truly going to say, "Hey, I had an accident and I need medical attention?" Where are they going to hide that? Because they get to walk past 50 of their coworkers and show everybody that they don't get a pizza party simply because they were dumb enough to get hurt that day. Right? We know everybody wants their pizza party. Right? So what one of the focuses and probably where this idea of industrial engineering comes into is outcome. How do we drive the outcomes that we want? Not necessarily how do we just measure something because we’ve got data in our hand to measure it.

Nick Reddin: So do you think that a lot of these companies are even having the data sources that they need in order to capture what they want? 

Jason Greer: You know, that's a big problem. Most of the time that I go into an organization we are, taking analysis of what they have a really good...whenever we go through this model, the questions that answer the biggest problems are ones where we need multiple sources of data. So, getting back to your question, most organizations do not have that. And we spend a lot of time trying to figure out what we need and how we're going to gather it and building data models that bring those things together. That's really where that term of data governance comes in. I know we're not going to get to that today, but as this is a very important piece to this entire data insights world and it's going to only get more complicated as we go. 

What Does it Mean to Weaponize Analytics?

Nick Reddin: So one of the things that you've said is you don't measure the easy because it's easy to measure and people can, when they're getting the data sources that they want, they can potentially, weaponize their analytics. 

Jason Greer: Yes. I'm going to skew here. You're going into action, so yes it is so easy to weaponize those analytics and use those as a way to get what we want. And if we really don't understand what that psychology is and where we're taking people, we can certainly take an organization in the wrong direction. 

Nick Reddin: So have you had any examples where, you've gotten in, cause I know you've traveled the country, you've met with every kind of company from manufacturing to retail to just about every industry that there is out there. Has there been a time when they've asked you to use data in a way that may not even be a great way to use it and you had to kind of push back against it? Maybe? 

Jason Greer: Yeah. You know, I had an opportunity that presented itself and this was many years ago, a casino out-of-state to me. And they had asked if we could build them a real time measurement tool to get and look at slot machines. And they wanted to look at the number of polls that were made on every slot machine in a live basis for any users. And so you can envision, there's a hundred people on the floor and they slide their card in. So we know who that person is. Now we know that they've pulled 14 times and we also know that they haven't won yet. They wanted us to understand the threshold of every person and when we thought that person would leave a slot machine because they hadn't won in time. And so their idea was they wanted a manager walking around live with an iPad in his hand so that he could monitor and see red flashing lights of who do I need to bring their favorite drink so that they will forget that they haven't won in awhile and keep pulling that slot. 

That was an opportunity that I turned down. It was too easy for me to visualize a family member or a family friend sitting there and being taken advantage of. So there's a lot of opportunities where we can see where data can be used in a poor way, and I see it in the news every day probably. 

Nick Reddin: Yeah, absolutely. No, that makes complete sense. So talk to me about the, model that you'd have up on the screen. 

Jason Greer: Yeah. So this is a model that I think is really important. It's something that I stumbled on about eight years ago. It was written by Deloitte. You can see that there. But I think it does a really good job and it really gets to the three key points that we wanted to cover in this presentation. So if we start at the bottom, you can see that we've got hindsight over there on the left and then we go into insight and then we go up to foresight. 

I use, this is what I tend to refer to it as the ladder. And I think that it's important for a client as we go into an organization to assess where they're at in their different areas, whether it's on a shop floor or whether it's in our sales department or whether it's in their management. Where do, where are they from a metrics perspective, so that we can walk them up this ladder. In today's world everybody has Excel on their desktop and everybody thinks they can make charts. So it's easy to say, sure, we do data analytics and you can see that at the bottom of those rungs. But as we progress higher up, we're creating more value instead of just looking back and saying, "Hey, we did great last month." Now we're saying, "What parts should we make next month so we can do even better?" So it's a very good analysis tool to help us move up that ladder with a client and help them understand how they're going to get to where they want to go. 

Nick Reddin: So how hard is it when companies are very mature? They're working off Excel and I've worked for some incredibly large companies that lived off Excel for the reporting, but then you're also taking the reporting out of the manager's hands, and a lot of the managers to your other points, they like to construct the data to their benefit. I've been in that place myself back in the day and now I don't have control over that. And somebody else's going to be publishing the data forward and they'll probably consult with me on it. But how difficult is it to move the analytics and reporting from the manager's desktop to a more centralized location or maybe even the center of excellence of some sorts? 

Jason Greer: I think that's it's very important. And I think about data as a title wave and this title wave has been growing for the past 15 years, and we are now getting to a point where every business can gain insights from data. But because data is so new to us and this terminology of big data, artificial intelligence, we really just haven't matured organizations quickly enough so that they understand and that we can govern this, the analytics and metrics. It's too easy for an organization to go query something and say, "Hey, I know something." And there is a lot of science to working our way up this ladder because, again, anybody can create a chart and anybody can get an insight, but does it truly get us to the point where we start getting to actionable results and answering questions that we started with? And it gets back to your comment earlier about weaponizing. We're in this to drive results for an entire organization, not just make somebody feel good about themselves. 

Nick Reddin: Do companies have enough structured data to make sense when you go in? Cause I know it seems like companies have a tremendous amount of unstructured data, but yet they want to try to gain insight out of it. Is it hard to get them to understand that their data isn't even structured correctly when you first start talking to them? 

Jason Greer: Yeah, it takes a little while and usually if I can show them one chart that's not working simply because their data's not right, they start to get a picture of it, and that gets back to that data governance conversation. It really requires somebody that's got a good data modeling background. Somebody that understands ETL. They are the extract, transform, load. It is important that we are pulling data. We are creating models that tell a true story of an organization. One misjoin of a couple tables and you can easily make some big mistakes with how you interpret your data. 

How Do You Focus a Client Toward Their Goal?

Nick Reddin: Absolutely. Every action has a reaction and action is at the heart of your framework. So how do you focus your client toward this goal? 

Jason Greer: This is really the point, right? So, I use the saying, and I'm waiting for somebody to argue that I'm not correct, but I'll keep saying it until somebody does. I don't believe that we should measure anything that we're not willing to change in our organization. There's too much work to be done to measure everything. And so when I work with a client, I use this framework because I really want to condense them down and not say, what are the 168 things we can measure? But instead, what are the really big questions that we need to answer? And then what actions are we going to take? And so a lot of times what I'll do is I'll take a chart or a graph that we've created and I will put it in front of an executive team. And, I will ask two questions of them. 

Do we all agree on what the information is and do we all agree on what actions that we're going to take? And so, I want an organization to use data and use metrics and use dashboards in a way that is extremely intentional. And so I set up and I create structures around that data so that we are very intentional. So one of the things, and I'm jumping around here next, so I apologize. One of the things that I find is that this really is a process around discipline. And I heard somebody speaking one time and they were like, let me ask you a question. Is weight loss really that hard? Like you could throw a stone anywhere in America and you can hit a place to work out and eating healthy is, everybody knows how to do it. 

It's just nobody wants to do it. Right? Like I can agree with that. And so what we have to do is we have to create an organization. We have to create this governance around metrics so that we are intentionally moving ourselves forward every day, and that we are moving in the right direction. That is just something that just requires a lot of hard work, honestly. 

Nick Reddin: When all is said and done, that's the whole point of data, right? That's why we want, I mean, some companies are looking at it to find other sources of revenue. I mean, they find a data stream of some sort and now they can create a product line around it because they recognize something that they hadn't seen before. And maybe it's that they've got a tremendous amount of data on a certain segment of the population just by happenstance. And now they can go take that and you can go resell it. And so when they look at the data, when they look at what they start to pull together and you start to drive them towards action, a lot of people to your point say they want action. And a lot of them don't really want action cause they don't really want to change. And at the highest levels of the company it can be even more difficult to get them to agree on change.

What or How Many Data Point Should Be on Your Dashboard?

Nick Reddin: So how do you, when it comes to the dashboards and the reports that come out, when you start with them, they've got their own and you probably look at them and then you look at, what do I need to break this down to? How many points, how many things should be on a decent dashboard? 

Jason Greer: Yeah, that's a good point. So, I had an opportunity to work for an earthquake insurance company, about three years ago and we've helped them redesign a process. And as a part of homework, we asked them one evening to as a team develop all of the metrics that they thought that they were going to need to put into a dashboard to make sure that this new claims process ran smoothly from here on out. And it came back the next day and they had 39 metrics on their sheet. It was total counts of claims and total count of quality issues and, and so, the way that I help them kind of bring that down to a manageable level and understand true action was with a simple question. It was, if you were to give me this chart, what action would you expect me to take? 

If the chart goes up, what actions am I going to take because of that? If the chart goes down, what actions would you expect me to take? And if you can't tell me the answer, then this isn't really actionable and it doesn't add value to what we're trying to do. We actually narrowed that 39 metrics down to three things that were really important. The great thing was that everybody was on board and everybody understood their business way better after that conversation because we weren't just grasping at straws saying, "What can we measure?" We were saying, "What should we measure?" instead. 

How Do You Find Clarity With Your Data?

Nick Reddin: Very good. So, I always tell people nothing provides clarity like a lack of options. But with data, the options are enormous. So what's the best way to get clarity and then the focus? 

Jason Greer: Honestly this is the method I use and it's quite simple. We start with a question that's really important. We start to use the insights that we think may be appropriate, and we start to see how that would, how those insights, what outcomes they would have here with your business. Then from there we understand what are those actions and what, and will those actions, drive our company in the right direction. I try to keep it because it's way too complicated, and I'll get lost if I tried to do anything else. I've really tried to narrow an organization down to just having a simple conversation that creating a meaningful use of their data in their organization. 

Are Companies Ready for Predictive Analytics?

Nick Reddin: With the artificial intelligence and the advent of almost a demand for predictive analytics are companies really ready for it? Do they even really understand what that means? Or is it still kind of a buzzword and they're really not clear on what could really be provided at this point in time with what we know with AI and predictive analytics? 

Jason Greer: You know, I would say every organization I go into, I can see hints where AI would be a great thing for them. But if you think back to that ladder that I had on the screen earlier, you have to climb the ladder to get to AI. And most of the organizations don't have the data in place. They don't have the historic information available. They don't understand the questions that they should be asking, so I believe AI is a very important thing. And I would say in the next five to ten years, it's going to revolutionize the way we do business. The organizations that I'm going into right now are just not mature enough usually on a data scale. They haven't walked the ladder where they've thought through what are the metrics, what actions do we really want to take with this data to ask the big questions that AI would answer for us. 

Nick Reddin: And one of the things I notice in our own working with clients around data as well, that they don't understand, they'll say, yeah, we've got some great historical data. But the problem with historical data is it's also got the historical bias is built in it, but it also can force you to repeat the past. And in my mind that's not always a great thing depending on what those biases were or the business might've completely changed. So if you have 10 years worth of data, your business 10 years ago now, today with the advent of the internet, I mean, let me 30 years ago, and now with the continued advent of technology, the way that the business runs today would be completely different. So it's almost like historical data may not be as great as people think. 

Jason Greer: Yeah. I would say an organization that manages their processes really well would know that, but most organizations don't. So an old example that somebody gave to me, a little bit crude, was a question somebody asked me. That was what is the average age of a diaper wearer?, And most people, when you ask that question, they'll say somewhere in the toddler age and in reality, it's actually age 42. And if you think about it, you may need them at the beginning of your life and the end of your life. And getting back to your point, our data may be so mixed up with bi-modal data that we don't use it correctly and it's not ready for an AI experience. 

Nick Reddin: Yeah, that's what I'm starting to notice more and more. And as we're meeting with companies and talking about data and their data strategies, they have a complete misconception about what's available. And as you've said a couple of times, it's not always about what we can do. It's what should we do and why should we do it and what are we really going to get out of it? What's really going to help the company move forward and really have a better understanding of their business at the end of the day? 

Jason Greer: Right. So let me give you one last example here. How are we doing on time? Are we doing well? Alright. So this is an example I use whenever I do presentations and I think hopefully it drives home the point of really condensing down to what's important. So what I'll do in a large presentation is I'll ask everybody to close their eyes and I'll ask them to spend some time doing meditation. I ask them to start focusing on their body and understanding where their limbs are at. And just starting to relax. And then I'll ask them the question of, tell me all the sensations that you feel with your right shoulder. And I want you to start cataloging those in your brain. So think about the pressure from whatever you're wearing, the heat, or the cool that you may be sensing from there. 

And most people can usually come up with four or five sensations there. And then I'll ask them to do the exact same thing with their left foot, so to speak. We do that for a couple of minutes and allow people to just sit and think and meditate on their body. Then I asked them the question, "So you've been sitting here for 40 minutes, and I would guess that not one time did you register any of those sensations that you just made yourself find within yourself." So the scientific piece behind all of this is the STEM of your brain is what's called the reticular activating system. And it's taking around 10,000 inputs every second and condensing it down to about three things that you should be paying attention to. And the same thing goes for data and metrics. There are 10,000 things you could worry about inside of an organization, but in reality, most of that stuff is noise and it's not important. The pressure or the temperature that you felt on your shoulder, your body for that first 40 minutes, so this isn't important and we're not going to worry about it. But if we go in and we start deciding, we're going to measure everything and look at everything we are now overriding what's truly important to this business and missing opportunities to see the really important things that may be going on around you. 

What Should I Look for in a Dashboard Tool?

Nick Reddin: That's an excellent analogy and a great point as well. So we're going to have our Q&A session. It looks like a couple of questions that have come in so far. If anybody else has a question, feel free to go ahead and put that in there and we'll try to answer while we're here. So she's putting the first one up on my screen so I can see it. Alright, so Jason, it says we're using Excel for reporting. I want a more sophisticated tool. What things should I look for in a tool? 

Jason Greer: That's a great question. I would say that, you are going to want to look for opportunities to bring multiple sources of data together. You're going to want something that would allow you to bring those sources together and have a repeatable process that you can gather that data and bring it into that tool quickly and efficiently and automated overnight so you don't have to worry about it. There's nothing worse than seeing a dashboard that is three months old because nobody wants to click the button to revamp the data. So at the most basic level, that is the step, I would say as above Excel. Honestly, it's a big step, but it's very important. 

Nick Reddin: So with that step, and I'm just kind of thinking through this with you because I totally agree and this is something that at ATC we do for customers, but one thing we've noticed is that it does take a level of skill and insight in order to create a kind of what we would call a unified dashboard. It seems to be more difficult for companies to do it themselves without some outside help. Would you say that's pretty fair to say? 

Jason Greer: Yeah. You certainly, when we start jumping to bringing multiple systems together, we hit a couple of problems. Let me use an example. So let's imagine that we've got an accounting system and inventory system and CRM system and somewhere in those systems we've captured the gender of our clients. I kind of doubt we're going to do that in those three systems. We'll use an example in one system we've captured is male and female. In one system we've captured is M and F and on one system we've captured as a zero and one. Now we've captured gender in all three places, but in reality, there is no way for that data to talk. I see this a lot with client types. A salesperson looks at a client type way different than somebody on the inventory system does. Right? And so we are not meshing that data together, and so it's a bit of a hurdle and it takes some people that have an understanding of data modeling and ETL concept to really bring that all together. 

Nick Reddin: Yeah, no, that makes sense. Another question is, "I look at a number of dashboards each week for multiple departments." I think we just talked about that. "So is there a way to have one dashboard? Is that what you would recommend?" I think we already answered that. But for somebody that's got multiple departments, should there be a different dashboard for each department? Should the President have a different view than the VP, Director, and the Manager? 

Jason Greer: So we used to use a three tier approach a lot of times. We'd have more of an executive view. And we may have two or three dashboards, maybe two or three executive dashboards that they would look at. And by an executive dashboard I mean a single page where they could really big into the PMLs if they wanted to. They could go dig into the inventory pieces if they wanted to. A step below that is we would start to build a few more charts and graphs and perhaps maybe some tables they could go into. And then the third level is what we called the "sandbox" level a lot of times. And those were the power users who could really dig into and see all the data and the tables how it was interacting with each other. So they could click on, "I want to see all the sales of the Northeast" and they would see all the data fields that reflected that data for sales in the Northeast. 

Nick Reddin: it is good then to be able to pull from multiple systems to ultimately get a real good picture of what's taking place and to be able to drill down to some degree? 

Jason Greer: Yeah. That is where the maturity comes in and if we're going to answer really hard problems, you cannot just look at one slice of your business to answer really hard problems. You're going to have to bring in multiple sources of data and multiple pieces and understanding of your business to get to the really hard solutions. Yeah, no, 

Nick Reddin: That makes complete sense. Let me check here to see if we have any other questions coming in. I think that's it. Jason, I want to thank you again for being here. And just as a recap, he's the owner and a results strategist for Higher Standard Consulting. They are a wonderful practice and they do a great job. At ATC, we specialize in business intelligence and dashboarding. Jason is a great partner to us and we are to him. And I want to thank everybody for attending today. I hope you found this valuable and I would encourage you to keep up with our other webinars that we do on a monthly basis. The next one will also be about data and analytics, but they will be from a much different point of view. I think it will be something that you'd really enjoy and get something out of. So thank you very much for attending. And I encourage you, look at our website, follow us on LinkedIn, Twitter, Facebook, all the social channels that are out there and have a great day. 

Next Steps?

Just because the webinar is over, it doesn't mean your journey is. In fact, this may be just the beginning of your travel toward understanding data intelligence and predictive analytics.

Luckily, you have a few options to choose from. You can get hold of Nick Reddin and ATC directly here, or you can tune in to our upcoming webinars, which also focus a lot of data, but with great variances so you get to see a collective picture of the entire technology.

Upcoming Webinars Include:

November 21: How to Build a High Performing Analytics Team

December 5: How to Accelerate Digital Transformation

We encourage you to indulge in both to gain the best outcome from this experience. We hope you join us next time. 

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