Tech Unknown | Episode 5 | Season 2

Featuring guests Carla Gentry, Iver van de Zand, and Timo Elliott with host Tamara McCleary

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Your organization is sitting on buried treasure. There are precious insights to be extracted from organizational data and used to guide your business to new levels of profitability. In this episode of Tech Unknown, our guests explore how you can find the buried treasure, extract and refine it, and put it to work for your business.

Businesses worldwide are creating and storing massive amounts of data, more than at any point in history. How much data? By 2025, we’re looking at 175 zettabytes of data. To put that in context: If you burned those files to DVDs, you’d have a stack that reached all the way to the moon.

And back.

Twelve times.

This business data is one of the most valuable assets for a modern corporation. It holds the information businesses need to make smart investments, develop new lines of business, increase efficiency, and ultimately drive more revenue. 

If simply having the data were enough to realize these results, every business would be going like gangbusters. But the value doesn’t come in having the data; it’s in using it, analyzing it, extracting insight, and using those insights to guide the business.

If you’re still relying on spreadsheets and manual processes to analyze your corporate data, you’re likely missing out on much of the potential. There’s simply too much data, in too many streams, updated too quickly, for humans to keep up.

That’s where intelligent cloud-based analytics can help. The processing power of the cloud can help with every part of the process: data collection, sanitization, aggregation, analysis, and reporting.

This episode, we’re taking you on a treasure hunt to find the gems hidden in your mountains of business data. Our experts explain what intelligent analytics means, share how businesses can use it, and offer inspiring success stories from leading organizations.

Listen to Learn:

• How businesses can use machine learning and AI to analyze data
• The three crucial questions you can answer with predictive analytics
• How analytics improves outcomes across the organization
• How custom dashboards can make reporting quick and easy

Want to learn more about SAP Analytics? Connect with an expert today.

About Our Guests:

Carla Gentry is a data scientist with over two decades of experience in predictive models, algorithms, and data structure as they relate to driving business insight. She has consulted for Fortune 100 companies and is currently the resident “Data Nerd” at the University of Central Florida.

“Look at your data. Don’t wait two, three, four months to look at that data because now it’s hindsight. Look at that data daily or weekly if you can.” –Carla Gentry

Iver van de Zand is the vice president of solution management and product strategy at SAP. He is the author of Passionate on Analytics and blogs at ivervandezand.com.

“Bringing analytics into the cloud allows you to scale your analytics towards the scale of your business.” –Iver van de Zand

Timo Elliott is the vice president, global innovation evangelist at SAP. He has spent over 30 years presenting to business and IT audiences in over 58 different countries, talking about digital transformation, AI, analytics, and the future of digital marketing. He also blogs at timoelliot.com.

“Analytics really is core to every aspect of business. Wherever you have a process, or a customer experience, or employee experience, the very first thing you need to do is be able to measure it. Without being able to measure it, you can’t analyze it and you can’t optimize it.” –Timo Elliot

Did you miss our last episode?

Check out our previous episode with guests Lisa Anderson, Tim Crawford, Eric Kavanagh, and Tom Roberts: “Intelligent ERP: The Foundation of Digital Evolution.” Click here to listen.

Episode 5 Transcript:

Tamara: Welcome to Tech Unknown, a podcast to prepare your organization for the tech-centered future of business. I’m Tamara McCleary, CEO of Thulium.

Our big umbrella topic this season is data. We’re digging into how sharing data across the organization can increase efficiency, reduce costs, and improve the customer experience.

This episode, we’re going to talk about the process of turning data into insight. Raw data by itself isn’t yet an asset to your business. The value comes from what’s hiding inside the data: The trends you can use to build predictive models and guide your business to greater profitability.

Think about it this way: Imagine that you’re an old-timey pirate, sailing the Seven Seas…

And you have a map that leads to an island full of buried treasure. That’s your data.

Parrot: Awk! Pieces of eight!

Tamara: Exactly. But when you get to the island, you can see that it’s MASSIVE… you can’t even see the other side from where you’re standing! 

And your map doesn’t say where the treasure’s buried… there’s just a big X marked across the whole island.

Now, you could just start digging and hope for the best… a total shot in the dark! But, that’s what most businesses are doing with their data.

So in theory, you have this enormous treasure at your fingertips…

But really, you can’t find your doubloons hidden in all that dirt.

When we talk about data as a business asset, this is the challenge. We all know there’s value in the data, but how do we find it and extract it?  

The answer is through cloud-based, intelligent analytics.

Now, analyzing data is nothing new – but analyzing massive amounts of data quickly for real-time insight is new. Revolutionary, even. Let’s dig into why we need the processing power of the cloud for data analytics at scale.

Iver van de Zand: Hey, my name is Iver van de Zand. I’m the vice president of product strategy for augmented BI with SAP.

If we look today how the worldwide volume of data is evolving, then that looks as follows. Today, worldwide, we have, more or less, 50 zettabytes of data, which is expected to grow to 175 zettabytes in 2025. If you stored that on DVDs, that brings you 23 times to the moon. 

Companies today are only able to look at the tip of the iceberg of that data, yeah? Because simple business intelligence today with on-premises users doesn’t allow to even scan and analyze that amount of data. Bringing this all to the cloud means that we can use new technologies, automated insights, augmented insights to start understanding these enormous amounts of data. That can only be done through the cloud.

A few more reasons going to the cloud is all about using analytics… a cloud allows you a way bigger and a larger variety of accessing data sources. But also, if you manage a platform around analytics through the cloud, you have way lower costs in terms of manageability, yeah? Because it’s centrally managed for you. That means together, that if you’re looking at the real potential of bringing analytics to the cloud, then that answers a scalability question. Bringing analytics into the cloud allows you to scale your analytics towards the scale of your business.

Tamara: So you have zettabytes of data to process, and now you have the storage and processing power that you need to do it. But you need one more piece of technology to eliminate the final bottleneck to business intelligence: You. Well, not you personally, but human beings. If your team is manually processing data, or your data is siloed between departments, you’re holding up the computers! Here’s Iver again on why machine learning and AI are a crucial part of the equation.

Iver: The amount of data is increasing that rapidly that we must come with machine learning-driven algorithms that help us find the correct insights at the correct moment. You could also see this as making things smart so what we massively are doing, incorporating in our software today are, for example, forecasting and correlation algorithms. Forecasting algorithms automatically start forecasting for you through time, whatever metric you have. Correlation algorithms are even applied even more. Correlation algorithms tell you what perspective, what attribute contributes to a certain metric the most.

To make it very tangible, imagine your margin is growing with 2%; you want to know what element contributed to that? What caused that increase of 2%? Was it the product line? Was it the time of the year? Was it a region? Was it the sales owner? So that’s correlation really helping you with machine learning and artificial intelligence finding the correct insight the moment you need that. That, for me, represents the role of artificial intelligence in modern analytics.

Tamara: So all we have to do is connect our data streams to a cloud-based analytics platform, and we can all head home for the day?!  Well, in that case, I welcome our robot overlords! Uh, sorry – that was entirely in jest for my fellow science fiction enthusiasts. The best results are clearly achieved when we pair human intelligence with artificial intelligence…Here’s Carla Gentry, data scientist at Analytical Solution, to tell us why.

Carla Gentry: It’s not about just collecting data, it’s about gleaning insights from that data. So, I would say the future of analytics has to be that we finally, you know, stop talking about Perl and Python and R and all this other programming crap, and start talking about the results that we actually get from that. Because all of those things that I just mentioned are just a tool that is in the data scientist’s arsenal of weapons. They have statistics, that is a tool, they have programming as a tool. SQL is just a tool. The visualizations and all are a tool, but they’re tools that are needed to be able to show your boss or your client or, you know, the C-suite that this is working.

So, let your data tell a story. You know, “this is the information, where we got it, what we did to it, how we cleansed it.” Be transparent. Don’t start, “Oh, well, there was missing data, so we just auto-populated some stuff in there.” Well, you just skewed the entire dataset and biased it based on your personal biases because you assumed what the customer meant, and you really don’t have a clue. So, letting the data speak for itself is how we’re gonna get integrity and transparency.

We have to have clean data to be able to do machine learning. 

Tamara: So we start with clean data. We take it to the cloud for processing and analytics. Okay, then how do we identify the buried treasure? Iver says there are three types of questions our data analytics can answer.

Iver: If I look at predictive analytics for my area, meaning providing customers with 360-degree insight in how their business is running, then you typically see three areas. You have an area that answers the questions, “hey, how is that process going? What is the level of that KPI? How am I doing there?” This is typically covered by business intelligence, whereas you also have an area is on, “hey, what am I planning to do? What was my objective to have that KPI and be on?” Which is typically the planning area. And to answer your question, answering questions as “what could happen? What happens if I change that?” That area of insight is typically covered by predictive analytics. So predictive analytics for me is all about looking into the future, but primarily also simulating, “what can happen with this product line if I change the supplier?” for example.

Tamara: As Iver says, there are three types of questions that intelligent analytics can answer: Performance, planning, and prediction. These “three p’s” combined can generate amazing insights for any type of organization. For example….

Iver: This is a company that produces tires for cars in Europe, and of course, they deliver those tires today with modern technology embedded in those tires’ sensors. And those sensors measure the quality of the tire 24 hours a day, 365 days per year. The data that they take out of that is, of course, very useful for their suppliers and the support organizations to understand because they can predict when a tire on a truck is going to break, so they can automatically serve the truck. What I really like about this example is that you would expect that company to provide that data on the tires to those suppliers and assistance to these services companies. But they didn’t. They sell the data, and this is a very nice example of not only solving a business problem, even stronger: monetizing a business problem.

Tamara: When you fully leverage the value of your data as a business asset, you can actually sell that asset as a product! As in Iver’s example, even a tire company can be in the information business. 

But predictive analytics can do more than solve business problems. It can even do more than create new business models! Smart data analytics can literally save lives.

Iver: So I think everybody’s with me if I say that data is more or less becoming the blood of our economy today, yeah? The blood that drives the heartbeat of our economy today. And a nice example is in a hospital in London where they research patients with heart diseases, using sensor technologies that are put into the bodies of these patients. And using modern artificial intelligence, today, this hospital is capable of predicting a heart failure with a patient, and that is massive news. So by constantly monitoring heart rhythm and blood quality through sensors put into the bodies of those patients, the hospital can predict – when this patient makes a certain move or has a too intense pattern in his life – that they can warn the patient that a heart failure is going to come. And I think there is no better example to prove the use and the value of artificial intelligence than this one.

Tamara: That’s quite a heartwarming story, don’t you think?

AI voice: Bad pun detected. Predictive analysis shows a 60% possibility of future attempts at wordplay. Recommendation: Early termination of episode.

Tamara: Okay, so AI may be great at predicting the future of business, but it still doesn’t have much of a sense of humor.

AI voice: That makes two of us.

Tamara: Ouch! Okay, fine. Maybe intelligent analytics can quantify human behavior. In fact, Carla has a great example of how you can use predictive analytics to take some of the guesswork out of hiring new talent.

Carla: Well, like for turnover within a particular company, you can look at the habits and the personalities of the particular person in that position. So, we’ll take, for example, like a high turnover position like sales or a cashier. So, say you were a big-box industry, and you have 120% turnover, and you’re spending millions and millions of dollars to train all of these people, and what you really need to do is expand your employee lifecycle. So, you wanna look and see, you know, the employees that you have there, what are their personalities, and you can do that through an assessment. You can do that through surveys or interviews or however you wanna do it. So now, you’ve collected this information. It’s like a test and training set. So, you’ve got the employees that are happy, what are their characteristics? I’m talking about personality characteristics. And look at the employees that are unhappy, see what their characteristics are.

And then when you go to hire people, you give them that same survey, that same assessment, that same, you know, questionnaire and ask them and see what they answer. So, the person that you hire that has the personality more likely to, you know, stay longer and increase that employee, you know, lifecycle is gonna have a personality like this. So, then at the very end, you’ve got like red light, green light, yellow light. The green lights are the people that have similar personalities to the successful employees. So, of course, they get an immediate interview. The people in yellow are the ones that, you know, they have some characteristics, but you’re probably gonna have to have a more formal interview to be able to get any insight. And then the third case in red, you know, and this is all a dashboard kind of thing for the employed or the C-suite or the HR person to be able to look at and make decisions. And it’s a visual kind of cue, we’re, “Okay, red light, we don’t hire them. We’re not even gonna give them a callback.”

So, that is a practical use of being able to predict your employee lifecycle and your turnover rate, based on collecting unstructured data from, you know, surveys or assessments or questionnaires within your own company and then using that same thing for your external potential employees to try to predict whether they would actually work out well in that position or not.

Tamara: Intelligent analytics can keep a heart beating, a tire from blowing out, and a new employee from burning out. But let’s not forget perhaps the single most important source of data we should be analyzing and optimizing for – your customers. Carla says that data is “the voice of the customer,” and she’s not wrong!

Carla: Well, anytime your customer is talking about your brand, that’s the voice. And whether that be social media, whether that be a Google review or a Yelp review or a survey or Net Promoter score, you know, like, how are we doing? Would you recommend us to a friend? Emails that you’ve sent out, did they respond? If they did, how long it took. Any of that information is the voice of the customer because a lot of times when we’re looking at data, we’re thinking PoS data or syndicated data, you know, data that we would get from, you know, like, we would purchase data, but there’s so many other forms of data that’s for free. I mean, we could go on Twitter and use hashtags and see what the customers say about us, and that didn’t cost us anything. We didn’t even have to pay a Twitter ad for that. We just had a person, maybe an influencer, ask a question about a brand. Now we can collect all of that information about how the customers feel about us. So, that’s the thing when I’m talking to people about collecting data. There’s many, many resources out there. Just because you don’t feel that you have a robust dataset, there’s more information out there, you just have to look for it. And a lot of times, it’s free.

Look at your data. Don’t wait two, three, four months to look at that data because now it’s hindsight. Look at that data daily or weekly, if you potentially can, and be able to rectify those issues. That’s why we have, like, knock meetings, these emergency, you know, meetings, “Oh, God, our website is down,” because they’re losing money. When something’s wrong with that website, they respond to it immediately. So, if something is wrong with their service, they need to respond just as immediately. Because, yeah, the website’s your money. That’s your PoS data, your bread and butter, but your customer, ultimately, is that bread and butter. That’s the person you need to respect and take care of. So, if they’ve taken the time to respond to you, respond back to them. Find out what the problem is. That’s free data.

Tamara: As you can see, intelligent analytics is all about finding the value in your data, no matter what business you’re in, and in every department in that business. If we go back to the treasure map analogy from earlier… 

Avast, it’s good to be back on the open sea, me hearties. But if you’ll recall, we had a map that led us to Treasure Island, but it just had a single big X over the entire landmass! The value was there, but we couldn’t get to it without a lot of time-consuming labor.

Intelligent analytics can put the X exactly where the treasure is. More than that, it can call in a swarm of drones with shovels…

Equipped with SONAR….

To pull out those priceless insights and bring them right aboard my boat!

Robot voice: Here be your treasure, Captain McCleary. Avast me hearties, yo ho.

Tamara: Here’s one big difference between a treasure map and an analytics dashboard, though: a custom dashboard can not only show you these insights, it can also update the most important metrics for you in real time. With a custom analytics dashboard, you have the data right at your fingertips.

Here’s Iver again to explain how custom dashboards work for the SAP Analytics Cloud.

Iver: As part of SAP Analytics Cloud, Analytics Designer allows you to build custom-designed dashboards. And that is really what our customers use Analytics Designer as part of SAP Analytics Cloud for. These examples of custom-designed dashboards are, for example, we have a number of car manufacturing companies in Germany that create these custom-designed dashboards that are put into the factory on the ceiling where everybody in the factory can in real-time follow the number of cars they have produced in a day, the number of failures, the outage of the system. So, this is really used to provide all the employees with constant insights on how strong the performance in their factory is on that day. But it also triggers as a kind of a, I would say, gamification mechanism, teaching people to do better the next time. So, this is one of the ways that Analytics Designer is used with our customers.

Tamara: We’ve covered a lot of ground in this episode – from hospitals to highways to the Spanish Main – but it’s time to wrap it all up. In season one of Tech Unknown, I interviewed Timo Elliott, innovation evangelist for SAP. Timo told me a great story about analytics and football fans. It really illustrated how cloud-based analytics can help solve for any problem, including optimizing how much fun you’ll have at a San Francisco 49ers game!

I can almost hear that conversation now… 

Timo Elliot: One of my favorite projects recently is we worked with the San Francisco 49ers. So, in their stadium, they really want to optimize people’s enjoyment of the game. And so, they’re collecting data from nine different systems in real time, so they can immediately spot anything from parking, to the food and beverage, to the weather problems, and really, in real time, act to make sure that whatever is happening is not damaging the overall customer experience. In fact, they even have big screens in what they call the Executive Huddle, so that, as you’re watching the game, they have these big screens with SAP Analytics Cloud bringing all of the data from the game in real time but also all of the historic information, so you can see how this game relates to the overall trend. So it’s directly helping fans enjoy the game more because they have more of the context.

So again, you know, analytics really is core to every aspect of business. Wherever you have a process, or a customer experience, or employee experience, the very first thing you need to do is be able to measure it. Without being able to measure it, you can’t analyze it and you can’t optimize it. So ultimately, analytics, I think, is the ultimate business process that we need to work on in today’s organizations.

Tamara: Analytics, Timo says, is the ultimate business process that we should be concerned with. It makes sense: Data is one of your business’ most valuable assets. The insights from your data can help measure your performance, help you plan for the future, and predict what’s next for your industry. 

Even more than that, the data itself can become part of your product offerings. Intelligent analysis can actually change the way you do business, whether it’s adding a new revenue stream or completely transforming your business model.

But you can’t get all that value from your data with a treasure map and a shovel. It takes intelligent analytics – cloud-based, real-time, augmented with artificial intelligence and machine learning – to unearth every bit of that buried treasure.

Thanks for listening to Tech Unknown, and thanks to my guests Carla Gentry, Iver van de Zandt, and Timo Elliott. Please subscribe on iTunes, Google Play, Stitcher or wherever you listen to podcasts.

I’m Tamara McCleary and until next time: Stay sharp, stay curious, and keep exploring the unknown.

To learn more about intelligent ERP, go to s4hanaopp.com. You can also find a transcript of this episode and more at digitalistmag.com. And make sure to subscribe wherever you listen to podcasts.

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