Intelligent Analytics: The Search For Hidden Treasure In Your Business Data

Posted On Apr 27, 2020 By admin With Comments Off

Tech Unknown | Episode 5 | Season 2 Featuring clients Carla Gentry, Iver van de Zand, and Timo Elliott with multitude Tamara McCleary

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Your organization is sitting on buried rich. There are precious penetrations to be extracted from organizational data and used to guide your business to brand-new levels of profitability. In this incident of Tech Unknown, our guests explore how you can find the buried wealth, obtain and refine it, and positioned it to work for your business.

Businesses worldwide are creating and storing massive amounts of data, more than at any point in autobiography. How much data? By 2025, we’re looking at 175 zettabytes of data. To articulate that in situation: If you burned those documents 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 resources for a modern corporation. It maintains the information enterprises need to meet smart investments, develop brand-new strands of business, increase efficiency, and eventually drive more receipt.

If simply having the data were enough to realize these results, every business would be going like gangbusters. But the appraise 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 procedures to analyze your corporate data, you’re likely missing out on much of its full potential. There’s simply too much data, in too many torrents, updated too quickly, for humans to keep up.

That’s where intelligent cloud-based analytics offers an opportunity to. The processing capability of the vapour can help with every part of the process: the collected data, sanitization, aggregation, analysis, and reporting.

This episode, we’re taking you on a treasure hunt to find the gems hiding in your elevations of business data. Our professionals explain what intelligent analytics signifies, share how businesses can use it, and offering inspiring success floors 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 aftermaths across the organization How custom-built dashboards can compile reporting quick and easy

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

About Our Guests: Carla Gentry

Carla Gentry is a data scientist with over two decades of know in predictive representations, 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

Iver van de Zand is the vice president of answer 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

Timo Elliott is the vice president, world-wide innovation missionary at SAP. He has depleted over 30 times presenting for enterprises 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 know-how, 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 bout?

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

Escapade 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, reducing costs, and be enhanced the customer experience.

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

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

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

Parrot: Awk! Sections of eight!

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

And your planned doesn’t say where the treasure’s interred … there’s merely a big X recognized 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 obtain it?

The answer is through cloud-based, smart analytics.

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

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

If we look today how the worldwide volume of data is evolving, then that lookings 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 placed that on DVDs, that brought you 23 seasons 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 customers doesn’t allow to even scan and analyze that quantity of data. Bringing this all to the cloud means that we can use new technologies, automated revelations, augmented penetrations to start understanding these enormous amounts of data. That can only be done through the cloud.

A few more rationales going to the cloud is all about abusing analytics … a mas shall be allowed a method bigger and a larger variety of accessing data sources. But likewise, if you oversee a scaffold 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 possible of bringing analytics to the cloud, then that refutes a scalability question. Bringing analytics into the cloud allows you to scale your analytics towards the scale of your business.

Tamara: So “youve had” zettabytes of data to process, and “youve had” the storage and processing power that you need to do it. But you need one more piece of technology to eliminate the final blockage to business knowledge: You. Well , not you personally, but human being. If your squad is manually processing data, or your data is siloed between agencies, 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 sum of data is increasing that rapidly that we must come with machine learning-driven algorithms that help us find the compensate revelations at the remedy moment. You could also see this as originating things smart-alecky so what we massively are doing, incorporating in our software today are, for example, foreshadowing and correlation algorithms. Calculating algorithms automatically start calculating for you through time, whatever metric “youve had”. Correlation algorithms are even devoted even more. Correlation algorithms tell you what perspective, what facet contributes to a certain metric the most.

To make it very tangible, imagine your perimeter is growing with 2 %; you want to know what element contributed to that? What stimulated that increase further 2 %? Was it the product line? Was it the time of its first year? Was it a region? Was it the sales owner? So that’s linkage truly helping you with machine learning and artificial intelligence finding the compensate 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 brooks to a cloud-based analytics platform, and we can all head home for the working day ?! Well, in that case, I welcome our robot overlords! Uh, sorry- that was entirely in jest for my chap science fiction addicts. 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 really collecting data, it’s about reap revelations 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 programme turd, and start talking about the results that we actually get from that. Because all of those things that I just mentioned are just a implement 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 buyer or, you are well aware, 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 purged it .” Be translucent. Don’t start,” Oh, well, there was missing data, so we just auto-populated some stuff in there .” Well, you merely skewed the part dataset and biased it based on your personal biases because you premised what the customer meant, and you really don’t have a clue. So, causing the data speak for itself is how we’re gonna get integrity and transparency.

We have to have clean data are permitted to do machine learning.

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

Iver: If I look at predictive analytics for my area, symbolizing cater purchasers with 360 -degree insight in how their business is running, then it is usually read three provinces. You have an area that rebuttals the issues,” hey, how is that process leading? What is the level of that KPI? How am I doing there ?” This is typically covered by business intelligence, whereas you likewise have an area is on,” hey, what am I are projected to do? What was my objective to have that KPI and be on ?” Which is typically the planning expanse. And to answer your question, answering questions as” what could happen? What happened when I vary that ?” That sphere of revelation is commonly included as part of predictive analytics. So predictive analytics for me is all about looking into the future, but primarily likewise simulating,” what can happen with this product line if I modify potential suppliers ?” for example.

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

Iver: This is a company that produces tires for vehicles in Europe, and of course, they extradite those tires today with modern engineering embedded in those tires’ sensors. And those sensors set the quality of the tire 24 hours per day, 365 epoches per year. The data that they take out of that is, of course, extremely useful for their suppliers and the support organizations to understand because they can predict when a tire on a truck is going to break, in order to be allowed to automatically perform the truck. What I really like about this speciman 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 amply leverage the value of your data as a business asset, you can actually sell that asset as a commodity! As in Iver’s example, even a tire busines can be in the information business.

But predictive analytics can do more than solve business difficulties. It can even do more than create brand-new business sits! 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 neat example is in a hospital in London where they research cases with congestive heart failure, using sensor engineerings that are put into the bodies of these patients. And abusing modern artificial intelligence, today, this hospice is capable of predicting a heart failure with a patient, and that is big word. So by invariably monitoring nature tempo and blood excellence through sensors put into the bodies of those cases, the hospital can predict- when this patient makes a certain move or has a too intense decoration 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 spied. Predictive analysis evidences a 60% prospect of future struggles 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 concludes two of us.

Tamara: Ouch! Okay, punishment. Maybe intelligent analytics can quantify human action. 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 practices 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 “youve had” 120% turnover, and you’re expend millions and hundreds of thousands of dollars to train all of these parties, and what you really need to do is expand your employee lifecycle. So, you wanna look and hear, you know, the employees that you have there, what are their temperaments, and you can do that through an assessment. You can do that through inspections or interrogations or nonetheless you wanna do it. So now, you’ve accumulated this information. It’s like a test and training courses give. 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, encounter what their characteristics are.

And then when you go to hire people, you give them that same overlook, that same analysi, that same, you know, questionnaire and asking questions and meet what they answer. So, the person that you hire that has the personality more likely to, you are well aware, stay longer and increase that hire, 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 same personalities to the successful employees. So, of course, they get an immediate interview. The parties in yellow are the ones that, you are well aware, 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 are well aware, and this is all a dashboard kind of thing for the applied 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 regarding, you are well aware, cross-examine or evaluations or questionnaires within your own company and then expending that same thing for your external possible employees to try to predict whether they would actually work out well in that plight or not.

Tamara: Intelligent analytics can save a nature drumming, a tire from blowing out, and a brand-new hire from igniting out. But let’s not forget perhaps the single most important source of data we should be analyzing and optimizing for – your clients. Carla says that data is “the voice of the customer, ” and she’s not wrong!

Carla: Well, anytime your patron is talking about your brand, that’s the expression. And whether that be social media, whether that be a Google review or a Yelp review or a questionnaire or Net Promoter score, you are well aware, like, how are we doing? Would you recommend us to a friend? Emails that you’ve sent out, did they react? If they did, how long it took. Any of that information is the voice of the customer because a lot of meters when we’re looking at data, we’re thinking PoS data or syndicated data, you know, data that we are to be able get from, you are well aware, like, we would purchase data, but there’s so many other forms of data that’s for free. I signify, we could go on Twitter and use hashtags and picture what the customers say about us, and that didn’t cost us anything. We didn’t even get paid a Twitter ad for that. We merely had a person, maybe an influencer, ask a question about a label. Now we can collect all of that informed about how the customers feel about us. So, that’s the thing when I’m talking to beings about collecting data. There’s numerous, many resources out there. Time 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 days, 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, blow satisfies, these disaster, you know, gratifies,” Oh, God, our website is down ,” because they’re losing money. When something’s wrong with that website, they is submitted in response to it immediately. So, if something is wrong with their service, they need to respond just as immediately. Because, yeah, the website’s your fund. That’s your PoS data, your bread and butter, but your purchaser, ultimately, is that bread and butter. That’s the person you need to respect and take care of. So, if they’ve made the time to respond to you, answer back to them. Find out what their own problems is. That’s free data.

Tamara: As you can see, smart analytics is all about seeing 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 preceded us to Treasure Island, but it precisely had a single big X over the part landmass! The price was there, but we couldn’t get to it without a lot of time-consuming labor.

Intelligent analytics can make the X exactly where the rich is. More than that, it can call in a horde of hums with shovels…

Equipped with SONAR….

To pull out those priceless insights and generating them right aboard my barge!

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

Tamara: Here’s one big difference between a rich map and an analytics dashboard, though: a tradition dashboard can not only show you these insights, it can also update the most important metrics for you in real epoch. 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 purchasers 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 fellowships 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 manufactured in a daylight, the number of lacks, the outage of the system. So, this is really used to provide all the employees with constant revelations on how strong the performance in their plant is on that day. But it also provokes as a kind of a, I would say, gamification mechanism, doctrine 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 reported a lot of ground in this episode- from infirmaries 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, invention missionary for SAP. Timo told me a great story about analytics and football followers. It certainly represented how cloud-based analytics can help solve for any problem, including optimizing how much fun you’ll have at a San Francisco 49 ers tournament!

I can nearly hear that conversation now …

Timo Elliot: One of my favorite jobs recently is we worked with the San Francisco 49 ers. So, in their stadium, they really want to optimize people’s delight of video games. And so, they’re collecting data from nine different plans in real occasion, in order to be allowed to immediately spot anything from parking, to the food and liquor, to the weather difficulties, and genuinely, in real meter, act to make sure that whatever is happening is not damaging the overall client experience. In fact, they even have big screens in what they call the Executive Huddle, so that, as you’re watching the game, the government has these big screens with SAP Analytics Cloud bringing all of the data from the game in real term but too all of the historic information, so you can see how this activity relates to the overall trend. So it’s immediately curing love experience video games more because they have more of the context.

So again, you are well aware, analytics really is core to every aspect of business. Wherever you have a process, or a client event, or hire suffer, 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 eventually, analytics, I conclude, 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 utters impression: Data is one of your business’ most valuable resources. The penetrations from your data can help measure your performance, help you plan for the future, and foresee what’s next for your industry.

Even more than that, the data itself can become part of your product presents. Intelligent analysis can actually change the practice you do business, whether it’s adding a brand-new revenue stream or totally altering your business model.

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

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

I’m Tamara McCleary and until next time: Stay abrupt, keep puzzled, and restrain exploring the unknown.

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