Take 3, Scene 17: The Value of Data Analytics

Dan Greene and Adi Chikara join us for this episode of Take 3 to discuss the value of Data Analytics for making sense of the massive amounts of data that your business is already collecting.

Data Analytics is the process of analyzing and sorting business data to derive value and enhance productivity. Analyzing data includes a process of designing, building, and architecting models for data that will give insights into the business and predict outcomes for the future. These models can also help businesses identify actions needed to monetize their data.

Listen to the Episode

Episode Highlights

  • Dan and Adi address the problem most companies face – massive data backlogs – and why this continually happens
  • We talk about the benefits and the value that this data can bring to a business, and offer suggestions for companies just starting to make sense of it
  • Dan and Adi look to the future of Big Data and what role data analytics will play in it

About the Guests

Dan Greene is the Director of Architecture at 3Pillar Global. Dan has over 18 years of software design and development experience, along with software and product architecture experience in areas including eCommerce, B2B integration, Big Data, and Cloud Computing.

Adi Chikara is a Client Partner at 3Pillar Global. He is responsible for the health and growth of strategic 3Pillar client relationships, and helps companies derive value out of new or existing revenue-impacting digital products.

Read the Transcription

Julia Slattery: Let’s start with the basics, what do you mean when you say “data analytics?”

Dan Greene: For most companies, data analytics is taking the information that they already have or are collecting and discovering insight from it. That can be anything from marketing trends to customer behavior to even human internal human resource data, to find some additional value above and beyond the data itself.

Adi Chikara: As Dan said, most of this data already exists with these companies, and getting value out of it is really what it comes down to.

Julia Slattery: Okay, so like you said, most businesses seem to have a wealth of data stored up, but they don’t know what to do with it. Where do you think this data backlog comes from?

Dan Greene: Well, in today’s day and age, with the advent of everything around the Internet of Things or social media or what have you, there’s really a never-ending source of data that comes to you about your product or about your company or about people. So the challenge is taking this giant depth of information and finding the value on it before you’re crushed by the weight of all of the data you have. It’s very intimidating, because data is endless. People talk about storage being cheap, but data is endless.

So you need to find the value out of the data you have and all of the data coming in and get rid of the big pile behind you. I’ve mentioned before a term called data hoarding, where that concept of you don’t always want to save all of this data you have in your industry at your fingertips forever. It’s like saving a newspaper from 1987 because, “One of these days, I’m gonna read it.” It’s very similar, and as I mentioned a few minutes ago, the data’s endless. Find the value you can get out of it and then put the data in a storage or an archive where perhaps you might go after it later, but be willing to just get rid of it, because the value of a piece of data is typically proportional to its age.

Adi Chikara: As Dan was mentioning, when you get to an enterprise level and you get to a certain size of a company, just to maintain your operation system and your product, you have to use tools and technologies to scale it. As soon as you do that, most of these tools, by default, start collecting a large amount of data.

It’s as simple as Google Analytics on your site collecting a huge amount of data to being as complex as your TV box data provider or cable provider collecting a large amount of data. Your mobile phones are collecting a large amount of data. The information gathering – unless if you’re looking for something very specific – is not that hard. It’s the, “Don’t be a data hoarder, and actually get value out of it quickly,” that’s the hard part.

Julia Slattery: How does data analytics help companies not only sift through all of this stored data that they have, but actually start to make sense of it and use it?

Adi Chikara: So, to begin with, it really comes down to what is important to you. What really are you trying to figure out? What are the real questions, the key metrics that you really want help with? Once you know what that is, then you can start looking into what part of the data you really want to be looking at, and what is the other part you don’t really need right now. Once you’ve figured that part out, you start cleaning up your data to being getting that answer you want.

Once you have your cleaned-up data, it’s really a mechanism of running some of the tools available to get that insight, get that knowledge you need, and in some cases, running it through a prediction model to get some future forecasting that you need. So all of it really comes down to, “What are the questions you’re trying to answer?” And then, based on that, taking that data in and figuring out the answers it gives.

Dan Greene: From another perspective, 15-20 years ago, you would run batch reporting every night or at the end of the week or at the end of the month to chew the data that had come in. I’ve spent a few years in e-Commerce, and doing things that way allowed companies to determine what to put on sale and what not. With the advance of processing and the increase in volume of data coming in, you can do this a lot more real-time and store just the results of the analysis, as opposed to the raw data itself. That way it’s typically orders of magnitude smaller, and it’s already been honed down to the beginnings of the insight you’re looking for. That allows you to have a little bit of flexibility if you want to additionally analyze it but not have that overwhelming burden.

It’s interesting that everything old is new again in terms of analysis of data, but because of the massive increase of computing power, we’re getting closer and closer to real-time in terms of what’s happening. But as Adi was mentioning, you get into predictive analytics of what’s going to happen and, again, the increase in computing power and cloud computing, where you can scale up-to proportional to the load of data, and it allows you to predict in real-time, which is really impressive nowadays.

Julia Slattery: So what are some of the benefits of understanding this data?

Adi Chikara: Well, number one, you get higher top-line revenue. If you can understand your data better, that means you can make your business decisions better. You can run your business more efficiently. Businesses that make decisions based on the data generally are more successful versus just going with your gut, and that alone has enough value for you to be looking into it.

Dan Greene: I would add onto that that there’s knowing what your customers are actually doing. Most people feel that they have a thorough understanding, “I know my customer.” Or they assume, “My customers want X, Y or Z.” When you start measuring and analyzing end user behavior, you can determine what they truly want and deliver to your customers what they need, not what you think they need. Combining data and gut together will always be a more realistic and more profitable solution for a company’s products.

Julia Slattery: How can a company put a value on the data that they have?

Dan Greene: That’s a highly complicated question. It’s a very good question, but it’s a highly complicated question. The value of it is going to be on a company-by-company basis, but you know the market. You know the industry. You know the types of things you’re looking for, so my recommendation is to just get to work on it. Try to find an insight, and then move forward. As I mentioned, the value of data declines rapidly over time for most use cases, so get what you can out of it. And then, if you think of another way, get some more out of it.

I wouldn’t stop and wait to have the giant big picture of, “I know all my algorithms; I know all the analysis I want to do,” before you get started. Just start getting as much value that you get out of this data that you’re already collecting, and you’ll get a lot more. You’ll start getting benefits and insights, and those insights will likely drive additional analysis or additional questions that you could then probe the data you have for.

Adi Chikara: The other way to look at it is backtracking it from where your business is right now, in what state it is. If you’re looking to grow your customer base, then your value for data is getting more customers or increasing the satisfaction of the customers. If the focus of your business at that point is to be more efficient, then your data can help you do that.

So, the value of the data is actually tied back into what your business is trying to do and where it is in its life cycle. A very mature company – saturated or near to saturation in market share – might want to focus on increasing in customer satisfaction and increasing the value it provides to the customer, while a new startup would probably want to focus more on how to get new customers. So really, the value is tied back to what you do with it.

Dan Greene: Adi brings up a really great point. What I’ve seen in the past is companies starting with the data and working out, which gives you an outbound wedge, so you can really go in any direction. So it’s very hard to actually deliver success versus starting at the value you’re looking for and then looking backwards to the data you want. It a much more targeted and efficient strike, if you will, to provide value.

Adi Chikara: Focus and execution.

Julia Slattery: So, big data became one of the most talked-about topics in the last year. What do you think the future of data looks like, and how does analytics play a part in it?

Adi Chikara: Well, the future of big data is what Dan uses: Fast data. The amount of data will keep growing. That’s not the problem. The problem is: How quickly can you start analyzing and, even before storing it, analyze it first, then store it in a fashion that — in near real-time  you’re getting the information you need. And that’s really the future. Real-time decision-making is what makes companies better. And at the end, data analytics allows you to do that. It’s a core part of it, and it just fits so nicely.

Dan Greene: I would say the beginning of the end of the data link construct. The data link construct is: I’m taking everything I have, and I’m pouring it into a storage mechanism to find value later. It’s very much like a lake. The river’s flowing and just fills it up, and I’m going to mine this. I’m going to pan for gold in this lake. In the history of the world, how much gold was found in a lake versus found in the rivers of the incoming water? The analogy is pretty solid on that front. You’re better off looking in the flowing river of data for gold than this giant pile of very, very expensive data. As I said, it’s endless amounts of water coming into this lake, so you always want to make sure that you are intelligent in your storage and your retention policies.

Adi mentions fast data. That’s an area that I’m keen on. There are more and more technologies Spark, Storm  that are taking advantage of this space. Cloud computing is another. I think hybrid cloud computings are going to be pretty popular in this space. People are going to have the data that they want to process internally. I think they’ll find the mechanisms to anonymize it if it’s sensitive data, spike out to a public cloud provider, be it AWS or Azure or Google, to do the analysis via an elastic bank of resources, get their answers and then shut that down. I think that there’ll be a lot of play in the hybrid analytics space over the next year.

Adi Chikara: Completely agree with that.

Dan Greene: It’s a very exciting time. We’ve all been talking a lot about this advance in technology. Data is business nowadays. Every company is a data company in the end, and if you think of some of the massive collections of data that people are taking part in, you can really gain a lot of insights. It’s an exciting time to be in this space.

Adi Chikara: And if you look at just the last five years, the companies that have really come up and really succeed are data-driven companies. Even though some of them  like the likes of Uber and Airbnb  are operations-driven companies, they are successful because they are able to utilize their data in a fashion that allows them to operate. There are companies that are purely data, like Snapchat and Twitter and all of those social media platforms, which the entire premise of that is a data-driven business. So you really are starting to move to a scenario where data-driven business is at the top of the industry right now in any given industry.

Dan Greene: Five years ago, every company realized that they were a software company. And coming up, everybody’s going to realize that they are also a data company.

Julia Slattery

Julia Slattery

Marketing Content Specialist

Julia Slattery is a Marketing Content Specialist for 3Pillar Global in our Fairfax office. She manages content for 3Pillar’s web properties, hosts 3Pillar’s ‘Take 3’ podcast, and assists with the production and editing of a wide variety of audio, video, and written content. She holds a BA in Writing, Rhetoric, and Technical Communication from James Madison University.

Leave a Reply

Related Posts

Innovation Wars, with Scott Bales Scott Bales joins us on this episode of The Innovation Engine to dive into the concept of "innovation wars." Among the topics we'll discuss are what c...
Heather Combs to Moderate Panel at IES Conference 3Pillar's Heather Combs will moderate a panel at the IES Women in Sales Q2 Straight Talk Panel Discussion on April 13th in Vienna, Virginia. The panel...
Jessica Hall and Paul Axente Present at UX Talks On March 14, 2017, 3Pillar's Jessica Hall and Paul Axente presented at the UX Talks event in Cluj-Napoca, Romania. UX Talks centered on how UX shapes ...
Automation Testing: Excel to SQL Query Creation Objective We're looking to automate regression testing data preparation by populating data for different scenarios from Excel spreadsheets to a datab...
Take 3, Scene 21: Building a Serverless Architecture Derek Tiffany and Huagen Peng join us for this episode of Take 3 to talk us through the process of building a serverless architecture, and why this ca...

Free product development tips delivered right to your inbox