Michał Grela (MG): Hi, my name is Michał and I’d like to welcome you very warmly to yet another episode of IT Leadership Insights by Future Processing. The possibilities of data is what are we going to cover today. While the world is becoming more and more interconnected, organizations, people, and last but not least, devices, are creating enormous amounts of data each day. So the question arises, do we take advantage of the data we collect? What potential does data provide us with? Can any strategic decisions be optimized by taking a look at this data, the data we already have in real-time? These are the fundamental issues we would like to try answering today during my conversation with Chris from Future Processing, my colleague. Thanks for joining me here today. Would you be so kind and tell us a few words about yourself?

Christoff Nykiel (CN): Thank you, Michal. Christoff Nykiel. I’m data solution service line manager at Future Processing, and I’m responsible for the team development and the services in the area of data.

MG: Wonderful, few questions about data then so let’s jump right to it. IBM in 2017, which was three years ago, already said that 90% of data was generated over the last two years alone. So imagine where we are today when it comes to this amounts of data we have available, but do we actually take advantage of it?

CN: Yeah, that’s a good question, an incredible number of data. Actually the data growth each year is in the exponential growth so you can imagine that what’s changed since this three years. And actually not all of the C-level managers are satisfied with how they process their data and draw conclusions out of them. Different sources say that about 30% of managers are not satisfied with the state of art in their companies.

MG: So what made people satisfied, or what was making people satisfied few years ago, now is not enough not to fall behind the competition. People are not satisfied with the level of use of data they have.

CN: Agreed, the things that we did with the data like five years ago, what makes us ahead of the competition, now it’s an absolute must in order not to fall behind. So the question is, what we need to do right now to be ahead of our competition, what we need to do to achieve the goals and strategy our company needs to follow.

MG: That’s definitely a very good question and when I was preparing to this conversation I came across this flow of how you develop as an organization with data. Where you start with data and then you try to derive some information out of this data and then this information gives you knowledge, but that’s not the last step because on top of it there’s this wisdom coming out of the previous steps. Do you observe that working with different companies?

CN: Yes, exactly. So most companies that we work with start with the point that usually they have already some amount of data, different depending on the industry they work with. But they have already the data, but the problem is that they don’t use it efficiently. So they are not building enough information as you said, out of the data they already have. So what we do is we are trying to help them to use this data to build the information, that’s the first step. And based on this information, we can go even further to make wiser decisions, better strategic steps for the companies and the wisdom, which builds out the real value in the business.

MG: So, that’s the flow of building data science around the data. But what are the actual benefits of implementing such data strategy, data science strategy within your business? Why should I bother doing that in the first place?

CN: Yeah, first of all, thanks to the proper analysis you can draw better conclusions. You can make wiser decisions to your business. You can possibly find some niche markets that you can target, and when targeting them, you can find new customers and new business basically. What else you can do? You can use the predictive analytics based on the data that you already have in order to predict some events in the future, which also allow you to be even more prepared for the future, build your strategy for the company. This is obviously dependent on the industry you work with, but in any production, when the machinery is involved for example, usually nowadays the machines are equipped with different measurements and these measurements build enormous amount of data you can work with and you can deliver some value. So the predictive maintenance can provide you the information that you can use to prevent the eventual losses that you can have if you are not taking care of the machinery and different parts of your production processes.

MG: I really like the fact that you mentioned examples that focus not only around cost cutting or cost optimization. Because of course you can, as I understand, optimize operations by cutting costs using the data you have. But I guess in achieving operational excellence in being innovative, it’s not about looking at cost cutting alone. It’s about building something actually innovative and doing this digital transformation and transforming the industry. And one of the trends I see now when it comes to data is the business intelligence solutions. What’s there for organizations? Why is that a sexy thing right now?

CN: Well yeah, it becomes more and more popular important over the last years. The problem that we see is that most of the data, actually different sources say 80, 90% of data that we produce every day, is unstructured. And usually that’s a problem for organizations, because you are not able to work efficiently with the data. And yes, you are not able to draw the information and then the knowledge out of unstructured data that efficiently that you could do with the data that has been already analyzed. So the business intelligence is the process when you analyze your data with the help from the data analyst and build systems like data warehousing or different reporting dashboards with plots, and different numbers that show where you are with your business, with your costs, with your revenue and with the products that you provide. So basically the business intelligence tools allow you to structure data from coming out from different sources. So you may be storing data in databases different, other files that provide you with the data, or even from the devices that you may have in your company, structure them appropriately and then use them for the reporting purposes and the main goal is to answer to your actual problems. So the first step would be to identify those problems that you currently have in your business. And with use of this technology these people, data analysts, business analysts, will try to provide you with the solution that is tailored for your needs so that you can find the answer to these problems in seconds, just looking on the dashboard. Not like in case that you have the unstructured data and you need to go deeply in the documents, in the databases, look for the information you need, call for example 10 people to provide you with the information you need to take this one little step for your business to achieve the strategic decision.

MG: Definitely, I guess good business intelligence solution is tailor made to each organization. And as every business is different, the solutions need to reflect that as well. And I really like the fact that it allows you to actually build this competitive advantage on top of your competition. Because if you have this access to real time live data that you can rely on right here right now for decision-making and your competition is not having this access, then you’re definitely ahead of them.

CN: Exactly. And more to that, the different services that the C-level managers believe that by 2022 about 80% of the decisions that need to be made will be based on real-time data as you said. So that’s why this business intelligence reporting connected with the real-time data flow will become more and more important over the coming years.

MG: What’s also a curious topic and was also very interconnected with data is, because when I think of data is we have to store them somewhere, which brings me to cloud. And how is that relevant here? How would cloud or cloud services affect harnessing data companies have?

CN: Good question. Maybe one step backwards. The same survey, you mentioned the same research by IBM three years ago, said that in 2012 there were like 2.5 billion internet users worldwide, while in different sources say that in 2019 there were like 4.1 billion already.

MG: Which is double, more than double.

CN: Much more basically. And you can imagine they are only the internet users alone, but the technology evolves. There are more and more internet connected devices that we need to cooperate with. So the amounts of data become enormous, as we already said. And the cloud comes into play, which enables us as the organizations to take advantage from the worldwide access to the data instantly. This is in comparison with the on-premises solutions gives much more benefit to the business, because the businesses may operate worldwide, taking advantage of this growing number of users every day. That’s why the cloud providers constantly work on the new services that follow this increasing number of users, this technology, which is evolving every day. And they answer, try to answer with their developed services on cloud to this challenges. So for the business it’s very important to look into what they offer in order to follow this revolution you may say, in the technology, not too far behind the competition, which already is there.

MG: Yeah, that’s very interesting. But another question that just crossed my mind is even more broader when it comes to context. You’re referring this level decision makers in your answers and I’m keen on understanding how would you convince a C-level decision maker to actually invest in, or to actually implement or somehow involve this data science or data engineers or data analysts in their products? Why should they consider it valuable and necessary and relevant? Where’s the value?

CN: Okay. So while business intelligence answers the problems that we have with the data that we collected, the data science answers with the proposal to look even deeper into the data we have. So by applying for example artificial intelligence, different machine learning models to our data, we can predict events. We can predict some non-obvious connections between the information that we already have. And thanks to that we can draw even better conclusions, which is obviously beneficial for our business and the changes that we need to make to it in order to evolve.

MG: Okay, and for that we need engineers and specialists in this area?

CN: Yes. So this is a bit different approach to the services that we already mentioned, the data analysts. We need to have data engineers that can let’s say play with the data that we have to experiment to apply different as I said machine learning models, for example. Or what’s becoming more and more important with the data growth that we observe to use the proper big data tools as well, to cope with the for example, IoT devices that we have connected to our systems. We govern this enormous amount of data that provides us certain information, and to take the advantage with the information these devices provide us, we need to cope with the big data discipline. So the data science engineers are required to cope with this problem.

MG: What would you say are the best or the biggest trends right now in the data industry or vertical? What are the two most important hottest topics right now?

CN: What I observe, this would be relevant for different industries, especially in the let’s say COVID pandemic, it is different chatbots and generally natural language processing disciplines. So basically when more and more people are online because they need to take care of their everyday matters through the online contact, the organizations may start to see that their staff is not that fluent with the responses as they used to be before COVID. So nowadays that technological trends are to supplement this. Let’s say our people, staff in the company with the solutions that can answer different percentage of the interest from our customers, classify the let’s say importance of this customer queries, so that we can use the time of our workers, of our employees in the best way, so that we can answer these queries that may provide us with the actual revenue. And the rest of them we’ll try to cope with automatically via for example, chatbots.

MG: It’s interesting how many trends right now are focusing around eliminating these mundane tasks, and freeing some time to spend on this most relevant and most important facts and tasks and I guess chatbots are a good example here. They free your time so you can focus on the actual important stuff, whereas they take the conversations on their shoulders. And what would be prediction for the future, say for next five years, where this data industry will go?

CN: So we can try to draw conclusions what happened like over the last five years. What has happened to the image processing, where the algorithms became even better than the human perception in analyzing the images and providing the output, let’s say with comparing two different images and producing the let’s say output, may say painting. It’s incredible how this technology rolled over the five last years, when we say that right now this artificial intelligence is even better than the human abilities and what I suspect may happen in the next five years, this very similar process may consider the text processing and voice. So basically in the next five years we can observe the chatbots or voice bots let’s say, replacing the need for the actual employee to discuss the matters that our customer has.

MG: So call the call center and have a conversation with a robot, not even being aware of the fact that…

CN: Exactly.

MG: … I’m not speaking with an actual person.

CN: Yes.

MG: Frankly speaking, that’s cool on one hand, but kind of creepy on the other hand.

CN: Yeah. As all the industry evolving, you may remember the previous industrial evolutions also replaced employees on different industrial aspects.

MG: Sure.

CN: So I believe the technology is tending towards this solutions. We may observe different models of the NLP, so natural language processing, which answer many of these problems today. So you can imagine what will happen over the next five years.

MG: Thanks. Thanks, Chris, for sharing that insights. Where, to some of this conversation, I would say that we covered that of course the majority of the industry has the data already, but not exactly everyone is making the right use of it, but they should be because it brings tangible benefits, not only in the form of cost optimization, but also building a competitive advantage on top of it. And things such as, or trends such as business intelligence or data science, big data or cloud services should be on your list as a business strategist or as a C-level executive. And then you should focus on that, because without actually doing that, achieving competitive advantage will no longer be possible. And well, interesting times ahead, I guess.

CN: Exactly. So you need to take into your interest these technologies, because this is the way you can keep up with the competition. And if you do it right, you can be ahead of them.

MG: Cool, thank you. Thank you.

CN: Thank you, Michał.

MG: And thank you our viewers for watching another episode of IT Leadership Insights. Please share it, like it, and do let us know if you’d like another topic covered in one of our other episodes. This was IT Leadership Insights by Future Processing. Thank you.