Mr. Soupal, how do you define the term “big data”?
VS: If the amount of data is very large, takes different forms, and varies in data flow velocity, we are talking about “big data.” The information can come from a lot of different sources like computers, the Internet and social networks. A study recently showed that 2.5 trillion bytes of data volume is produced worldwide every day. No wonder that companies are losing track on this.
Why should companies start collecting big data?
VS: Basically, big data helps companies to get to know their customers and to understand their own processes better. Whether the task is to develop a new product, to improve an existing product or to make internal processes more efficient – big data is the tool to foster your business.
Good examples are companies with digital business models like Airbnb. Because of data analysis they know exactly what their customers want and are successful because of it.
Another aspect is that in an increasingly global business world, you have to compete with competitors around the world. Most of them have already started to put big data and its analysis to best use and were able to respond to changing consumer needs with great success. If you hesitate, you might fall behind.
For whom is big data a must?
VS: Big data can help each and every company to grow. Maybe the hairdresser or the baker around the corner are not the main target group for big data tools, but once you have a certain amount of customers or produce a certain amount of products, you should use big data to analyse your customer’s needs and improve your own processes.
How should companies proceed?
VS: If companies want to use big data, they need the right tools on the one hand to collect and store the data and on the other hand to analyze and visualize it. And there is a slight difference whether they are using big data to get to know their customers and predict their wishes or to improve internal processes.
In both cases, they first have to find out where their data sources are and if they need to add new measurements to collect the information. Sometimes the data is there, but just in paper form, so they must digitalize it to process it. Simply put, the first steps are to scan data sources and prepare the data for the big data tools.
Then the companies need to define what their most critical processes are, set up a big data project prioritization to state where to start and make the results visible.
If a company decides to use big data, how can they make sure that it will be successful?
VS: There are a few business parameters that will make the change. I call them “V’s” with regard to the four technical key criteria’s for big data – velocity, variety, volume and veracity – defined by Gartner. So the in my opinion most important business key factors are vision, visualization and value. Let me explain:
Every company that starts with big data should have a vision, what they want out of it. To just collect them is not enough. Key is to be ready for the opportunities it offers you because it could be your ticket to the digital transformation. And not just the IT department, a company’s management must be aware as well of big data benefits.
Visualization is an important issue in big data. Big data is based in technology capabilities and you need the right employees for that. Data engineers, scientists, statisticians don’t grow on trees and are hard to find. But without them, collected data is of no use to the companies. If it should be successful, companies need not just change their processes, but also make the results as visual as possible. So they need to hire data scientists to enable regular workers see as much as possible. This will also support process changes.
Last but not least, successful big data projects should bring value to companies. It is important to never forget that only data that has been analyzed is useful data. Companies should be driven by value and the value can be classical business improvement but also secondary value with new business models for example with the Internet of Things.