RapidMiner is a software platform developed by the company of the same name that provides an integrated environment for machine learning, data mining, text mining, predictive analytics and business analytics. In the interview below, RapidMiner’s CEO Ingo Mierswa discusses his perspectives on the state of the BI industry. Read on for more important insights from RapidMiner!
insideBIGDATA: What are new developments over the past year?
Ingo Mierswa: Over the past year in the BI and analytics market, there is widespread recognition of the business value of analytics. There has been an acknowledgement that while BI is widely deployed and valuable for reporting and analyzing what has happened, predictive analytics is less widely deployed, but of equal and possibly higher value for reporting and analyzing what is likely to happen in a business. We think of this as BI being a car’s rear view mirror, while predictive analytics is your windshield view of what is around the next corner.
insideBIGDATA: How is BI and analytics different from five or 10 years ago?
Ingo Mierswa: The two obvious differences are the use of the cloud and the use of Big Data. And the cloud is not just about computation power but also about data living in the cloud and feeding analytics. This has positive implications, especially for mobile workers, as well as businesses that want to use cloud processing power to complement in-memory and in-database processing.
BI and analytics platforms are also much more able to work with Big Data and especially with unstructured data, including email archives and call center text notes. What hasn’t changed are the challenges of working with corporate data, which may be “dirty,” unstructured, or in different formats (text, images, video).
insideBIGDATA: Are toolsets becoming more intuitive, easier to use?
Ingo Mierswa: BI and analytics toolsets are most definitely more intuitive and easier to use, but that’s only part of the story. These systems can also take advantage of ever-increasing processing power to analyze much larger data sets, in many more formats, than ever possible. Some analytic tools are also becoming more sophisticated in an attempt to unleash the power of predictive analytics for business analysts, and not only data scientists. We have seen the democratization of BI in the past, and see the same happening now for more advanced analytics.
insideBIGDATA: Are BI/analytics platforms becoming available more to decision makers at all levels?
Ingo Mierswa: Given the maturity of the BI market and platforms, they are more available to decision makers at all levels, particularly given the use of BI dashboards by executives and other workers who aren’t skilled in BI platforms otherwise.
For analytics platforms, it’s only in the past several years that they are moving from the domain of data scientists and programmers to also include business analysts and managers who are more likely decision makers. This is due to increasing ease of use, and growing awareness of the business value of analytics other than BI – predictive and prescriptive analytics, primarily. As with BI, we expect, over time, predictive and prescriptive analytics will be embedded into applications that are used by executives and other decision makers who also aren’t skilled in analytics platforms.
insideBIGDATA: Is big data opening things up, or making things more complicated?
Ingo Mierswa: This is a bit of a trick question, because the answer is Yes to both, depending on how and why Big Data is being used. Perhaps the better question is how Big Data can open things up and not be too complicated. The answer is twofold: 1) Big Data can open things up step-by-step. Just as BI and analytics systems are refined and improved over time, Big Data projects can start small in scale and expand with more use and understanding. 2) While the industry hype is around Big Data, what is often overlooked is the value of little data – really all the data. This can be as simple as an online weather forecast graphic prompting a user to dress more lightly or heavily, and mentally plan for a light or heavy commute to work the next day. Or it can be a more complex example of a business initially using only customer complaint data, among all the other customer data available, for a retention project necessary to meet that month’s business goal.
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