Data Science is a strategic asset
Over the past decades hard disk space has become incredible cheap and most companies decided to simply buy more hard disk, rather than send digital information into nirvana. Whoever made these decisions back then can now be pretty satisfied with himself: These year long histories of events, sensor measurements or simply customer interactions have turned into raw gold ore. Whoever sits on this huge pile of data can nowadays feel like the king of the hill: The dawning of the age of data driven decisions and data science as their principal method promises nothing less than mining this mountain of raw data and turn it into golden nuggets of valuable information.
However, moving a company into the age of data science is not trivial task. A lot of things need to be considered as well from the management's point of view as from the technical point of view. How do we anchor Data Science within the company? As single department or spanned and distributed across all units? What kind of support does a Data Science department need from other parts of the company? Which software vendor does one elect as platform? Or would a multiple vendor strategy be beneficial? What technologies are required to solve the tasks without increasing costs unnecessarily?
The Experience Gap: A hen-egg problem
What is most often lacking at this point in time is a fundamental understanding of the principles of Data Science and the available technologies. However, Data Science seems to be a trend in our time although usually disguised behind an ever growing set of marketing words like Big Data, Predictive Analytics, Deep Learning and many more. Driven by the fear to miss an important capability, more and more companies decide more or less blindly to take a dive into Data Science.
But whether the companies divers will surface again with gold nuggets in their hands or just produce bubbles of warm air depends on the divers experience where to dive, for what to look out and last but not least what equipment to use. If you want to fish in two meter deep water, you don't bring your full scale submarine.
While nobody would probably mount a submarine in this situation, in Data Science it's not always clear what is the most efficient technology to solve a task. For being able to decide that requires years of experience in Data Science and also a fair understanding of the technologies involved. If you start from scratch, you will have a very steep learning curve and probably sink several projects that could have been cheaper and more successful. This does not only waste the money spend on the wrong infrastructure and the work time spend on learning it on the hard tour, but also the time until the first results from Data Science projects are deployed into productive, money generating use is unnecessarily high.
Our Experts bridge the gap
Our mission is to establish Data Science in the companies around the globe for higher work efficiency and a better life. Our team aggregates many years of theoretical and practical experience in the area of Data Science. We have seen the rise and fall of buzz words and always took the look behind the marketing scenery. We helped large and small companies to establish their own Data Science capabilities and we have trained many Data Scientists. Our consultants have been engaged in projects that failed and we learned from that. Our consultants have delivered surprisingly good results and we learned from that, too.
We can provide that experience to help with each phase of establishing Data Science:
- A general assessment, whether an investment in Data Science will pay off
- Suggesting the right anchoring of Data Science capabilities within a company's organization
- Identifying potential use cases within the company
- Developing a concept for the necessary infrastructure and it's setup
- Performing Proof of Value projects
- Nurturing of In-house experience and expertise
- Provide support for single projects