Data Science and IT are veritable treasure troves of innovation for the business, but having a great asset is not the same as knowing how to use one. As Lisa Morgan for InformationWeek explains, today’s ‘big thing’ in IT comes with its share of caveats. Let’s review the top six.
- Focus on business impact
- Adjusting the company DNA
- Appreciating clean-up efforts
- Confronting legacy issues
- Building a talent acquisition strategy
- Locating a critical mass of skills
Data scientists love to build and optimize analytic models, but that doesn’t always make this a practical activity for the business:
Technology for the sake of technology and analysis for the sake of analysis have little or no practical value. The possibilities typically exceed what is practical or advisable, although not everyone might agree on the best course of action. Because different people tend to have different vantage points, there may not be common agreement about what the scope of an initiative should be, what it will cost, the time it will take, what its likely impact will be, and what the priorities should be. [source]
In light of technology’s supremacy in today’s business landscape, the IT unit should no longer be regarded as a mere appendage. It is now the lifeblood of enterprise. How can company leadership integrate its data assets to become the DNA of decision-making processes? The answer from Pneuron CTO Tom Fountain is to tolerate unwelcome results and dissenting views.
By some estimates data scientists spend 50 to 80 percent of their time cleaning up servers, sprawling databases, and corrupted data. This time would be more effectively utilized on problems that have never been solved. Similarly, legacy systems and software often function as barriers to real science, subverting the practitioner’s efforts to build complex data platforms that weave together old and new technology.
A caution about hiring data scientists – they’re all the rave, which means they’re currently being over-hyped. This in turn means that if you’re hiring them, you need to ask some pointed questions about their necessity at your organization. In the same vein, don’t expect to hire data scientists in isolation. They’re not really justified on their own – not without developers, sponsors, statisticians, and data visualization engineers for support.
Read the original article summary in AITS here>>