Any big data or analytics conversation would be remiss without the mention of "data scientists." Much has been written about data scientists– who they are, who they should be, and where to find them. My colleague James Kobielus wrote an interesting series of blog posts about the skills required to become a data scientist.
From a customer intelligence (CI) perspective, we outlined four segments of CI professionals — marketing practitioners, marketing technologists, marketing scientists, and customer strategists. Of these, marketing scientists typically orchestrate the customer and marketing analytics function. They manage the reporting, analysis, and predictive modeling processes using marketing and customer data.
In a CI context, we find that the role of the marketing scientist has evolved from being a pure data analyst drowning in data analysis to that of an analytics translator — someone who is equally comfortable with building advanced predictive models and also adept at embedding the output of the models into customer-facing processes. What type of marketing scientist does your analytics team have?
We recently published a report on why "Customer Intelligence Needs A New Breed Of Marketing Scientist" (accessible to Forrester clients). In the report, we highlight ways to develop analytics translators across the staffing cycle — starting from attracting the right talent, nurturing the relevant skills, training with new skills, and incenting them based on business impact.
Is big data just more marketecture? Or does the term refer to a set of approaches that are converging toward a common architecture that might evolve into a well-defined data analytics market segment?
That’s a huge question, and I won’t waste your time waving my hands with grandiose speculation. Let me get a bit more specific: When, if ever, will data scientists and others be able to lay their hands on truly integrated tools that speed development of the full range of big data applications on the full range of big data platforms?
Perhaps that question is also a bit overbroad. Here’s even greater specificity: When will one-stop-shop data analytic tool vendors emerge to field integrated development environments (IDEs) for all or most of the following advanced analytics capabilities at the heart of Big Data?
Of course, that’s not enough. No big data application would be complete without the panoply of data architecture, data integration, data governance, master data management, metadata management, business rules management, business process management, online analytical processing, dashboarding, advanced visualization, and other key infrastructure components. Development and deployment of all of these must also be supported within the nirvana-grade big data IDE I’m envisioning.
And I’d be remiss if I didn’t mention that the über-IDE should work with whatever big data platform — enterprise data warehouse, Hadoop, NoSQL, etc. — that you may have now or are likely to adopt. And it should support collaboration, model governance, and automation features that facilitate the work of teams of data scientists, not just individual big data developers.