Reference document

Develop Data Strategy
Data Science in one of the most exciting areas in business today. While most decision makers understand the true potential of this fields, many remain skeptical on its implementation. To be successful, organizations first need to define a clear strategy in synchronization with their core business objectives for their data science implementation. In the 2015 TDWI report “Seven Steps for Executing a Successful Data Science Strategy”, the following checklist of seven basic tenets of smart and results-oriented data science was presented * Identify your key business drivers for data science – Before getting started, an organization must ask what real data science efforts can provide that traditional business intelligence and analytics are not. If there are gaps, it’s critical to hire personnel with real “knowledge of and curiosity about the business” to help fill them. * Create an effective team – It takes more than curiosity, however. And hiring a multitalented superstar – which is “like chasing unicorns” to begin with – can leave an agency with a one-off, artisanal operation whose creator then leaves for greener pastures. “[A] wiser course is to develop a stable team that brings together the talents of multiple experts.” * Emphasize communications skills – “Organizations that use data science successfully almost universally point to communication as a key ingredient to their success”. Organizations should “make it a priority as they evaluate candidates for data science teams.” * Expand the impact through visualization and storytelling – “Data science thrives in an analytics culture”, but “not all personnel… are going to be part of data science teams, nor should they be.” Finding ways to help non-statisticians grasp the insights in the data is critical to getting real value out of the investment. * Give the data scientists all the data – While traditional analytics often focus on a carefully defined set of structured data, data science has the potential to draw value from the vast messes of unstructured data that most organizations create. “Data scientists need to work closely with data at every step so they know what they have” and they need to have as much of it as possible. * Pave the way for operationalizing the analytics – Descriptive analytics are useful, but predictive analytics are far more valuable – and prescriptive analytics offer the most potential benefit by far. To make this possible, “data science teams can move away from uncoordinated, artisanal model development and toward practices that can include quality feedback sessions to correct flaws.” * Improve governance to avoid data science “creepiness” – Both data science teams and top leadership “must be cognizant of the right balance between what they can achieve … and what is tolerable - and ethical - from the public’s perspective.”
Strategy Development Processes