The solution is to develop data science business and knowledge processes and engage data scientists to gain business understanding. It may be helpful to think of data scientists like lawyers: highly trained knowledge workers that can be hired full time in-house or engaged independently on a time or project basis.
In the above image, the rectangular boxes represent an approximate data flow and circles represent process flow. The processes and data are arranged by degree of effort and degree of information structure.
Data analysis may be driven by bottom-up (from data to theory) or top-down (from theory to data) processes - or a mixture of both.
Such data science processes include:
1) Information gathering
2) Re-representation of the information in a schema that aids analysis
3) Development of insight through the manipulation of this representation
4) Creation of some knowledge product or direct action based on the insight
Data Science for Business Understanding Formula
Information > Schema > Insight > Product
The data analysis may be organized in two key loops:
1) Searching loop (seeking, extracting, filtering information)
2) Understanding loop (modeling and conceptualization from a schema that best fits the evidence)
New academic research suggests that companies using this kind of data science and business analytics to guide their decisions are more productive and have higher returns on equity than competitors that do not. As data science changes the game for virtually all industries, it will tilt the playing field, favoring some over others.
Top Benefits of Data Science for Better Business Decisions Formula
Data science in business is about having the right information and insight to create better business outcomes. Business analytics means leaders know where to find the new revenue opportunities and which product or service offerings are most likely to address the market requirement. It means the ability to quickly access the right data points to evaluate key performance and revenue indicators in building successful growth strategies. And, it means recognizing regulatory, reputational, and operational risks before they become realities.
1) Having the knowledge you need: Data science delivers insightful information in context so decision makers have the right information, where, when and how you need it.
2) Making better, faster decisions: Data science provides decision makers throughout the organization with the interactive, self-service environment needed for exploration and analysis.
3) Optimizing business performance: Data science enables decision makers to easily measure and monitor financial and operational business performance, analyze results, predict outcomes and plan for better business results.
4) Uncover new business opportunities: Data science delivers new insights that help the organization maximize customer and product profitability, minimize customer churn, detect fraud and increase campaign effectiveness.