Many organizations have problems they think are data science problems but data science teams struggle to solve without more direct understanding. Business analysts often rely on data scientists, machine learning practitioners and other statistical/data professionals to build and deploy custom models that would inform critical business decisions, rather than having the tools to apply their domain knowledge and do the analysis themselves.

While data science is sometimes the right solution, a highly precise model is not always required to answer business problems in dynamic environments where experience, speed to insight and business knowledge are critical elements.

Democratizing data sciences with AI-powered solutions such as predictive models, simulation, scenario planning, clustering, and on-demand models can help businesses
-Free up Data Science teams to tackle large-scale, mission-critical & highly repeatable problems
-Make faster data-driven decisions with do,main expertise
-Use AI-powered techniques to make a decision faster
-Fully understand the insights surfaced by AI-generated algorithms to bolster business decisions

Changing role of data scientist

I’m observing a change in the way businesses are thinking about the role of data sciences and its position within their corporate structures. Data Sciences work falls into two distinct camps. One group is focused on the more academic aspects of data science like models and algorithms. The other group is more focused on the pragmatic work of helping make business decisions. This latter discipline is commonly referred to as applied data sciences.In many organizations a centralized team handles both of these jobs.

The role of the Data Scientist is moving out of this centralized group and into subject matter specific departments throughout the organization. A perfect example could be if there is an expert in purchasing process, he or she brings expertise in purchasing and comes with an interest to influence on improving the company’s purchasing processes. In this example it is logocal to push data scientist out of the BI / IT nest and closer to the tactical work in purchasing function.

Another example is role of data scientists in building internet of things capabilities. The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Scientists for IoT traditionally took increasingly central role.But with the merit of bringing business context to data sciences, both consumer and industrial IoT takes data sciences one step closer to the business teams.

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Recommended Reading

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Written by renowned data experts Foster Provost and Tom Fawcett, Data Sciences for Business introduces the fundamental principles of data, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

You’ll discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

• Understand how data sciences fits in your organization—and how you can use it for competitive advantage
• Treat data as a business asset that requires careful investment if you’re to gain real value
• Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
• Learn general concepts for actually extracting knowledge from data
• Apply data principles when interviewing data science job candidates.