International Institute for Analytics

The analytics landscape has matured, broadened, and totally transformed in recent years. This transformation has been driven by a wide range of factors including big data, the cloud, the increase in processing capability, the decrease in storage and processing costs, and the near-ubiquitous availability of algorithms. As a result, an organization must also totally transform the tools and processes it uses for analytics if it is to recognize its analytics potential.

In fewer than two years, Honeywell Aerospace turned a functional BI competency into an enterprise business analytics team—and created a competitive advantage. This 75-person analytics team has transformed the company’s operations, developed analytics products for customers that Honeywell is monetizing, and created well over $100 million in aggregate value with an impressive ROI. Perhaps Honeywell Aerospace’s most important lesson is leveraging BI to evolve to analytics. Analytics experts know that BI is not analytics, yet many non-experts don’t realize this; they see BI and analytics as the same. Honeywell Aerospace hasn’t argued about it, but has focused on delivering value. This meant starting with BI opportunities, creating value, building trust, and then proceeding to deliver even more value by incorporating advanced analytics to deliver insight beyond the expectation.

With the hype surrounding Artificial Intelligence (AI) today, almost everyone in the analytics and data science space has been asked about AI by their business partners. Unfortunately, during these conversations it often becomes apparent that the business person really doesn’t have a clue what AI really is or what AI is best able to solve.

To respond to the high demand of the private sector, over the past decade, colleges, universities and a large number of for-profit institutions have created certification, bachelor’s, master’s, and Ph.D. programs along with numerous executive ed. offerings; the number of institutions now offering data science degree programs has grown exponentially. This data science-centric education orientation is transforming academia, affecting research funding, degrees, research, and publication. For universities, private sector involvement provides funding and real-world perspective. For companies, collaborating with universities provides access to data science resources and can help develop a pipeline of data science talent.

The number of analytic experts in business units is growing three times faster than IT or central analytic staffing. Some call this trend the emergence of “citizen data scientists.” For me, data science apprentice is clearer. Typically, these apprentices are members of a line of business. They are not as technically sophisticated as data scientists. They overlap with each other with similar business goals and skills. Apprentices are both distinct from or the same as business analysts, depending on the enterprise. This primer explores the foundations of “When to Use Which” platform, important to an apprentice’s full potential. It is purposely not a deep dive into a complex topic but an entrée into the architecting of analytics enablement.