Modernizing Data Science in Today’s Business

As we kick off our final blog series of the year, we wanted to take a deeper look at the application of data science. Though the notion of a total integration of data science may have seemed like a stretch a decade ago, integrating data science is the gold standard for thriving modern businesses today. Fulcrum’s Executive Vice President, Tara Piazza and Director, Jim Chung shared their perspectives on the changing role of analytics.

Modernizing Data Science in Today’s Business

October 30, 2018


As we kick off our final blog series of the year, we wanted to take a deeper look at the application of data science. Though the notion of a total integration of data science may have seemed like a stretch a decade ago, integrating data science is the gold standard for thriving modern businesses today. Fulcrum’s Executive Vice President, Tara Piazza and Director, Jim Chung shared their perspectives on the changing role of analytics.

Q: Has the application of data science to improve business changed? If so, how?

Data science has become an operational mainstay because it has been proven to successfully impact bottom lines.“There was a time not too long ago when data science was something done as a project - such as forecasting sales based on the prior year’s sales”, notes Tara. She adds,“Companies that recognize the value of data science today have a plan for data harnessing, analysis, and implementation”. Successful companies are putting analytics to work in nearly every internal department to increase their productivity, create and offer products to match their client needs, etc.

Q: To what extent are companies transforming their approach to data science (e.g., in terms of systems, skills migration, etc.)?

Simply speaking, businesses are hiring for data science. Large organizations have been making decisions to develop their capabilities, starting with a very senior hire, to build out a data science group and set the course for becoming data-oriented. On the other hand, companies still in the transition of data adoption may not be able to make such an investment. Instead, they may choose to outsource their data initiatives.

The overall hiring approach has transformed alongside the maturity of data science adoption. There is no denying that the need for business-oriented data experts is skyrocketing. Jim notes,“Many seasoned data science practitioners have advanced to senior level strategic roles by being on the forefront of analytics strategy, change management, and data science delivery”. With an increase in demand for data-driven analysis, businesses are looking to hire (or work with) more data specialists who utilize analytics to formulate business strategies and measure results.

Companies that recognize the value of data science today have a plan for data harnessing, analysis, and implementation

Furthermore, Fulcrum is observing a greater demand for Data Engineers and Backend Developers. In the past, many companies were hiring Statistical Analysts or Statisticians to deliver insights. With the advancement in data processing technology, such as maturing big data applications and increasing utilization of unstructured data, there is a larger demand for engineering related skill sets.

In addition to the volume increase of data, the technical needs for data science applications are being driven by the shift towards open source tools as well as emerging methods.“We are seeing an industry shift away from paid applications such as SAS and Oracle SQL and a movement towards open source tools such as R and Python”, notes Jim. While data scientists are busy becoming fluent within these open source tools, emerging data environments (i.e., cloud computing) are enabling faster and more efficient processing. With such advancements, practitioners are finding themselves in a state of continuous learning and progress.

Q: What lessons have you learned about a company’s transformation with respect to data science applications?

Collectively, Tara and Jim point out three elements they have seen among clients of varying industries, sizes, and functional areas who have successfully applied data science. They touch on key factors that drive the success of an organization’s data science transformation.

The successful integration of data science begins at the very top. There needs to be full organizational support, a clear vision set by upper management, and transparent expectations.“With a clear vision defined by the top management team, the organization can keep it laser-focused and demonstrate success quickly”, says Jim. Tara adds,“Getting everyone on board with the importance of data science is key, especially if it requires diverting budget away from traditionally funded aspects of a business. For example, hiring a data science team might mean hiring fewer people in other parts of a company, which might make other people unhappy.”

With a clear vision defined by the top management team, the organization can keep it laser-focused and demonstrate success quickly

“From there, fostering a culture of data science-backed creativity can encourage a team to think outside of the box”. Tara notes. The innovative vision set by the leadership team needs to flow down to the business areas and across the firm. When the organization is colored with such mindset, data science can make a lasting impact and change how an organization functions. Tara says,“For example, having a well-defined customer segmentation approach can successfully bring about a positive change in a company’s success with regard to the products developed, regionally offered, and micro-targeted. Cross-selling based on creative data-driven predictive models has led to major revenue growth”.

UX and design thinking are other key elements that should be an integral part of data-driven solution development. We have seen many cases where a business invests significant time and resources to build a new tool, and it struggles to implement or fully utilize the new solution in its day-to-day practices. Though the work of adopting data science is never completely finished due to its ever evolving nature, investing the time to map user stories and analyze experience gaps is an excellent first step in creating a plan for integrating analytics. Jim adds“Apply design thinking to thoroughly understand how the users interact and behave among each other. Invest enough time on the initial research”. Then it will become easier to understand the current state and identify a roadmap for development and implementation. Such pre-work will increase the chances to scope it right, prioritize activities, and set goals appropriately.

Getting everyone on board with the importance of data science is key...

 

Fulcrum offers clients the opportunity to stay on the cutting edge of data science through a flexible data science acceleration team we call DSAT. Our clients utilizing DSAT benefit from the hands-on data science, data engineering, and backend development skills delivered by our specialists. Such partnership leads clients through the design, creation, implementation, and ongoing refinement of custom built data-driven tools and processes.

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