6 Warning Signs Your Data Science Team Needs Revitalizing

If you haven’t yet built a data science team that can leverage massive amounts of data to drive a better business, then you’re missing out on one of the most valuable opportunities of the digital age. Most companies have, in fact, built data science teams, but there are key signs that they’re not doing enough to keep up with changes in the data landscape. Here are six warning signs you may need to rethink your approach to data science. 

1. You Don’t yet Have a Chief Data Officer (CDO)

As data assets become increasingly important in more sectors of the organization, the Chief Data Officer (CDO) is becoming an indispensable role. A Gartner survey indicated that 57 percent of companies now have a formal title of CDO on their management teams, and more than 50 percent of those CDOs will report to the CEO in 2018, up from 40 percent the year before. Chief Data Officers are charged with creating a data-driven culture, distributing data insights across different lines of business and finding new ways to innovate where and how data is utilized to drive better business processes, products, and services. 

2. You Haven’t Employed an AI or Machine Learning Expert

The artificial intelligence and machine learning disciplines are supercharging the role of data science and big data analytics. Forbes reports that 70 percent of enterprises expect to implement AI over the next 12 months, empowering a new era in analytics and data management. Data teams that aren’t employing AI experts will soon be missing the boat on some of the biggest areas that data science can add value: generating real-time predictions for marketing and sales teams, streamlining common IT and other internal processes and improving the customer experience at scale. 

3. Your CEO Thinks “R” and “SAS” Are Just Letters in the Alphabet

Various programming languages and tools have been amplifying the data science field for some time, including R and SAS, and it’s important for your executive teams to know the lexicon and understand the role they can play in achieving data science mastery. The SAS data science tool is designed to empower data manipulation/optimization and advanced statistical concepts to solve real-world business problems and predictive modeling techniques. R is another key programming language that helps data scientists master data exploration, data visualization, predictive analytics and descriptive analytics. 

4. You Can’t Easily Turn Data Into Insights and Solutions

The value of data can only be optimized if your data scientists and engineers can apply raw data to glean insights and create real-world business solutions that scale. Data scientists can often identify a trend but fall short in effectively constructing and implementing a better process or business model that leverages the uncovered data. Another challenge is to scale insights into a viable solution: a McKinsey study found that only 8 percent of 1,000 respondents with analytics initiatives engaged in effective scaling practices. This leads to bringing some organizations to outsource their business intelligence capabilities. It is predicted that the insights-as-a-service market will double as insight subscriptions gain traction, and 66 percent of enterprises already outsource between 11 percent and 75 percent of their business intelligence applications. Outsourcing is one trending option. Upskilling your data science teams is another. 

5. You Don’t Have a Data Cloud

The cloud is already revolutionizing many of the ways in which companies process data and streamline operations. Cisco reports that 94 percent of workloads and compute instances will be processed by cloud data centers by 2021. But the trend is moving to the next stage with data analytics as 50 percent of enterprises plan to adopt a cloud-first strategy for big data analytics. The cloud-first strategy for big data and analytics can help organizations open the door to more cost control and better flexibility with analysis than on-premise systems can. 

6. Analytics Teams Are Still Working in Silos

Like most business endeavors, data science is most effective and productive when teams collaborate on their activities. Especially with cloud-based analytics platforms, companies can leverage a more cohesive work experience between analytics teams to improve ROI of their combined activities and lower the total cost of ownership (TCO) of solutions. Research shows that businesses will see a minimum of 3-5x increase in TCO and an ROI of 171 percent from using analytics that are designed to work together. Bridging the gap between operational silos can have a measurable impact on bottom-line solutions and results. 

As the data science field reaches a new stage in its evolution, there is still ample time for organizations to take advantage of the latest tools, technologies and techniques to stay competitive. Take this opportunity to identify warning signs like these and get ready to thrive in the golden age of data science.

About the Author

Eshna VermaEshna Verma

Eshna writes on PMP, PRINCE2, ITIL, ITSM, & Ethical Hacking. She has done her Masters in Journalism and Mass Communication and is a Gold Medalist in the same. A voracious reader, she has penned several articles in leading national newspapers like TOI, HT, and The Telegraph. She loves travelling and photography.

View More
  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.