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4 Top Challenges to Implementing Data Science Modernization

The emergence and growing capabilities of the public cloud have led to a revolution in new products, processes, and tools. Lower costs have supported an explosion in data, and new analytical capabilities have created an exponential increase in insights — just to name a few benefits.

4 Top Challenges to Implementing Data Science Modernization

That said, change is rarely easy, and broad data science modernization efforts spurred by enhanced capabilities are no exception. In our white paper The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution, we explore the myriad of ways in which technology is enabling an eruption of innovation and examine nine challenges organizations may encounter during their own data modernization efforts.

Four key examples of the challenges we address in the white paper include:

  • Slow ROI: Experience shows that data science modernization, particularly when supporting machine learning (ML) and artificial intelligence (AI), is typically slow to deliver ROI. However, evidence also shows companies that pursue these capabilities show a 3x increase in revenue, efficiencies, and cost reduction. 
  • Insufficient investment: Many data science modernization POCs and pilots show promise, only to die on the vine due to a lack of foresight in available investment. It pays to be frank (and somewhat aggressive) in securing the necessary resources to drive sustained success. 
  • Data integrity and security: It’s well understood that ransomware and rising regulatory requirements have raised the stakes for data integrity and security, but less acknowledged is that better data directly leads to superior outcomes for AI and ML. For all of these reasons, it is imperative this area is addressed from the very beginning of program efforts. 
  • Institutional buy-in: Of all the factors that go into determining the success or underperformance of data science modernization efforts, sufficient and sustained buy-in from all aspects of the enterprise is probably the most important. What’s more, leaders need to lay out a clear commitment and be willing to consistently back it up.

Any change program is going to face challenges, and they usually increase in direct proportion to the level of effort and potential payoff. In The Inevitability of Data Science Modernization During the Machine Learning and AI Revolution, we explore what it will take to succeed in a future that is dominated by AI and ML. This includes real-world road maps to guide the journey as well as the many challenges that can occur along the way.

If you’re ready to tackle your own data modernization effort, download the full white paper here and arm yourself with a proven strategy for success.

October 22nd, 2021

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