Many organizations are being overwhelmed by huge volumes of data, but they don’t know how to reap its benefits. This ebook looks at how to gain key business insights from that data, choose the right tools to manage it, and leverage predictive analytics to improve business processes and make more targeted decisions.
From the ebook:
A 2017 research study conducted by Forbes Insights and Dun & Bradstreet revealed that 59% of more than 300 companies surveyed did not use predictive modeling or advanced analytics, and 23% still used spreadsheets for most of their data analytics work. Even more startling was the fact that 19% of respondents used no analytical tools more complicated than basic data models and regressions.
This news is not encouraging for big data and analytics champions and managers—nor is it good for their lagging companies, as evidenced by an MIT Sloan Management Review that found that 67% of companies that were aggressively using analytics achieved competitive advantage in their markets.
Sacrificing competitive advantage is reason enough for CIOs and CDOs to place analytics adoption by the company near the top of their priority lists.
What is slowing down meaningful big data and analytics adoption—and what can CIOs and CDOs do about it? Here are five common problem scenarios and ways to overcome them.
Problem 1: The business is not seeing enough tangible impacts from analytics
There are still too many organizations running analytics as a series of pilot projects in test tube mode. While testing small pilots was a good initial concept for introducing analytics in companies, too much time has passed to continue this approach. Test tube lab projects suggest that analytics are not ready for prime time in businesses. It is impeding meaningful corporate analytics adoption because individuals at the C-level don’t take these “lab” projects seriously.