The big focus in analytics today is on access for all, and the ability to not only see what happened in the past but what is going on now or about to take place.
A recent survey by Forbes Insights and Dun & Bradstreet of more than 300 senior executives across a broad range of industries confirmed that the goal of many organizations is to develop a data-driven culture, but also finds there is still plenty of work to be done to make that a reality.
The Dun & Bradstreet / Forbes Insights study revealed that data analytics skills gaps persist across the enterprise, and that 27% of analytics professionals surveyed cite this skills gap as a major impediment in their data initiatives.
Among the study’s finding are that analytical methods and tools lag behind both the appetite and ambition of most business leaders with 23% of analytics professionals still using spreadsheets as their primary tool for data analysis. And, although the study notes that “data analytics has gone mainstream,” it adds that the C-suite and senior leadership need to do more to drive the cultural change to enable better utilization of analytics, with 38% of those surveyed saying their companies need to do more.
To become a data-driven enterprise, organizations need to do more with the data they have and are analyzing. Only 38% of respondents felt strongly that business leaders took full advantage of their analytics initiatives.
Transforming data into agile, accessible, and actionable insights requires a multi-pronged approach, advises DBTA lead analyst Joe McKendrick. This includes development of an analytics business plan, taking stock of current data assets, casting a wide net for all data types and sources, keeping trust front and center, engaging in storytelling, developing and nurturing people who are skilled at both building and managing analytics systems, continuous evaluation of data, and a recognition that human and review or intervention will be required to oversee and assess the process.
Leading data analytics solutions provide capabilities such as integration, reporting, high performance data mining and statistical algorithms to not only look at what has happened in the past but to also change the future.
Use cases include the prediction of customer behavior; anticipation of cross- and up-sell opportunities; improvement of marketing campaigns; prediction of customer churn; analysis of online baskets to identify patterns; fraud reduction; assessment of merchandise availability; potential product failure; and anticipation of future product needs.
Data Analytics Solution
Microsoft SQL Server Analysis Services Data Mining
Oracle Advanced Analytics
Teradata Warehouse Miner