Is It Time To Expand Your Data Analytics Team? | 7wData

CIO recently published an article that highlighted a survey that was revealing. The survey, conducted in April and May, included 150 executives, from large companies of over 5,000 employees, with responsibility for making business decisions influenced by Big Data. The survey reports that 98 percent said their company encourages its employees to ground business decisions in data and evidence (60 percent said their company 'strongly encouraged' the practice; 38 percent said their company 'somewhat encouraged' it). This sounds consistent with current thinking and best practices for the adoption of analytics to make better business decisions and as a survey finding, isn’t particularly unexpected .

However, what was interesting was this same survey found that just 23% of respondents felt their company is ‘extremely successful’ and 39% ‘very successful’ at actually leveraging Big Data to make decisions.

Could this be indicative of the state of big data efforts? In other words, while we are on the right road, for many of us there remains the fact that we haven’t fully reached the hoped for destination.

Our ability to consistently reach the full potential of data analytics is hampered, according to California-based Ventana Research, because a majority of organizations do not have enough experience in applying analytics to business problems and lack training on using the tools. Ventana notes that it undertook benchmark Research (PDF) last year to determine the attitudes, requirements and future plans of companies that use predictive analytics and to identify the best practices of organizations that are most proficient in it. The researchers found that predictive analytics findings are most commonly used for forecasting (used by 56% of respondents), marketing analysis (46%), customer service (41%) and product recommendations or offers (35%).

The primary barrier for taking predictive analytics use to the next level, believes Ventana, are issues of expertise and training; critical considerations in adopting and using predictive analytics effectively.