Recent Bi-Survey enterprise research revealed that 53% of companies surveyed had inadequate big data “know how” in their organizations. Twenty-five percent of respondents said that they were still struggling with making big data usable to their end users, and 38% said they lacked relevant and compelling business cases for big data application.

These are big numbers for big data and analytics given the fact that both have appeared on corporate CEO and IT agendas for more than five years.

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Some remarkable big data and analytics gains have occurred in the financial services sector, which fine-tuned fraud detection applications as well as internal business decisions such as which customer applicants qualify for which type of loan and credit. Another significant area of inroad was made in retail, where companies gained a better understanding of customer behaviors and different types of buying preferences.

However, for many other companies, maximizing big data efforts remains a work in progress.

4 steps for buy-in

How do you achieve user buy-in for big data? IT and data science departments can take several steps to address big data and analytics usability.

1. Increase usability

Analytics vendors have done a lot to address big data and analytics usability by providing packages of canned “best industry practice ” dashboards and reports to get business users started with analytics–but users have reached a saturation point. They want to move beyond these introductions. Instead, they want the same ability that they had with their old spreadsheet programs, which were easy to build, drill into, and redefine on their own–without IT or data science help.

2. Make big data relevant

Big data and analytics adoption depend upon relevance. If users don’t believe that these technologies can make a difference in their businesses they aren’t going to bother with them.

Further, no one will use an application if it doesn’t solve important business problems, and no one will use something too complicated. Both of these issues must be addressed to maximize big data usability and adoption.

SEE: Special report: Sensor’d enterprise: IOT, ML, and big data (TechRepublic download)

3. Understand problems and solutions

Business analysts must thoroughly understand the end business and what business problems need to be solved by implementing big data and analytics.

4. Know when to pull the plug

If projects aren’t providing a benefit, it’s okay to pull the plug. I once visited with a Teradata data science manager who told me that one of the guidelines that his department used was “know when to pull the plug on a project and move on as soon as you see it is not going to deliver.”

More IT, data science and business departments should adopt this practice.