Building a Data Culture

virt_1_346x214Many companies want to ‘do’ big data today. They’re spending money on systems, software, consulting, training and other services to be able to capture, process, analyze and use data. Those are all things that need to be done to be up their data science capabilities and skills. Companies need the right platforms, the right systems, the right people and skills to be able to properly analyze and use their data.

There’s one area that many organizations fail to address when building up their data analytics programs and skills. That area involves the corporate culture. Specifically, it involves the culture around listening, curiosity, investigation and willingness to try and fail.

Corporate culture can play a huge role in the success or failure of data analytics programs. If your company’s culture doesn’t like hearing new data that may provide conflicting information, your big data initiatives may be set up for failure from the very beginning.

In my experiences, the ability to listen and act on new data is one of the most important aspects of corporate culture that leads to success with data analytics and big data. If you don’t have a corporate culture and leadership team willing to listen to new information. For example, if your CEO doesn’t listen to data or arguments that go against her beliefs, you may be in for a very difficult time if your data analysis shows a reality different than the one that she expects or wants.

While listening and accepting competing arguments and data is the top cultural issue that can make or break big data, the other cultural aspects are important as well. For example, if the people who are working with your data aren’t curious about the data and willing to spend plenty of time investigating that data then you may be wasting money giving those people the proper skills to become a data scientist. You may be training them to act as your data scientists, but if they aren’t interested in finding out more about your data and investigation new avenues of analysis, you may not get the move value from them or your big data initiatives.

Lastly, your corporate culture should be willing to accept failure. Now, I’m not saying you should embrace or excuse failure, but many times in the data analysis world you end up finding analyses that don’t match with your expectations. Much of the time spent by data scientists is spent in small analysis projects looking for new ways to look at data. Many of these small projects end in failure with nothing of measurable value to show for the time spent on that project. Even though it may seem like wasted time, these types of projects are what make great data scientists as it allows them to continuously improve on their skills.

Successfully implementing big data initiatives is much more than just buying some software or systems. Successful big data initiatives require working on soft skills as well as organizational culture to ensure that the big data mindset is ingrained throughout the organization.

This post is brought to you by SAS.

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