by March 18, 2014 0 comments

Given an economy that’s still getting back on its feet, scandals left and right, and a world that can seem full of strife, people today are not in a trusting mood. It’s not surprising that over the last several years, the public’s trust in government and business is markedly low.
And when the topic of big data comes up, it spurs distinct trust issues for the corporate world. With big data, the biggest hurdle isn’t so much achieving success. It’s actually getting people to believe in big data-to trust it. And it’s not just trusting the data itself. It’s trusting what can be done with the data.
Why building trust in Big Data is so big
You see, a single piece of data in and of itself has only so much intrinsic value. What contributes the most value to the business are the correlations done to provide insight, perspective and meaning to a given data point. It’s not easy, however, to get people to believe in-and trust-insights pulled from models based on big data. We are faced with it every day. Do we trust our gut, our experience, our intuition-or the data? Even when data correlations reveal an increase in sales or efficiency, line of business leaders may not believe what they see-at first. That’s why building trust in big data is so important. People will not want to give up the gut-level decision making processes they’ve used for years until they are convinced beyond a shadow of a doubt that big data works. That’s why proving big data’s worth may well take more than one successful project.
Earning trust doesn’t happen overnight. It can be a long process, and one that requires tact as you interact with business leaders whose turf you are treading on. But one has to go through a lot of trial and error to be successful. A major part of the initiatives involves gaining the trust of key stakeholders within the business lines whose problems can be attempted to solve.
6 Steps towards earning trust in Big Data initiatives
Through this process, six steps have been identified towards earning trust in big data initiatives.
1. Understand the business and understand the data – It may seem obvious, but undertaking an often complex analytics deep-dive for a line of business requires sitting down with key people to understand what that line does, how it interacts with the rest of the company, and the challenges it faces. What is impeding progress? What is keeping them from being more efficient? You’ll need a business process thinker for this-someone who can ask the right questions and has a good understanding of the available data.
2. Determine the problem and how the data can help – Start to connect the dots between the business problem and the available data. Will this data help solve this problem? At this point you might realize that the type of data you need is missing. Can you get access to it? One caveat: People tend to think of big data as social media and the Internet of Things (IoT). They feel the need to immediately go outside of the enterprise to mine that type of data, and sometimes that is necessary. But integrating external data adds complexity and I would argue that there is a significant amount of untapped value in the data inside the organization. Start there and determine whether pulling in external data is necessary or helpful. Work with internal data first, and then branch out.
3. Set reasonable expectations – walk away if you have to – Make sure the business understands that for every business problem solved, there may be three or four unsolved. If projects are not generating the results you’re looking for, you have to be willing to walk away and look for the next opportunity.
4. Bring in big data while living in the old world – Approach big data projects in parallel with traditional methods. Business leaders are not going to give up familiar processes and technologies and say, “Okay, I’ll trust the data now.” You’ve got to prove it to them while still operating within their parameters for making decisions.
5. Be flexible – The big data analysis you’re embarking on is an exploration of sorts. You may find value in an unexpected area. This implies being flexible both about methodology and about tools. Big data tools are in the early stages of development. Recognize that your big data toolset will probably not be the same a year from now. You need to be flexible in implementing tools, upgrading and investing in technologies as needed. You may also need to seek out expertise with different types of analyses as you continue to work to find value in big data and show that value to the business in new ways.
6. Keep your eye on the prize – The process will feel cumbersome at times. It’s in those moments that you have to stay focused on results. Big data is in the early stages of maturity, and the methods and tools for easy, efficient data analysis aren’t quite there yet.
Building trust in big data insights takes time. It could take up to six months just to prove that first business case But once a few initiatives are under your belt, the business will warm up to big data and will be clamoring for more to solve key challenges. Not only will the business change but IT will also experience a shift. As IT drives big data initiatives that deliver business outcomes, IT will be transformed into a strategic partner to the business, a trusted partner that has earned that trust.

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