At the times when digital mainly meant collaboration and knowledge management, businesses quickly realized that the principal barrier to success was trus (or lack of). No trust, no success. When the question of data and analytics raised, many felt relieved. A field where the human bias had no importance, where everything will be about factual and undisputable information.
The fact is many analytics projects are facing the disbelief of end users. They see it as black box inside which a big amount of data is being processed to deliver numbers, insights and indicators supposed to help them make better decisions.
But in the mind of users who’ve been used to trust “their own” numbers, obtained by following their own methods, and who even used to favor their intuition over facts :
• amount of data means question about where the data come from and how relevant they are.
• processing means to apply an unclear logic that’s not theirs.
In other words, when one does not know what comes inside the box and how it’s processed, it’s hard to trust what comes from the box. And even more when it’s a logic an easy excuse to stick to one’s habits.
We must build trust in data
So the success of a data/analytics project mainly relies on the trust people will put in data.
This is confirmed by a KPMG study titled “Building Trust in Analytics“, and backed with numbers.
KMPG says that trust in data relies on four cornestones.
• Quality:what is the source of data, where does the data goes through before entering the system, can the data be modified, what are the available skills to exploit them, how does the company ranks regarding the best practices ?
• Efficiency : do results meet expectations ? Can we tell how relevant and useful they are ?
• Integrity :is data collection and processing compliant with the lawn, is the client informed of what’s being done with his data, how does the client perceives it, does the use of data follows ethics and internal policies ?
• Resilience :what are the governance and security rules applied to data processing and use ?
We must admit that most businesses are not up to standards since less than half of them are far for meeting these requirements.
Data is back to basics for KPMG
After having read this study I had the opportunity to visit KMPG’s Insights Center in Paris. It’s a space dedicated to working with clients on data/analytics projects. A data/analytics practice at KMPG ? That’s obvious for those firms whose business has always been about numbers. To some extent, seeing them building a data business means they’re only back to basics.
Building trust in data requires education and a lot of collaboration with the client. Making business owners aware of the reliability of what’s being done requires to co-create with them. These new ways of working also requires new workspaces to favor conversations, ideation and collaboration.
In the end the very specific nature of data based projects forced KPMG to invent new ways of working with the client to build trust in data :
- through collaboration
- through co-creation
- by involving business stakeholders at every step
- by using interactive devices making it easier to manipulate data and understand the insights.