Big Data Needs The C-Suite's Trust

If decision makers don't trust data, projects are doomed from the start


In a recent survey of 2,165 data professionals commissioned by KPMG and conducted by Forrester Consulting, 49% of respondents expressed a belief that their C-level executives don't fully support their organizations' data and analytics strategies. This is despite the survey also finding widespread acknowledgement that data and analytics are essential. Indeed, 70% of respondents said that data and analytics (D&A) are integral for understanding how products are used, 69% that they are vital for understanding existing customers, and 67% that they are key for understanding what new products and services to develop.

The reason for this discrepancy appears to be a lack of faith. Only about 34% of business leaders said are 'very confident' about the insights they get from data, just 13% that their firm excels in the privacy and ethical use of data and analytics, and around 70% that using data and analytics leaves their organization vulnerable to reputational risk.

The survey follows an emerging pattern, with other recent surveys showing similar skepticism from decision makers about their data initiatives. In another KPMG survey of 400 US CEOs earlier this year, 77% said they have some level of distrust toward the quality of the data on which they base their decisions, while a recent Economist Intelligence Unit (EIU) survey saw just 2% of respondents say they had achieved ‘broad positive results’ from their data projects.

However, all of this does not mean that data and analytics efforts are not working, and it doesn’t mean that data analytics themselves are not worthy of trust. Surveys show only perception, and the ROI of analytics projects is often intangible. What is clear, however, is that many are yet to be convinced, and building trust in analytics among non-data senior executives still has some way to go.

In order to develop this trust, it is not enough to simply produce volumes of case studies showing analytics succeeding. Decision makers need to understand where their data comes from and why it generates the insights it does. There needs to be a culture of transparency and collaboration. Nate Crisel, head of the Real World Informatics and Analytics unit at Astellas Pharma US Inc. argues: ‘Having bridges in place is what separates leaders from laggards. These bridges are what allow the analytics insights to flow freely from the analytics experts to the business leaders. Without them, the high-powered - and, often, high-priced - fruit of advanced analytics will rot on the vine, as the saying goes.’

By democratizing data, all stakeholders have access to the data and you have a culture that understands how, when, and why to use it. Part of this comes from presenting the data in such a way as a layman can make sense of it, with data visualization a powerful tool in this. Adam Dathi, Senior Business Intelligence Consultant at Yieldify, recently described for us how they overcame issues with the understandability of the data, noting, ‘The data we had collected in the BI Warehouse were raw and difficult to use. We circumvented this through the use of custom tables created in Looker. These tables were summarized, enriched and the fields renamed before being presented to the user in comprehensible language. This created useful, understandable data that no longer required an engineering or data background to grasp.’

It is not just about simplifying how the data is presented, though. It is about how the shift to data-driven decision making is implemented, with an iterative, collaborative approach necessary to ensure that everybody is aware of the processes and, more importantly, understands why they are the way they are. Both business leaders and analytics practitioners must provide input and guide the change toward the best possible outcome for all parties. Data scientists usually lack knowledge of the business, and the business leaders don’t often know the data science, so they need to work together to figure out what they’re trying to answer and why. Important to this being done successfully is making sure that decision makers are engaged, through both face-to-face meetings, demonstrations, and even establishing an analytics center of excellence to provide training.

At many companies, it is still the case that management experience overrides insights from data, and a degree of the mistrust expressed in the KPMG survey may well exist because of fear that they will become redundant. Equally, however, it is important to understand that analytics is only about providing evidence for decision making, it’s not there to make the decisions. There is a fine line between understanding the power of analytics and being in thrall to it. Data scientists and business leaders need to find this line, or they risk missing out on an incredibly powerful tool.


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