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ON BEHALF OF: Findings From the Annual Data meanwhile, finding a workforce with the right skills was cited as one of the most sig- nificant challenges to innovating. Interviews with practitioners show how analytics leaders can foster a culture of innovation by educating, communicating, and collaborating with partners in lines of business. Organizational choices such as centralizing the analytics function and having a chief data officer or chief analytics officer role may also help advance analytics maturity. Those who have a CDO or CAO are more likely to report that they have the data needed for decision-making, as are those who work in organizations where analytics are centralized. For leaders of organizations still striving to achieve analytics maturity, the testimony from practitioners who range from data leaders at multinational corporations to the head of a small municipal government team is particularly useful. Their chief lesson: Communication and collabora- tion between analytics and business experts lead to mutual understanding and measurable benefits. In other words, trust delivers value.l Executive Summary CUSTOM RESEARCH REPORT DATA, ANAL YTICS, AND AI: HOW TRUST DELIVERS VALUE2Building Trust in Data: How Analytics Leaders Get the Right Stuff W hen enterprise information leaders at the Cleveland Clinic set the goal of advancing the organizations data analytics maturity about four years ago, they had already established strong programs for decision support and business intelligence. They knew that going beyond dash- boards and reports to give clinicians and managers predictive and prescriptive insights powered by artificial intelligence and machine learning would demand more than just supplying the technology. The leading academic medical center recognized that to create a culture where people understand and use advanced analytics to make better decisions, it needed to focus on its information and data: “Ensure that its available, that its valid, that its governed appropriately, that we have the right processes around accessing it, using it, sharing it, protecting it,” says Chris Donovan, executive director of enterprise information management and analytics. Trust Advances Analytics Maturity Like other leading organizations pursuing advanced analytics capabilities, the Cleveland Clinic has placed a priority on building trust trust in the data thats collected and stored and trust in the analytic insights it generates. And it has seen how building that trust can reinforce a culture that trusts and embraces data-driven decision-making. Findings from our recent survey, which focused on the data and analytics practices of more than 2,400 business leaders and managers, underscores the importance of such priorities. We found a strong correlation between those who report using the most advanced analytics techniques and those whose organizations actively foster data quality, safeguard data assets, and build a data-driven culture. These organizations focus on data quality and management. They set up measures for governing its proper use and security, and by following these practices, they achieve results. In the case of the Cleveland Clinic, that means more researchers trust the data they are accessing from a centralized data lab instead of copying data to work on in their own, siloed system. This leads to more consistent data and more precise results, Donovan says. However, such benefits, bred from best analytics practices, are still not widespread: Our research shows that most organiza- tions are still developing their analytics capabilities. Just 15% of survey respondents report that they use advanced analytics to inform management decisions. Fewer than one in 10 are working with automated analytics, and only 7% apply machine learning and artificial intelligence in decision-making or production workflows. Far more common, respondents rely on business intelligence tools and employee dashboards to support decision-making. At the same time, we observed what could be called a “utility gap”: While 76% of respondents report they have increased access to data they judge useful which is not surprising, given the proliferation of data that accompanies the digitalization of business that access does not equal empowerment. A much smaller number say they are able to leverage that data: Only 43% feel they frequently have the right data needed to make decisions. This utility gap is a persistent trend, with a similar gap found in the MIT Sloan Management Review survey in 2017 1(see Figure 1). While the majority of survey respondents reported increased access to data in surveys conducted in 2017 and 2018, those who believe they have the data they need for decision-making remain in the minority. Figure 1: A Utility Gap Persists 1S. Ransbotham and D. Kiron, “Using Analytics to Improve Customer Engagement,” MIT Sloan Management Review, Jan. 30, 2018. Percentage of respondents reporting somewhat or significantly improved access to useful data over the past year Percentage of respondents reporting frequently or always having the right data to inform business decisions CUSTOM RESEARCH REPORT DATA, ANAL YTICS, AND AI: HOW TRUST DELIVERS VALUE3Why Closing the Trust Gap Matters While there can be a range of reasons why people may not feel they have the “right” data to handle, our survey probed and found evidence for one: a trust gap. Only a very small minority of respondents say they “always” trust data judged by qualities of relevance, completeness, timeliness, and accuracy; slightly more than half say they judge data trustworthy by those qualities at least “often” (see Figure 2). This finding suggests a significant opportunity to shore up data quality to build confidence in data for analytics, in particular when it comes to increasing the likelihood that data is complete the aspect trusted least often. There is ample opportunity for organizations to do more: Only 21% of survey respondents report formal approaches to data quality, which we defined as routinely monitoring, managing, and improving data quality as part of a formal data governance effort (see Figure 3). The largest group is reactive to quality issues a practice that Jeanne Ross, principal research scientist at the MIT Center for Information Systems Research, advises against. “The worst place to fix the data is when its already been collected,” says Ross. Data quality efforts should focus on the business process that takes in data, whether that is from cus- tomers or a part of the business. Ross acknowledges such pragmatism takes commitment. While its straightforward to say, “Fix your processes so that the data collection is very reliable and the quality issues are pretty min- imal,” meeting that goal is a challenge. Thats because it takes ongoing discipline to refine data collection processes, testing data quality regularly along the way. But in her research, Ross has found the effort pays off. “Heres the interesting thing about analytics: Your unique opportunity is going to be on your own data,” she says. The principle applies whether a company is using its own internal data or augment- ing that data with third-party sources. “If you have data, and you supplement that data and you do that in ways that other Few survey respondents are always confident in the quality of their analytics data, although a majority of respondents often trust that its accurate, up to date, and relevant. Trust in completeness of data is lowest, but trust in accuracy is most frequent. Percentages may not equal 100 due to rounding. Just one in five organizations takes a formal approach to data quality, while 30% report at least proactive efforts. The plurality of respondents still tackle the issue informally. Figure 3: Data Quality Efforts Show Room for Improvement Figure 2: Data Accuracy Is Most Trusted Quality How often do you trust that analytics data is: Always Often Sometimes Rarely Never Informal: Individuals who produce or use data reactively correct for accuracy, consistency, timeliness, and completenessData stewardship: Someone is responsible for proactively identifying and correcting causes of data quality problems Formal: Data quality is routinely monitored, managed, and improved as part of a formal data governance effort No data quality efforts CUSTOM RESEARCH REPORT DATA, ANAL YTICS, AND AI: HOW TRUST DELIVERS VALUE
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