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Business Intelligence Maturity Market Study and Recommendations

Organizations today face an increasingly challenging business environment. Across industries, new companies and nimble competitors are taking advantage of advanced analytics and AI and disrupting traditional business models and markets.

Cloud computing and open source have caused fundamental changes in business intelligence (BI) and analytics infrastructure, enabling the introduction of new technologies such as artificial intelligence and machine learning while also lowering the barriers for provisioning new capabilities. The most nimble, innovative companies have quickly taken advantage of these new technologies—and the analytics they enable—to gain a competitive advantage.

Many traditional companies, despite significant investments in data, technology, and resources, are struggling to navigate this complexity and effectively compete on analytics. Though many top executives acknowledge that high-quality data, BI, analytics, and AI are critical to the future success of their companies, up to 70 percent of the initiatives and projects in these areas fail to meet their objectives.

Why is the transformation so difficult? Competing on analytics requires fundamental changes across the entire organization. Companies must create a data-driven culture; leaders need to develop new skills; legacy processes need to be changed; and organizational inertia must be overcome. To succeed in today’s business environment, companies must embrace and develop analytics maturity.

For more than a decade, the International Institute for Analytics has assessed and evaluated the analytics maturity of hundreds of companies, assisting in those organizations’ transition to data-driven decision-making and advanced analytics and AI.

A recurring theme across these engagements, and our own market research, is that companies that have not achieved a level of sufficiency in descriptive and diagnostic business intelligence (BI) prior to their strategic turn to advanced analytics development struggle to develop their advanced analytics competencies.

This reality motivated us to create IIA’s Business Intelligence Maturity Framework as a tool for characterizing the components of a successful business intelligence capability and for assessing the relative maturity of an organization’s business intelligence. For this framework, business intelligence encompasses an organizations descriptive (backward looking) reporting, dashboard, visualization and analytics capabilities while advanced analytics represents its diagnostic, predictive, and cognitive (forward looking) analytics capabilities.

In the third installment of our blog series on BI maturity, we will explore IIA’s Business Intelligence Maturity Market Study. The purpose of this market study is to augment our real-world assessment experience with cross-industry data from companies with known levels of analytics maturity. The goal of the market study is to characterize the capabilities required for BI success, to validate the IIA Business Intelligence Maturity Framework, and to better understand the foundational relationship between business intelligence maturity and analytics maturity. We conducted this market study in partnership with Metric Insights, a leader in enabling organizations to fully leverage their BI capabilities.

Overview and Methodology

To conduct this Business Intelligence Maturity Market Study, IIA and Metric Insights developed a survey to both characterize and capture information about the 10 layers from IIA’s Business Intelligence Maturity Framework and the specific tactics that organizations are using to achieve sufficiency in these areas. The survey was also designed to assess the participating organizations against IIA’s Business Intelligence Maturity Framework. The final survey featured 38 questions designed to capture information on 67 unique business intelligence metrics. The study is designed to examine industry leaders in terms of size, brand, innovation, business intelligence capability, and analytics capability (labeled “BI Leaders”). We ultimately collected data from BI Leaders at 86 companies across 27 different industries.

Key Insights

Insight #1: Companies with advanced levels of BI maturity adopt an enterprise-wide platform.

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Our BI maturity market study included a series of questions designed to characterize the reporting, business intelligence, and analytics infrastructure of the participating companies. There were also questions designed to characterize the data state and capabilities of the participating companies.

The market study reveals that companies with high levels of business intelligence maturity (“BI Leaders”) generally deploy a common data platform (68%) and leverage public cloud and SaaS technologies (80%). While the use of these platforms has enabled uniform reporting and dashboard capabilities for BI leaders, most of these companies are still struggling to successfully deploy self-service capabilities (42%).

Insight #2: Data management and data governance are critical for advancing BI maturity.

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Our BI maturity market study includes a series of questions designed to characterize an organization’s data management and data governance processes. There were also questions designed to characterize the quality of data across several dimensions including accuracy, availability, and currency.

The market study reveals that most BI Leaders have formal governance models, processes, and procedures, with designated personnel to promote and, when necessary, enforce them (80%). In addition, most BI Leaders are leveraging advanced data management techniques such as automated data quality capabilities (84%) and rich metadata (79%).

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While these advanced data management capabilities are consistently used across our BI Leaders, there are still opportunities for improvement. For example, less than half of our BI leaders report high levels of data accuracy (40%), data availability (37%), and data currency (35%).

Insight #3: Adoption and utilization challenges persist in companies with high levels of BI maturity.

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Adoption of BI tools in decision making and utilization of BI tools and assets (dashboards, reports, etc.) remain issues even in high maturity companies. While our BI Leaders report overall adoption rates in excess of 90%, widespread adoption at the strategic (51%), operational (44%), and tactical (63%) decision-making levels shows room for improvement. The lower adoption rates among operational decision makers are also consistent with our experience in assessing large organizations where the “middle management” layer is most disrupted by the widespread adoption of data-driven decision making.

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Figure 1: Which of the following best describes how your organization’s decision-makers have adopted business intelligence tools?

Meanwhile, even BI Leaders still struggle to achieve high levels of license and BI asset utilization. Only 26% of the companies reported high levels of BI tool license utilization (more than 75% utilization over the last 90 days) and only 30% of the companies reported high levels of BI asset (dashboards, reports, etc.) utilization (more than 75% utilization over the last 90 days).

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Figure 2: What percentage of your BI tool licenses or seats do you believe have been used in the last 90 days?

Insight #4: Engaging and supporting stakeholders is critical for advancing BI maturity.

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Almost all of our BI Leaders leverage a variety of approaches and techniques to engage and support their demand-side BI stakeholders (executives, champions, users, etc., of BI insights). For example, 100% of the study companies deploy at least one measure of effectiveness and 80% of companies leverage a formal process for managing demand. In addition, 73% of the companies reported high levels of leadership support.

Insight #5: Business intelligence maturity drives analytical maturity.

To explore the relationship between business intelligence maturity and analytics maturity, we built a preliminary BI maturity scoring model using select questions from our market study survey. The scoring model consists of five stages ranging from “Stage 1 – BI Beginner” to “Stage 5 – BI Master” and is designed to allow comparisons with our 5-stage analytics maturity framework and scoring model.

For comparisons, we calculated BI maturity indices for the industries where we had three or more companies. We then compared the industry BI maturity indices with the industry analytics maturity indices we calculated from previous research efforts.

This analysis indicates that:

  1. The majority of our BI Leaders have achieved high levels of BI maturity.
  2. BI maturity scores are higher than analytics maturity scores across our BI leaders.
  3. BI maturity scores converge with analytics maturity scores as analytics maturity scores increase.

Key Recommendations

The key insights from the market study led to a series of recommendations for advancing your organizations business intelligence maturity. (IIA clients have access to the full report and detailed recommendations).

  • Recommendation #1: Pursue an enterprise-wide common BI platform.
  • Recommendation #2: Be intentional with data management, data governance, and data availability.
  • Recommendation #3: Implement BI lifecycle management.
  • Recommendation #4: Build robust processes for stakeholder engagement.
  • Recommendation #5: Drive self-service initiatives with built-in community, collaboration, and support.

As organizations continue to invest in building their data, business intelligence, and analytics, an understanding of how to effectively deploy these capabilities to deliver business value is critical. Organizations must also consider the foundational importance of business intelligence maturity and its role in enabling a data-driven culture and the adoption of more advanced analytics.

The IIA Business Intelligence Maturity Framework is designed to help organizations assess their current state and characterize the capabilities they will need to develop to advance their BI maturity and build a solid foundation for advanced analytics.

The BI maturity market data presented in this study provides real-world examples of how the layers of the Business Intelligence Maturity Framework can be implemented through technology, data management, data governance, BI lifecycle approaches, automation, platform features, and the proactive engagement with demand stakeholders.