Infographic: Strategies for Driving Adoption and Usage with BI and Analytics

BI Adoption Trends: Learn From the Companies That Do It Best

Although adoption rates for BI/analytics tools remain stuck in the 20% range, usage is increasing. Usage growth is primarily fueled by “off-license” usage from front-line workers using BI/analytics output embedded in operational applications as well as external users (e.g., customers and suppliers) using external-facing reports and dashboards. These new usage trends are most prevalent among leading adopters of data & analytics (e.g., best-in-class companies) as well as North American companies, which are traditionally more aggressive in adopting new technologies and approaches than their European counterparts.

In addition, new self-service tools, such as GUI-based authoring and data preparation tools, are making it easier for businesspeople to service their own data needs without IT assistance. Also, data catalogs make it easier for these business users to discover useful data, and new ad hoc query capabilities – namely BI search and augmented analytics – are starting to propel higher levels of BI/analytics adoption and usage.

At the same time, organizations are applying 30 years of hard-won knowledge about how to overcome barriers to adoption and usage. Specifically, organizations are implementing data governance programs and data quality workflows to improve data accuracy, completeness, and consistency. They are launching data literacy programs with coaching and support networks to improve knowledge and skills required to use BI/analytics tools effectively. Most importantly, executives are becoming more data-driven, providing leadership, funding, and personal examples to foster a robust culture of data and analytics usage.

Global survey

To investigate BI/analytics adoption, BARC and Eckerson Group conducted a global survey of 214 data & analytics leaders in November and December of 2021, drawing respondents from organizations around the globe of all sizes and in many different industries. More than a third (36%) had more than 5,000 employees, 29% had between 500 and 4,999 employees, and 36% had less than 500 employees. More than two-thirds of respondents (70%) were from Europe, while 19% were from North America and the rest from South America, Asia Pacific and Africa.

More than half of respondents (51%) were executives, VP/directors, or managers. The following is a list of the percentage of respondents by role in descending order: manager of BI, Analytics, ML/AI, or Data Management (30%); VP/Director of BI, analytics, ML/AI, or data management (13%); architect of BI, analytics, ML/AI, or data management (13%); consultant or vendor on behalf of a current client (13%); analyst of BI, analytics, or ML/AI (12%); executive (CXO) (8%); engineer of data, analytics, ML/AI (8%); and other (5%).

Key takeaways

• Adoption
  The percentage of employees actively using BI/analytics tools is currently 25% on average, reflecting minimal growth in the past seven years weʼve been tracking this metric.
• Usage
  At the same time, 50% of data & analytics leaders say BI/analytics usage has “increased a lot.”
• Technical drivers
  The primary technical drivers of increased usage are “self-service authoring tools” (73%), data preparation tools (48%), and “embedded BI/analytics” (38%).
• Business drivers
  The primary business drivers of increased usage are “change in data culture” (51%), “new data-driven executives” (50%), “digital transformation or other strategic initiatives” (50%).
• Regional and other variations
  These drivers are more prominent among North American companies and leading adopters of data & analytics (best-in-class companies) by a significant margin.
• Barriers
  The primary barriers to adoption and usage are “lack of proper training” (50%), “lack of quality data” (41%), “budget issues” (36%), and “ease of use” (33%).
• Adoption killers
  There are certain things that almost instantaneously kill BI/analytics adoption and usage:
  1. the data needed is not available or accessible
  2. the data isnʼt trustworthy
  3. the tools arenʼt flexible or easy to use
  4. query performance is slow, and
  5. there arenʼt enough people to coach or support business users
• Adoption drivers
  On the other hand, BI/analytics usage is bolstered by:
  1. data-driven executives
  2. comprehensive training and support programs
  3. tailored self-service tooling
  4. embedded analytics
  5. comprehensive data governance
  6. analytics centers of excellence, and
  7. agile delivery of high-value solutions.

Ten recommendations

Consider these 10 recommendations for improving adoption, usage, and value of BI/analytics tools and creating a successful data & analytics program:

• Tailor self-service
  Know your users and deliver what they need, even if you have to build it centrally. For 60% of business users, tailored parameterized dashboards are the epitome of self-service.
• Govern self-service
  Self-service BI/analytics implemented without governance or knowledge of user requirements will strangle a data & analytics program.
• Power users first
  Focus on meeting the needs of power users first to develop useful data models and structures that other users can leverage. But donʼt let power users dictate choice of tools, reports or dashboards provided to regular business users.
• Tear down the data silos
  Data that is available but not accessible or a general lack of data are major reasons for lack of tool use. Understand the need for data and consider how it can be captured or how to overcome the organizational barriers of closed data silos.
• Data quality at all costs
  Move mountains to deliver data that users trust. Certify reports, implement data governance, build data quality rules and workflows, report on data quality, and partner closely with source system owners to improve data entry and systems notifications.
• Embed analytics
  Turn operational workers into just-in-time analysts by embedding charts, tables and dashboards into ERP/CRM applications, portals and other run-the-business applications.
• Look externally
  Service users in your organizationʼs ecosystem. For example, you can improve customer loyalty by providing them with data and insights about their activity with your company. These data products for customers, suppliers and others can help improve the bottom line too.
• Create an analytics center of excellence
  Whether you centralize or embed data analysts, teach these individuals enterprise standards using data and how to communicate with business managers. Align analysts with business units and rotate them periodically.
• Go beyond training
  Training is critical, but coaching and support create a culture of analytics. Build peer communities for both power and casual users to spread knowledge and excitement about how to use data to achieve business goals. Improving data and analytics competence should not be the sole purview of power users, but lift the data literacy of everyone in the organization.
• Work top down and bottom up
  Find or cultivate data-driven executives who lead by word and example. At the same time, organize departmental managers who feel the pain of poor quality data and insights into an Analytics Council that sets standards and pushes for change.

Infographic of the key findings

In vielen Unternehmen stößt die Nutzung von Analytics, trotz Initiativen zur Bereitstellung von Self-Service-Tools und zur Modernisierung von Datenplattformen, an ihre Grenzen. Dafür gibt es viele Gründe: Die Tools sind zu schwer zu bedienen, die Abfrageleistung ist langsam, die Daten enthalten Fehler und so weiter. Die Quintessenz ist jedoch, dass Unternehmen nur dann einen Nutzen aus Analytics ziehen und datengesteuert werden können, wenn die Beschäftigten Analysetools verwenden und den Ergebnissen entsprechend handeln.

You can access the full study here