The McKinsey Global Institute says that data-driven organizations are not only 23 times more likely to gain customers. They are also 6 times more likely to retain them—and 19 times more profitable. There is no doubt that effective data visualization bridges the gap between end users and data. But many companies have yet to harness its power.
Did you know that…
“90 percent of enterprise data is never analyzed”
“61 percent of business users depend on IT teams to analyze data “
“50 percent of information is in silos”
“The adoption rate for business intelligence (BI) and modern analytics is 35 percent”
“The data literacy rate of enterprise decision-makers is only 24 percent”
The importance of user adoption
In a fast-changing world, trends are unlikely to last. They tend to change within a few weeks, which is why agility is more crucial than strength. Anyone slow to react misses out on significant opportunities.
With faster decision-making becoming critical, it is important to have instant access to data and understand it quickly to ensure speedy and informed decision-making. Proper data visualization with accurate data interpretation empowers executives to act decisively and reduce risk.
But many enterprises are failing at user adoption. Gartner* states that organizational BI and analytics adoption is only at 30 percent, whereas a Harvard Business Review Analytic Services survey found the rate at 27 percent. Low user adoption rates remain widespread and affect IT executives negatively.
Reasons for poor user adoption
The most common challenges to user adoption include poor user experience, poor report performance, unavailability of data, and the inability to understand data. These reasons are valid, but they are highly contextual, too.
InfoCepts surveyed analytics practitioners within its customer base to identify the top reasons that are negatively impacting user adoption:
1. IT dependence and lack of self-service
There is a lack of self-service BI capabilities and coordination between teams. The development process is rigid, and there is too much dependency on the IT team.
2. Data access issues
Data is in silos, the required information is not handy, data security is rigid, and there is no access to relevant reports.
3. Data literacy issues
Users lack data literacy and are unaware of design best practices in data visualization. Moreover, they may not access capability development and training in data analysis tools.
4. Lack of insights and usability
Poor interface design and user experience, lack of descriptive and predictive features, and using multiple BI tools lead to inconsistent experiences. As a result, users are unable to dive deeper into data.
5. Process and methodology issues
There is a flawed development process and a lack of a change management. There may be too many tools involved, and the data is not trustworthy.
InfoCepts also found that user adoption gets impacted by the lack of business value and issues within an organization’s people and culture.
Strategies to drive user adoption with data visualization
Bridging the gap between data and users requires the proper use of data visualization. InfoCepts recommends the five initiatives that can improve user adoption for analytics applications:
1. Enterprise information portals
Integrate all analytical assets in a centralized platform to eliminate data silos and simplify data access to non-technical and technical users. An information portal is an interactive application with a centralized repository of dashboards, data, enterprise-level analytics applications, and reports. The secure platform must enable multiple users to quickly access the data relevant to their business functions without relying on third parties.
Information portals must be personalized and enable secure access to be effective. Moreover, they have a modern interface design, provide monitoring and alerts, and allow collaboration.
2. Address data literacy issues
Users who are more data-aware will value analytics. Encouraging a data-driven culture requires prioritizing data literacy. Organizations must also help users understand how to use data and interpret numbers with effective data visualization. Driving data literacy may involve a cultural change along with a technology-driven initiative.
Organizations can implement data literacy with an assessment program, starting with categorizing data users and their roles and encouraging or providing workshops, courses, self-paced learning, and opportunities for online certification. A ‘train the trainer’ program and a 30-60-90-day plan can also help.
3. Enable self-service analytics
As soon as the organization is empowered with data, implement self-service analytics for business users to establish a data-driven culture.
Self-service analytics provides insights on-demand without relying on an IT team. Implementing it calls for a structured and methodological approach. InfoCepts recommends a three-phase self-service enablement approach to address governance strategy, data management, data quality, data preparation, and data security & privacy:
- Phase 1: Focus on the system, use case, and process while defining KPIs, data preparation, and business rules. Create basic reports.
- Phase 2: Focus on defining frameworks and templates.
Phase 3: Focus on governance, ensuring that all the frameworks and processes are followed to ensure self-service implementation.
Successful implementation of self-service analytics can reduce dependence on IT teams, encourage collaboration, enable access to multiple data sources, and simplify change management.
4. Narrate stories from data
Effective data storytelling communicates actionable insights through narrative and visual stories. It is an essential skill that helps users gain and broadcast quick insights from data.
Even when a user has access to data, it is challenging to convey business insights meaningfully. Data storytelling turns data into insights. However, most organizations do not have sufficient skills for effective data visualization to build meaningful reports or dashboards. As a result, users lack actionable insight, take too long to gain insights, or fail to become flexible and interactive.
Operationalizing data storytelling can reduce time to insight while delivering meaningful outcomes. These factors lead to enhanced user adoption. Creating useful dashboards requires knowledge of business and data, understanding how data is represented using the best practices in data visualization and design, and optimizing the use of technology.
5. Implement augmented analytics
Soon more than 50 percent of analytics queries will be generated through search using natural-language processing or voice, or will be automatically generated.
Augmented analytics is a new data analysis approach to automate insights with natural-language generation (NLG) and machine learning (ML). It transforms how analytical content is consumed, developed, and shared while accelerating the time for insights in a business. These insights let users act fast on data and make critical decisions.
Augmented analytics impacts user adoption by simplifying analytics and making them easier to use. It is a crucial feature of modern BI and analytics, self-service, data science, and data preparation platforms. Automated insights can be embedded into conversational analytics and enterprise applications to mainstream their usage.
InfoCepts can operationalize next-generation BI capabilities using commercial out of the box tools or a custom development approach that seamlessly integrates with your preferred enterprise BI tools. It also applies a persona-based implementation strategy to focus on the user during development, ensuring that specific requirements get addressed.
Learn more about how InfoCepts improves analytics adoption in this eBook.
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