As businesses of all sizes rush to make sense of the growing volumes of data they're collecting, they face a variety of business intelligence challenges that complicate efforts to make BI processes productive, effective and useful.
The challenges are shaped by multiple factors, including diverse data infrastructures, data management issues, new types of BI capabilities and varying levels of data literacy in the workforce. On the one hand, BI teams must ensure that proper data governance and security protections are put in place; on the other, they need to demonstrate how BI can benefit workers, including less data-literate ones.
Another set of BI challenges centers around changes in the ways that business intelligence tools are being used in organizations to guide business decisions.
"Traditional BI typically involves curated data and applications driven by IT," said Porter Thorndike, product manager for Tibco Software's WebFocus BI and analytics platform. The traditional approach provides information to business users via dashboards, reports and portals, with well-defined workflows, Thorndike said. In contrast, modern BI initiatives are often driven by business units using self-service BI, data preparation and data visualization tools to hunt for insights.
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In many cases, the challenges start with getting approval and funding for a business intelligence program and developing a solid BI strategy that meets business requirements and can deliver the promised return on investment. In addition to conventional querying and reporting, BI strategies often need to incorporate mobile BI, real-time BI and analytics, augmented analytics and other specialized applications, further increasing the deployment and management challenges.
As they address all the issues, BI and data managers need to strike the right balance between self-service agility and good governance. Faster time to insight can provide a competitive advantage. But that needs to be balanced against data security and privacy concerns and the risk that business users may perpetuate inaccurate findings. As Thorndike put it, "Is the speed at which those insights are generated worth acting upon knowing that some of the insights could be faulty?"
Here's a more detailed look at the top business intelligence challenges for enterprises, plus advice from BI practitioners on how to avoid and overcome them.
1. Integrating data from different source systems
The growth in data sources means many organizations need to pull together data for analysis from a variety of databases, big data systems and business applications, both on premises and in the cloud. The most common way is to deploy a data warehouse as a central location for BI data. Other approaches are more agile -- for example, using data virtualization software or BI tools themselves to integrate data without loading it into a data warehouse. But that's a complicated process, too.
"While the BI tools have the capability to merge data from different data sources on the fly, it still requires a combination of technical skills and data understanding," said Ramesh Hariharan, CTO at consultancy LatentView Analytics. That limits scalability and increases the time it takes to analyze data, Hariharan added. To help speed that up, he recommended creating a data catalog that contains information about data sources and lineage for users.
Other types of data integration challenges require technical tradeoffs. For example, Sameer Dixit, general manager of data, analytics and AI/machine learning at consulting services firm Persistent Systems, said his team ran into problems refreshing Microsoft Power BI reports on the fly from a customer's Google BigQuery cloud data warehouse. The team found it could refresh the reports in memory instead, but Dixit said performance was constrained by the hardware being used.
Dixit's team also faced challenges in harmonizing different file formats used by another client's source systems. It explored creating a separate extract, transform and load (ETL) mapping for each file, but that would have taken too much time, he said. Instead, it wrote a Java application to bring all the files into a common format that could be processed efficiently in a single ETL job.
2. Data quality issues
BI applications are only as accurate as the data they're built on. Users need to have access to high-quality data before beginning any BI projects, said Soumya Bijjal, director of product management at Aiven, an open source data infrastructure platform provider.
But Bijjal added that, in the rush to aggregate data for analysis, many organizations neglect data quality or think they can just fix errors once the data has been collected. "Data quality is one of the most crucial aspects of BI that is often overlooked," she said.
The root cause can be a lack of understanding about the importance of proper data management among users. Bijjal recommended that, when organizations deploy BI tools, they create a data collection process that involves everyone in thinking about how to ensure data is accurate, along with a data management strategy that provides a solid foundation to track the entire data lifecycle.
3. Data silos with inconsistent information
Siloed systems are another common business intelligence challenge. Data completeness is a necessity for effective BI, but Bijjal said it's difficult for BI tools to access siloed data with varying permission levels and security settings. BI and data management teams must break down silos and harmonize the data in them to have the desired impact on business decision-making, she added.
Many organizations struggle with that, though, due to a lack of internal data standards in different departments and business units.
"This is one of the hardest things to overcome because a lot of definitional work needs to be done spanning business functions," said Cameron Cross, technology senior manager at business and IT consultancy West Monroe Partners. For one client project, his team had to assemble senior business leaders in a room and get them to agree on fundamental data definitions, such as what constitutes a pair of eyeglasses.
Inconsistent data in silos can lead to multiple versions of the truth, said Garegin Ordyan, head of analytics at data integration vendor Fivetran Inc. Business users then see different results for KPIs and other business metrics that are labeled similarly in separate systems. To avoid that, Ordyan recommended starting with a well-defined data modeling layer and clear definitions for each KPI and metric.
4. Creating a data-driven culture
"One of the ongoing challenges is around creating a data-driven culture, not just at the executive level, but at the front lines, where the business truly interacts with the world around it," said Sudheesh Nair, CEO of BI and analytics software vendor ThoughtSpot. In his view, building that kind of corporate culture requires organizations to succeed on two fronts: giving workers the right tools and empowering them to apply the insights those tools generate in business processes.
Nair said BI managers need to enlist business leaders from all parts of an organization to help drive a cultural shift that prioritizes the use of data analysis to inform decision-making. Middle managers should also be included to facilitate the change in business operations, he said.
5. End-user training
Training and change management programs related to BI initiatives also require the involvement of business executives and managers to be successful, according to Nair and others.
For example, Chris Fielding, CIO at disaster recovery vendor Sungard Availability Services, said her team worked closely with the company's HR team to develop a BI dashboard with global data on employee head count, new hires and terminations, compensation and other metrics. Completed in 2019, the new dashboard is updated automatically, replacing a manual reporting process that took hours.
The dashboard was quickly adopted by business leaders in HR, and Fielding's team created a simple training program for managers in other departments and business units to promote a rollout across the company. She said that boosted adoption of the dashboard and spurred numerous requests for additional BI applications.
6. Managing the use of self-service BI tools
Uncontrolled self-service BI deployments in different business units can lead to a chaotic data environment with silos and conflicting analytics results that create confusion in the minds of business executives and other decision-makers.
Tibco's Thorndike said most modern BI tools have a data and security architecture that provides a protected place for user-generated analytics to be stored and shared. He recommended, though, that BI and data management teams curate data sets in data warehouses or other analytics repositories upfront to help avoid inconsistences.
"We've found that the key to enriching the self-service experience is to expose these tools to curated data and content, which users can leverage to create much better data flows and mashups," Thorndike said.
However, LatentView's Hariharan cautioned that enterprises need to balance developing standardized metrics and dashboards and enabling users to create their own. That requires careful considerations, he added. For example, when the freedom to explore and analyze data isn't regulated by any governance principles, self-service BI users may publish dashboards that have overlapping KPIs or metrics that are defined differently from one dashboard to another. On the other hand, too much control may hamper analytics innovation and agility, Hariharan said.
In addition, BI tools are often modified with custom extensions that meet specific business needs, Fielding said. Over time, such changes obstruct product upgrades. To prevent that, she said BI teams should work with end users to understand their needs and find ways to deliver required data and dashboards using out-of-the-box functionality.
7. Low adoption of BI tools
End users often take the path of least resistance and look to continue using familiar tools, such as Excel or SaaS applications. "Rather than use the BI tools to analyze data to derive insights, they export data and then perform their analyses elsewhere," Hariharan said. "This results in unexpected usage patterns that are not optimal and low rates of adoption of these tools."
Hariharan recommended continuously monitoring user activity and logs of user requests to identify potential adoption problems and issues with BI tools. BI teams should also aim to deliver continuous functionality enhancements with an eye on boosting user adoption, he said.
If you're just starting a deployment, user adoption often hinges on finding a good use case that quickly demonstrates tangible business benefits and encourages people to embrace a new BI tool, Fielding said.
8. Bad data visualization and dashboard design practices
Data visualizations often go wrong, making it hard to decipher the information they're trying to illustrate. Similarly, a BI dashboard or report is only valuable if it's easy for end users to navigate and understand the data that's being presented. But organizations often focus on getting BI data and the analytics process right without thinking about design and UX.
Persistent's Dixit said BI managers should involve a UX designer to develop an intelligible look for dashboards and reports with a visual interface that isn't cluttered. BI teams should also promote good data visualization design practices, particularly in self-service BI environments. Those steps are especially important for mobile BI applications on smartphones and tablets with small screen sizes.
More tips on overcoming business intelligence challenges
Fielding said she keeps the collective interests of Sungard's users front and center when working with business units on their BI needs. Another issue is that BI applications and dashboards often must be deployed to end users on a continuous basis -- but doing so manually is hard to scale and prone to errors, according to Fielding.
To avoid that, and help reduce costs, Fielding's team has turned to DevOps-style automation. Custom-written scripts make it possible to quickly deploy dashboards in a standardized and repeatable way. "This enables our team to allocate more resources to working on the [project] backlog, rather than the deployment," she said.
BI managers should also create a process for maintaining and updating metrics, data models and dashboards, Fivetran's Ordyan said. That includes routinely looking at what's being used and what isn't and removing items that are no longer needed. "If you don't delete the things that aren't being used, you're going to find yourself in a situation where you have too much to maintain for no good reason," he warned.
Other general advice for successfully managing challenges includes ensuring that your underlying BI architecture can scale and accommodate new tools as needed and that different users have the right tools for their skill levels. For example, augmented analytics features in BI software can help users find relevant data, prepare it for analysis, run natural language queries and create data visualizations.