Almost every modern company aims to be data-driven, but it’s tough to make a digital transformation without the right tools. Business Intelligence (BI) tools are essential for organizations looking to collate, analyze, and report on big data from multiple sources.
Still, despite the near-ubiquitous demand for data solutions, many businesses continue to rely on tools like Excel to process data. As leaders hear horror stories of drawn-out implementations and low adoption rates, they feel immense pressure to either stick with the status quo or find the perfect solution for their businesses. For many of these leaders, picking the right BI tool isn’t as clear-cut as it seems.
One of the primary choices to make is whether you need a traditional vs self-service BI model. Before you decide which option is right for your business, there are some technical and logistical considerations you should think about first. Here are some common factors to consider before choosing a BI tool.
BI tools are an important component of your data governance strategy. With the right processes, these tools can easily be set up to manage high compliance reporting demands. However, traditional tools are often favored over self-service tools in organizations with strict compliance needs.
Self-service tools make it easy for anyone with the right permissions to run reports, analyze data, and garner insights, even if they don’t have an analytics or IT background. Without enforcing best practices or developing robust data management processes, the environment is ripe for inefficiencies.
When people across the enterprise can freely add new data sources, share reports with external stakeholders, and slice and dice the data at will, it can pose a security or credibility risk to your single source of truth.
Meanwhile, a traditional BI tool limits access to experienced team members in your IT department and data specialists. Fewer users make it easier to ensure procedures are followed, so data is clean and accurate for compliance reporting. While this can be achieved with self-service tools, too, it requires more detailed team training and more robust procedures to maintain data integrity.
IT or Analytics Team Bandwidth
While traditional BI tools can maintain a more reliable single source of truth, it comes at a cost for many overwhelmed IT and analytics departments. With a traditional BI tool, teams across the enterprise request reports from IT or dedicated data specialists.
Depending on the size of your organization and your current structures, this can quickly lead to chaos for your analytics experts.
Self-service tools are designed in part to solve this bandwidth issue. By offering access to other teams, they can skip the middleman and run reports themselves. Ultimately, culture plays a big role in whether or not your teams will adapt to analyzing their own data and running their own reports. For many seasoned organizations, the standard work style may not align with a self-service method.
Many BI tools make creating reports and modeling data easier than ever, but that realization may lead to other teams requesting more unnecessary reports or self-generating reports that don’t align with your company’s reporting standards. There may also be more requests into IT to edit or change dashboards or add new data sources, too.
Look at the current workload for your IT and analytics departments and see if they can take on an influx of reporting requests. Consider that part of your IT team will still be responsible for managing the tool’s health and performance. If your teams don’t have the bandwidth, it benefits your company to adopt a self-service model with robust training and clear best practices.
As you can guess, requesting reports from IT can present a bottleneck. If you’re committed to becoming a more agile company, a legacy BI tool with more traditional management may not support your goals.
One powerful component of modern BI tools is the ability to slice and dice real-time data on demand. While any BI tool can help you make more data-driven decisions, a self-service tool offers the unique benefit of reviewing dashboards and modeling data on demand for faster decision-making. Plus, when new information is needed to make a choice, well-versed employees can present real-time data from their computer within a meeting, rather than asking IT to generate a new report for them.
Analyzing and modeling data on the fly is essential for fast decision-making, especially if IT has a reporting backlog. Self-service models can also present unique opportunities for future-driven predictive and prescriptive modeling—even with unstructured data—that are limited within traditional BI models, which rely on structured data. Still, these capabilities come at a cost, and the unstructured data can bring reporting results into question.
Along with compliance, security is one of the key reasons an enterprise would typically opt for a traditional BI model.
A traditional BI tool is an on-prem solution, leaving health and performance solely in the hands of your IT department. However, if your enterprise has highly sensitive data or data security concerns, a traditional BI tool ensures that your data is stored securely on site.
While traditional BI tools can sap IT resources, they’re also easier to monitor for security concerns. Cloud-based self-service tools present security concerns for some organizations, especially teams that primarily use legacy on-prem tools. Some IT teams may worry about observability across the tech stack as they introduce and integrate new cloud-based tools.
A robust data governance strategy is a must to maintain security expectations with either type of tool. However, you may need to put more procedures in place to reduce security risks with a self-service tool.
Is Traditional BI or Self-Service BI Better For You?
Companies know they need better access to their data to make decisions. While the real-time capabilities of self-service BI are tempting, these tools may require more substantial changes than your business is ready for.
The best BI tool for your business is one that gets used. Before you jump into implementing a new tool, make sure you examine how your business operates and make an informed choice. Review these technical considerations with your teams to discover which tool is best for you.