In our last installment of the BI Roadblocks series, we explored the effects of SaaS providers becoming the primary source of customer reports. This time, we’ll look at what happens when that pressure is transferred instead to customer companies’ BI analyst teams.
Large enterprises often leave the majority of reporting tasks to business analysts, data analysts, and data scientists who furnish incoming requests using a ticketing system. Unlike SaaS IT teams, BI analyst teams are hired to build reports for others — but that doesn’t mean they can’t also become overburdened. Analyst teams can become backlogged for any number of reasons, but that overwhelm reflects badly on the solutions they use and can tarnish the vendor-customer relationship. It’s in a BI purveyor’s best interest to consider analysts not just in their product design, but also in their customer success conversations, as BI analysts play a critical role in the software’s utility.
Unlike the last two roadblocks we explored in this series, overburdened analysts are external to your SaaS company and therefore a bit more difficult to detect. These are your customers companies’ BI power users, the ones who use your analytics tool the most. Those of your departments in closest contact with these individuals are likely Support and Success, so check there first for the following symptoms:
- A customer’s BI-related support tickets:
- Are high in volume and/or frequency.
- Convey persistent frustration.
- Contain data management requests or clarification around data structure.
- Request that less technical users be given analytical controls.
- A customer’s success check-ins reveal:
- That BI analysts cannot keep up with reporting demand.
- That reports take too long to create.
- That the tool is too difficult to use.
- That the tool is lacking in functionality.
These symptoms don’t necessarily indicate an overburdened analytics team but should be probed for further information, just in case this user group is indeed facing difficulties.
The root cause of BI analysts growing overwhelmed and backlogged can be either related or unrelated to the BI tools in their arsenal. More often than not, it’s some combination of the two. SaaS vendors can have a direct impact on many of the technological hurdles while consulting customers on navigating the more interpersonal challenges.
- Poor data quality and/or management. This is by far the number-one frustration BI analysts face in their daily work. Wrangling “dirty” or low-quality data takes a great deal of time, but analysts have little choice in the matter. The best they can do is report the issues and hope that they’ll be fixed at a later date. In the short term, they have to compensate for poor quality using tricks and transformations in order to fill the request and pass the right information on to stakeholders. Sometimes stakeholders unearth data quality and modeling issues by singling out a metric that “looks off” and asking analysts to explain how they arrived at it. In either case, BI analysts are made less productive by inadequate data governance practices.
- Too many requests for basic reports. If stakeholders have little-to-no access to analytical tools, even relatively simple reporting tasks fall to BI analysts. Easy though they may be to build, basic reports also tend to be numerous and so similar to each other that they’re almost redundant. If analysts are spending time on year-over-year sales reports, they’ll have less time to work on more strategic, higher-stakes projects.
- Excessive learning overhead. Some BI tools require more training than others, and those that require the most can make it more difficult to onboard new analysts. Some BI solutions require analysts to be well-versed in a number of programming and querying languages beyond SQL and R. MDX (or a proprietary version of it) is often needed in order to query multidimensional databases (also known as MDDBs, data cubes, or just “cubes”). Other BI solutions are part of an extended suite of tools, each with their own language. While these challenges contribute to analysts’ professional development, they can also reduce time to insight, particularly at the outset.
- Trendy but untenable solutions. Analysts are sometimes pressed into using trendy technologies such as machine learning, artificial intelligence, or natural language processing not because they solve a business problem but because the company fears not having them will put it at a competitive disadvantage. Putting the cart before the horse in this way can lead to wasted time for BI teams.
- Unclear project requirements. It can sometimes be challenging for stakeholders to communicate what they want in a report, particularly in terms the analyst will be able to translate into a design. Other times, stakeholders have a question but only a vague idea of what data might help them answer it. It can take a lot of back-and-forth to hone in on requirements and even more back-and-forth to edit the initial drafts.
- Redundant requests. Sometimes stakeholders submit tickets for reports that are already available to them. BI analysts must then take the time to direct colleagues to the report or dashboard they’re looking for.
- Ingratitude. You’ve likely heard the trope that good design is invisible. Well, the same goes for report design. Highly effective BI analysts make reporting look easy: their work is always clean, accurate, and to specification. It can be easy for stakeholders to take BI analysts’ time for granted under these circumstances, particularly if they have little firsthand experience with the data.
Just as BI analysts sometimes take the fall for dirty data, BI solutions (and the SaaS applications into which they are embedded) sometimes take the fall for dysfunctional BI departments. Overburdened analysts not only dilute the benefits of BI for your customers, but also put those relationships in jeopardy. While many of the problems plaguing BI professionals are outside the purview of SaaS vendors and their BI solutions, software providers can play a role in helping their customers correctly identify the cause of their BI backlog.
There’s a lot you can do as a BI-enabled SaaS provider to support your customers’ analysts. Here are some strategies to help BI departments from becoming backlogged:
- Assist in data management and quality control. Depending on how your customer data is entered and stored, you may have some control over its quality and structure. Encourage analysts to reach out with their concerns, and do what you can to clean and organize the data on the backend. If the problem is primarily with how data is being entered, consider making changes to your application’s forms that help prevent dirty data from being submitted in the first place (e.g., data validation, required fields, sample text, tooltips, etc.).
- Supply self-service (ad hoc) reporting to non-analysts. If stakeholders have direct access to basic analytical tools, not only will they submit fewer requests to their BI department, but they’ll also have a deeper appreciation for analysts’ work and be inherently more respectful of their time. Building simple reports and making small changes to those already available in their libraries will also familiarize them with the data, better equipping them to articulate their requirements when they do submit a request.
- Prioritize usability. Low- or no-code BI is not only more accessible to non-technical users, but also makes it easy for BI analysts to learn the software quickly. It also reduces the amount of time they’ll devote to troubleshooting syntax and combing through code.
- Offer counsel. Make yourself a ready resource for other BI-related concerns. Supply customers with best practices for gathering project requirements, for example, or suggest ways leadership might reinforce adoption of existing reports and dashboards.
Supporting your customers means empowering their analysts to meet reporting demands, either through technological solutions or business know-how. Their success with your product will not only boost your brand image, but also help you land additional contracts as they move on to new job opportunities.
Originally published with Software Business Growth.