Analytics & Insights, Augmented Analytics, Business Intelligence

5 Reasons Your Business Intelligence Solution is Failing You

5 Reasons You BI Solution is Failing You

The right business intelligence and analytics solution can be one of your most valuable competitive advantages. Organizations that make heavy use of customer analytics are 23 times more likely to acquire new customers and 19 times more likely to be highly profitable as a result.

Unfortunately, many companies struggle to reach these goals for themselves. Only 29 percent of businesses would agree that the term “data-driven” applies to their own operations.

As data-driven insights becomes essential to unlocking business value, your company can’t afford to settle for analytics as usual. And as data becomes more complex and analysis of increasingly larger volumes of data takes longer and longer, businesses these days face analytical issues that only intelligent and automated BI systems can address.

Below are the five obvious and not-so-obvious signs that your BI solution is holding your company back. If you see yourself in any of the five situations below, it’s a good sign that it’s time for a BI upgrade before you continue to suffer any longer.

1. Low Adoption of Existing Tools

Although many businesses have deployed business intelligence tools, actually earning user buy-in is another story. Gartner research vice president Cindi Howson estimates that “BI adoption is only at 21 percent — basically flat for a decade.”

One of main reasons BI tools are not used by more individuals or as much as companies would like is because they continue to be too difficult for mere mortals to use. Especially in this digital age when people have become accustomed to business and consumer applications – both mobile and desktop – that offer engaging and informative user experiences, even data visualization tools can appear to make it difficult for users to ask questions and explore their data.

Some of the most common points of failure that we see in BI projects are:

  • Pre-made dashboards fall short. While dashboards are great for monitoring your standard key performance indicators, they are not good for explaining why performance is the way it is.
  • The difficulty of asking follow-up questions. Analytics is a journey — one that starts with a question, followed by many other questions. And visualization tools are still the domain of analysts versus the business users who desire a continuously iterative approach to asking questions that come up.
  • The mismatch between the software and users’ actual workflows. If a tool doesn’t have an intuitive user experience that users are used to, there’s little desire for them to adopt it.

2. Business Users Wait Too Long For Answers

Data analysts perform a vital role for organizations looking to capitalize on their enterprise data. When business users have new questions not answered by pre-created dashboards and reports, or have questions which require complex analysis not easily performed by self-service visualization tools, they turn to their analysts. Unfortunately, the growing demand for answers means that the business waits longer and longer for the analysis they need.

Many companies’ analytical processes are far too manual, too slow, and too subject to interpretation. 72 percent of business and analytics leaders say that they aren’t satisfied with the time that it takes for users to get back the results they asked for. Often, business users want insights that usually require a good deal of in-depth, complex analysis. Root cause analysis, customer segmentation, or customer churn forecasting are by no means easy tasks with spreadsheets and traditional BI tools, especially when using large data sets or data at scale.

The effects of slowness have repercussions far beyond the trenches of data analysis. More than half of IT professionals report that data lag has decreased their company’s performance and reduced their efficiency.

3. Missed Opportunities in Your Data

Typical BI tools present answers to questions that you know to ask and deliver the information that you you ask for in-advance. Most people rely on dashboards to monitor high-level metrics to help run the business.

But when you’re focused on keeping the lights on and the engine humming, it’s very easy to miss things happening right under your nose that require your attention. You are not in the mindset of asking additional questions that might uncover new ways to improve the business. And you may not have an analyst by your side alerting you to interesting patterns in your data, so shouldn’t your business intelligence solution be looking out for you?

BI can’t help you unless you ask exactly the right question. You don’t learn anything outside of what you ask for. So how much of the story are you missing?

As an example, let’s say sales management has dashboards and reports that show overall sales by week/month/quarter/year, and present those in a breakdown by geography, salesperson, or product type. But you would also expect that a sales manager to know that Product A sells best on Mondays. But that’s not a metric that is tracked in a report, so the sales team is not equipped to ensure stock is always available or ready to cross-sell/upsell based on the product’s popularity.

These types of insights are hidden from the business only because no one asked the question beforehand. Therein lies the issue: BI tools only give you a complete picture of your business that you know to ask. What businesses are missing is the business intelligence hidden in their data that they have not explicitly asked about.

4. Data Science Insights Aren’t Accessible to the Business

Data science is often and unfortunately perceived as some kind of “holy grail” for businesses of all sizes and industries. But operationalizing data science insights to the business is difficult.

For companies to get value from data science, machine learning, and predictive analytics, it takes process, culture, and technology. It starts with the selection of a few problems that can be easily solved by advanced analytics techniques, followed by a process of experimentation, testing, and production-ization of predictive models. It also requires a culture of collaboration between the business who holds the domain expertise of operations, executives who must bring realistic expectations of such projects, and technologists who apply machine models to the real world.

If those are not already impossible heights to climb, data science brings a completely different technology stack that the business is used to. Business users often think of anything data and analytics related as business intelligence reports, dashboards, or visualizations. Data scientists utilize a separate stack leveraging programming languages such as R and Python to produce predictive models and predictions. At best, these predictions and recommended actions come in the form of a spreadsheet, and they require another step to integrate into the business intelligence systems for consumption by the business. Next-generation analytics platforms offer a better alternative.

5. Slow Performance from Large or Complex Data

One common scenario that makes data dfficult to work with is when data comes from many disparate sources. This can create bottlenecks and hold-ups, as different departments and groups each have their own processes and requirements for collecting and distributing information.

When you have multiple data sources — especially when data must be exported to spreadsheet files — analysts need to constantly jump from one application to another to extract data. Without a method to automate these processes and unify data, you’ll be on your own to aggregate this data, and then convert it into a format that’s digestible by those who need it.

Oftentimes, your analytics tools are not designed to process and analyze the volume and complexity of your data at the speed you want. What’s worse is that you may have simply accepted your fate and lived with these problems instead of seeking out appropriate solutions to get your business moving again.

Conclusion

I’ve outlined the top five signs that your current BI solution is inadequate for your business needs and objectives. From low adoption of your current tools and lack of speedy insight discovery, each one of these issues is a clear indication that you need a next-generation intelligent analytics platform.

Tellius reduces the friction and eliminates the pain points that organizations experience in all these ways, providing an analytics platform that will accelerate the velocity of analytics for any organization. By leveraging the power of search, machine learning, and a high-performance data platform, Tellius gives everyone across the business the peace of mind that their answers will be fast, accurate, and informative, all while providing your organization with easy and effortless access to insights.

Want to learn more about the signs that you’re due for a BI shake-up? Get our eBook that details the most important indicators that your BI platform is slowing you down and what to do about them: “5 Signs That Your Current BI Solution is Slowing You Down.”

Get eBook - 5 Signs You BI Solution is Slowing You Down

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