While Business Intelligence remains a top focus for organizations across the globe, many still don’t fully understand it. They believe common myths and misconceptions surrounding BI–which lead to BI projects that don’t meet expectations or fail altogether. In this article, we explore the most common BI myths and explain why they’re false.
Over the last few years, Business Intelligence (BI) adoption has exploded. As businesses create (and have access to) more data than ever before, they’re turning to BI to capitalize on that data.
The only problem: BI is still surrounded by confusion, and many businesses still don’t quite understand it. Driven by hype and false claims, they hold unrealistic views of BI. They believe common myths surrounding the topic.
The bigger problem: When businesses believe these misconceptions, they run into issues. These myths can lead to implementations that don’t meet expectations, or flat-out fail.
Today, let’s explore a few of the most popular myths surrounding BI, and explain why they’re false. While the list could certainly be longer, here are 5 of the most BI common myths:
Myth 1: “Excel is a universal BI tool. It has served us for years.”
Spreadsheet programs (like Excel) are the most commonly misused/abused software programs in the business world. They’re used for everything, from budgeting to time tracking to CRM and so much more.
But, the biggest misuse of spreadsheets occurs in Business Intelligence and Reporting. It starts small. Employees turn to spreadsheets to pull data together and create simple reports. Soon, these spreadsheets turn into multi-user reports, getting shared across the whole department. Before they know it, the business finds themselves relying on spreadsheets for all of their BI.
The problem: Excel was not designed as a multi-user, multi-department BI tool. While many use it in this way, it creates risks and is ultimately the least efficient tool for the job.
“Excel CAN be enough, but in terms of the cost of manpower is it ultimately the least expensive option?” says Stephen Rasey, Ph.D. — Business Intelligence Architect, Entrance Consulting. “Excel is a BI tool in the same way a piece of worked flint is a knife; they are both the products of the end user, can do the job and put food on the table. Today, however, there are sharper knives in the drawer to make quick work of big game. There are powered knives for assembly line work.
Milton Friedman was told by officials that it was better to dig ditches with shovels than with bulldozers, because the bulldozer was expensive and more jobs were created using shovels. Friedman replied if the goal was to create jobs, then they should use spoons; if it is to dig ditches, then they should use a bulldozer.”
Myth 2: “We just need the right tool”
It’s a misconception that we see time and time again. Many organizations assume that they’ll find a BI tool that will fix all of their data and reporting problems. When the project fails, they blame it on a bad tool.
Some go a step further, and buy multiple tools when they discover that the first one didn’t fix their problems. After a while, they’re stuck with many different tools–none of which helped them.
The reality: Successful BI projects start with clean, organized data. Without this, no amount of BI software will give you the insights you want.
“One common idea I hear in business circles is that with the right tool or software, we will suddenly gain actionable insight into operations from data,” says Daniel Bliley | Director of Marketing at Passport. “For a lot of BI software to work effectively, however, you have to start with an understanding of what data you need to collect and how to properly extract it. Just because you have information that you collect as a business, doesn’t mean analytical software will provide answers to your organization’s problems and questions. It must start with what you are trying to accomplish, rather than the notion that having data will lead to results once it is sorted.”
Going one step further, you’ll find a similar myth surrounding self-service BI tools. Many believe that they can place a modern self-service BI tool over their enterprise systems (like an ERP system), and let end users create reports by themselves immediately.
The fact is, it’s not that simple. Self-service BI needs data that’s in a user-friendly format–typically requiring a data warehouse. Unless you license a BI tool that includes ETL and data warehousing capabilities, this must be handled before using the self-service BI tool.
Myth 3: “Business Intelligence is just a software project”
Business Intelligence is often viewed as any other project. Gather requirements, buy software, deploy the software, train the users, and measure results. Once that happens, you’re done…right? Not at all.
BI goes beyond a one-time project. To be successful, you need buy-in from all levels of your organization. You need to recognize the fact that the BI project doesn’t end when the software is deployed. As explained below, the “BI project” is a journey, and will pay off in a big way if approached as such.
“Businesses to whom a formalized or methodical BI practice is new can experience a rude awakening around their “investments,” often after spending significant sums on software, training for staff, and consulting for solutions development,” says Douglas Briggs, Director, Business Intelligence at Washington University in St. Louis. “They are surprised to find that writing a check and waiting patiently is not enough to deliver the kinds of results BI is famous for: insights and recommendations to lower costs, improve processes, and identify opportunities. The truth is that BI isn’t a goal to achieve, it’s a journey that represents a decision at every level of the organization to treat data differently: as an asset. Accomplishing that culture shift takes time and repeated commitments to allow a BI practice to grow into greater and greater levels of maturity, to permeate decision making throughout the enterprise, and to become a part of the fabric of the company’s very philosophy. When a company starts every meeting with someone asking “What do the analytics say?”, that’s one of the key signs that a company’s BI investment has begun to pay off — not because the BI team has produced particular results, but because the company itself has subscribed to the premise of decisions based on careful analysis of the company’s data assets.”
Myth 4: “BI software always includes reporting”
Modern BI software has become synonymous with data visualization. It takes your data and turns it into fancy graphs, charts, and maps.
Now, don’t get me wrong–I’m not insulting data visualizations. They’re quite useful, and help businesses make sense of their data.
However, this is where many BI tools end. They provide data visualization, but lack the reporting capabilities that businesses need, like operational reporting, batch reporting, financial analysis, and more. The problem is, many assume every BI tool offers these features out of the box–and are sorely disappointed.
“Many companies expect business intelligence to provide all of the answers related to their business objectives and provide complex insights related to their data,” says Dev Tandon, CEO and Founder of The Kini Group. “However, most business intelligence solutions primarily provide data visualization. It takes a lot of customization to get “self-service” BI to those capabilities. While off-the-shelf BI does provide analytics, very few solutions can tell you how to look at your data or where to look for specific insights. They also fail to include advanced reporting to keep these insights coming. Unfortunately, that’s what most companies need, and that’s what most companies think BI provides.”
Myth 5: “BI is too expensive for our needs.”
Many make the assumption that BI is only for big organizations with big budgets. They falsely believe that it goes far beyond their needs.
So, they ignore BI altogether, or go on using manual processes (usually with Excel). The problem is, they fail to consider the costs of this approach. How much time do they waste pulling data together manually and creating reports from scratch? How much does decision-making suffer when you can’t easily access your data?
The fact is, BI investments vary greatly. But, don’t assume that avoiding BI software is the cheapest approach. In some cases, handling BI manually is the most expensive option.
“BI can be costly,” explains Rasey. “The price tag depends upon need. There are “free” BI tools available in R packages, but the human cost to develop and run your tools successfully and reliably in a production environment is non-trivial. On the other hand, there are BI tools that are not cheap but can project your analysis on the world wide web in public or in highly secure intra-web companies with minimum daily effort.
Whether you need a go-cart, a pickup truck, an 18-wheeler, or a 747, the cost depends upon the mission and need.
BI tools and training is an investment. The payoff in BI is that it will enable the Business to reduce the time and cost of existing reports and analysis processes, replace slow expensive process with whole new approaches. BI can make feasible some desirable processes, like forecasting with uncertainty bounds, which were too slow or horribly expensive to contemplate before the investment in BI.”
Summary: While this list could certainly go on, the points listed above are some of the most common BI myths. What do you think? Would you add anything to the list? If so, please feel free to share in the comments.