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Unexpected factors that affect the ROI of business intelligence

By August 2, 2016Article

A comprehensive study by Nucleus Research in 2014 found that average returns for business intelligence implementations amounted to $13.01 return on investment (ROI) per every dollar spent. So why do so many organizations struggle to prove the ROI of their BI initiative? 

Often, this is just a case of improper measurement. In many cases, the BI system enables organizations to actually measure certain processes. But for obvious reasons, there would be no historical data from before the BI implementation; hence it is difficult to evaluate the costs these processes previously incurred. Other times, executives tend to ignore indirect costs such as the amount of time employees previously spent wrangling data in spreadsheets. 

However, the opposite might also be true — stakeholders might become enamored with the newfound savings and efficiencies made by careful review and analysis of their organizational data and fail to see the hidden costs that the BI project itself creates. 

Here are some unexpected factors that can impact the ROI of your business intelligence project.

Hesitancy to take action on data 

The business intelligence tools you have in place could be top-notch, flawlessly implemented, and the analysts and business users employing them could even be producing accurate and insightful results. But to actually realize value from this analysis, you need to actually act on the insights. 

While this might seem obvious, it’s often easier said than done. Entrenched decision makers might see any form of change as a threat to status, territory or control, particularly when their old ways are challenged by “data geeks” that appear to lack the experience and expertise that the old guard has accrued. Hence, they will do anything in their power to prevent any change from being enacted. 

Getting cross-organizational commitment to the project helps ensure success, but it is a multistep process:

  • Proving the value of data-driven decision making to all relevant stakeholders, as a tool that can empower them and make their job easier, rather than replace their ability to make independent decisions.
  • Analysts and other data professionals must adapt to the company’s logic and be able to “sell” their analysis in terms of clear, easily communicable business goals, rather than technical statistical terminology.
  • Taking existing processes into account while gradually shifting corporate culture to make data a more central part of them.

Data preparation 

Some business intelligence tools are sold on their ability to produce captivating data visualizations, which can indeed “wow” organizations into writing big checks. However, when the time comes to actually implement the solution on the client’s data, complications arise. 

When working with complex data, you might find that integrating multiple data sources and ensuring rapid access to large data sets requires extensive IT resources or additional third-party tools. In this case, the costs of your BI initiative could easily balloon. 

To counteract this, organizations should take steps to understand the complexity of their data in advance, as well as the capabilities of the tools they are considering implementing (i.e., end-to-end tools for preparing and managing data versus ostensibly cheaper tools that are, in fact, limited to data visualization). 

Lagging adoption 

An important aspect of ensuring return on investment in business intelligence is to have people actually use the implemented system on a regular basis. Adoption could suffer due to technical limitations or lack of proper training. The solution should be threefold:

  • Make your BI system easily accessible, giving non-analysts the power to do most things themselves. It’s difficult to sell business users on a new system if all it does is show them the same data in a chart rather than a spreadsheet. But if they have the ability to draw their own original insights from the data, they might be more inclined to learn.
  • Invest in making every person in the organization more data savvy. It will pay off in the long run!
  • Have the professional data people available on demand for advanced data modeling, predictive analysis and other types of deeper analytics. You will still need dedicated BI professionals. Making the most of your BI implementation means having at least one such resource available, whether as a full-time employee or in a consultant capacity. 

Failure to properly evaluate savings 

Often, the advantages of business intelligence will not come in the form of a direct increase in the company’s gains but, rather, in savings due to eliminating inefficiencies and freeing technical resources such as IT professionals and developers to focus on their core proficiencies, rather than generating reports. 

While it’s easy to be intimidated by the price of implementing new technology, it’s just as easy to ignore the costs of outdated technology. If the IT organization spends 20 percent of its time writing complex SQL queries, it’s not spending that time on other initiatives that could give the business more digital tools to drive its success. If business users need to wait weeks for each report, they are not acting on data fast enough to actually make an impact. 

To better understand the value added by business intelligence, you first need to have a clear understanding of how much the process currently in place costs. This means carefully documenting the amount of time you spend on data-related tasks and the waiting times attached to each new data inquiry or report. Without having this knowledge beforehand, it will be extremely difficult to evaluate the gains achieved by your business intelligence project. 


The rapid growth of business intelligence over the past decade presents many unique opportunities for organizations of all sizes. However, and similar to any new technology, companies need to see the full picture of the costs and benefits of introducing new tools into their IT ecosystem. 

Successful implementation of business intelligence systems will change many established processes and habits within the organization, which might make it difficult to gain a clear notion of the “before and after” situation and actual ROI of the endeavor. 

However, a clear notion of the capabilities of the new system put in place, dedication to adoption of data-driven work methodologies and systematic evaluation of savings in manpower and technical resources can ensure companies reap the full benefits of business analytics and also produce a more accurate picture of the actual dollar value of BI to the organization. 

Ilan Hertz is head of digital marketing at Sisense, the leader in simplifying business intelligence for complex data. He has close to a decade of experience in applying data-driven methodologies in senior marketing positions in the technology industry.







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