Research Shows Analytics Pays Off, but Data Work Provides the Foundation

Loraine Lawson
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How to Sell Senior Management on a Data Warehouse

The real benefits of data warehousing are indirect: the ability for your company to make better, faster decisions that will save money and increase revenue.

Investing in business intelligence (BI) and analytics doesn't just pay off in a nice ROI; these projects actually give you a better ROI the longer you use them, it turns out.


That's less obvious than it sounds, because as Hyoun Park, principal analyst at Nucleus Research, points out, enterprise apps tend to start out with a strong ROI that diminishes over time. So, it's actually unusual that analytics would add more value the longer and more broadly it's used.


Nucleus Research found that enterprises attained an average of 188 percent in the initial automation phases of analytics, reports Enterprise Apps Today. That ROI rose to an average of 1,209 percent in the later predictive phases, according to the article.


The report looked at 58 case studies from different industries that used analytics tools over five years.


"The more companies broaden and deepen their use of analytics, such as BI, PM and predictive analytics, the greater ROI they see - that's the main take-away from our research," Park told Enterprise Apps Today.


I guess that's good news. But I hope CIOs and IT managers remember that BI and analytics programs do not happen in a vacuum. Data integration, data quality, data governance, master data management - these are the foundational pieces for successful analytics.


In fact, Jane Thomson, the executive director of EOH, says up to 80 percent of large BI projects that fail do so because of bad data. To me, that says it's oh so important to realize that part of that impressive payback should be attributed to the data management work that supports BI.


Nucleus Research acknowledges that these projects don't stand alone in its description of the four stages of business analytics. Stage one is marked by building data warehouses and data cubes.


Stage two includes an expansion of "data management capabilities to include data migration, data integration and better data quality control." Stage three adds advanced data governance tools and practices, as well as the use of metadata, while stage four adds Big Data and the ability to integrate and manage large data sets.


So, let's hear it for the data team and their work when we're celebrating successful BI.


And, on a practical note, if you're looking for a way to justify a data quality, data governance, MDM or other data-related initiative, don't forget to calculate in the ROI from your analytics program.

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