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Art of BI: The 6 Greatest Data Integrity Business Challenges

Christian Screen | | September 26, 2017

data warehouse

Clean, correct, consistent, reliable information is the holy grail of data-driven organizations, one of the most important assets that a company can have. In order to achieve data integrity, however, you’ll first have to surmount a number of obstacles. If your business is struggling with any of the challenges below, it might be time to take proactive steps to address the problem.

Multiple Sources of Data

Of course, any non-trivial data repository will have information coming from a variety of places, from the company intranet to public records. Gartner estimates that by 2019, three-quarters of all analytics solutions will rely on 10 or more external data sources. These separate pieces must first be checked against each other to ensure their consistency and then integrated within a single centralized hub.

Multiple BI Applications

Not only do organizations contend with pulling data from multiple sources, they also have too many tools to analyze it once it’s in their possession. Often this happens when business and IT teams don’t communicate well or understand what the other half is doing, resulting in software purchases with overlapping functionalities. Using multiple business intelligence applications both squanders money and is also inefficient, duplicating a good deal of effort.

Manual Data Pulls

At best, manual data collection is a tedious task that’s a waste of your employees’ valuable knowledge and experience. At worst, it’s a grave mistake that puts the performance of your business in danger. The 2012 “London Whale” disaster at JPMorgan, resulting in $2 billion in trading losses, was partly caused by errors in manually copying and pasting Excel data. Unless you’ve fully automated your data flows, manual data collection is probably creating issues for your organization: a 2008 study at the University of Hawaii found that 88 percent of non-trivial spreadsheets contain errors.

Inconsistently Built Reports

Problems with manual processes can affect more than data collection–they also result in inconsistent reporting. Manually-built reports may be inconsistent in a variety of ways, including their authors, their intended audience, their release schedule and the information they contain. If different departments have different standards for their reporting, this can also create issues for collaborative cross-functional teams.

Excel Dependencies

Microsoft Excel is the gold standard for productivity software, but it’s being used for much more than its creators intended, including complex business process management tasks. Excel simply isn’t appropriate for doing more complicated data analysis. According to a 2016 study by financial software company Accountability, 72 percent of CFOs believe that their organization relies too much on spreadsheets. Another report by IDC found that advanced spreadsheet users waste eight hours of time on repeatable work every week.

Lack of Best Practices

If you’ve ever struggled to break down departmental “data silos,” you know how detrimental it can be to isolate the different parts of your business from each other. In order to ensure data integrity, organizations need to maintain interdepartmental consistency in terms of best practices for data collection and analysis. Without a standardized workflow for data processing, each division will withdraw into itself instead of sharing valuable information with other departments.

Final Thoughts

Those organizations that prioritize data integrity are the ones that are best positioned to thrive in an increasingly complex and data-driven business landscape. How can you solve your data integrity challenges? Read Datavail’s new white paper, “When the Data Doesn’t Match Up – How to Get Business Insights You Can Trust,” to learn how a data warehouse can ensure data integrity, consistent reporting, and business decisions that will move your company forward.

In addition, please register for a live webinar on September 27, Data Availability and Data Confidence at 1pm EST.

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