Imagine a typical day in the office of your average BI team. Data is processing, information is moving through various systems, and reports are generating valuable insights for the decision-makers down the line.
Then a BI reporting nightmare crops up – a significant mistake has been discovered in a data field. None of the BI professionals on the team have any idea where this issue originated.
Suddenly, that “typical,” quiet day looks very different. Anxiety and uncertainty descend. The process of uncovering the error throws the team into chaos and unrest as current tasks become sidelined. The process of locating the error manually begins. Unfortunately, this may take weeks at a minimum.
Anyone who has spent time in the BI field has experienced this kind of emergency. But, before you imagine the worst, it doesn’t have to be like this. Using modern automation tools, teams can now find and fix these mistakes in a matter of minutes.
Automated Platforms Make BI Manageable
Performing BI metadata management manually is a complex operation requiring many dedicated hours to accomplish. Instead of wasting precious time performing these tasks by hand, automation comes into play in order to increase BI teams’ effectiveness. These are three critical capabilities that help boost BI efficiency, compliance, and enhanced organization:
- Data Lineage – Automated data lineage finds and traces the entire data journey, from error-to-origin.
- Data Discovery – BI teams can locate metadata instantly, even if it is scattered across many different systems. This allows data to be easily found and properly organized.
- Business Glossary – Using an automated business glossary platform ensures that all business terms within the organization are streamlined and consistent.
By leveraging these three capabilities, BI teams can gain a clear look at data health and this allows them to sift through data faster to find and resolve errors.
Automated Data Lineage and Discovery Make Finding Data Errors a Breeze
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Automation Helps BI Teams Meet Goals
Without implementing automated platforms, creating a business glossary, performing data discovery or tracking data lineage can become a lengthy and time-consuming process. This, in turn, impacts every task down the line. A recent Octopai poll found that 83% of BI and analytics professionals reported they expect their manual impact analysis to take weeks. Further, 50% reported that manual error detection could take months.
In terms of compliance, utilizing automated BI reporting platforms is now even more vital. With the numerous new global regulations in place, companies must have a clear understanding of their data and the information they possess. GDPR and CCPA laws governing customer data in Europe and the United States require companies to produce customer data documentation for periods that can last for months. BCBS-239 financial regulations depend on data lineage which requires BI analysts to trace their data for years.
BI Reporting Best Practices
With these tools in place, the time investment of data processing is reduced. But, this is just the beginning for BI teams looking to overhaul manual analytics. These powerful tools also package and deliver better reports for end-users.
BI teams should implement and utilize business intelligence best practices when analyzing the data and preparing automated data processing reports. These include:
- Identifying the business value within data requests
- Making enterprise-wide integration a priority
- Scheduling regular meetings with key stakeholders
- Setting clear BI targets
- Understanding requirements for different departments
By keeping the Analytics Department’s relationship and the rest of the organization in mind, BI teams can deliver more actionable insights in their reports. These guidelines can be achieved and upheld by every BI team throughout the company. With these regulations in place, enhanced reports will be generated across the enterprise.
Automation Saves BI Teams From a Potential Disaster
With the implementation of an automated BI intelligence platform, a typical-day-turned-nightmare goes a little different, if it occurs at all.
Automated data discovery tools increase the effectiveness of BI teams by eliminating the opportunity for errors in the first place. If a human-error or a bad-data input problem occurs, automated data lineage traces the entire lifespan of data points to isolate the mistakes. Instead of sidelining a BI team for weeks, errors are found and solved in a fraction of the time.
