BrainBlog for InApps Technology by Jason English
After reviewing the information carefully, senior executives begin to suspect that the reports are wrong. However, the challenge of unravelling where the potential issue lies is daunting:
- Is the report rendering incorrectly?
- Is there a problem in the report logic — or the business logic around it?
- Is there a problem in the data feed into the business intelligence (BI) system?
- Is the data being drawn from the appropriate data warehouse instance(s)?
- Is there an issue in the load, transformation, extraction, or source of the various data feeds driving data into the data warehouse?
The potential for complexity is immense. Just 10 years ago, most large enterprises claimed to have a handful of core data services (three, on average). Even if that picture was a bit rosy, with the advance of cloud, process outsourcing to SaaS and partners, mobile work and connected IoT devices, the total number of possible enterprise data sources has ballooned into the millions, feeding into specialized data aggregators and warehouses that could run just about anywhere in a hybrid IT infrastructure.
We’re in the midst of a data explosion and mission-critical data has burst far beyond the scope of traditional data-quality checks. A new approach is needed to ensure decision integrity: trust in high-stakes decisions that span multiple data sets, systems and business units.
The Market Forces That Got Us Here
So much of a company’s performance depends upon executive decisions. And executives depend on accurate data and business context to inform critical decisions.
Poor data quality and a lack of real visibility into the context of data underneath BI systems leads to decisions that have huge impacts on earnings. Bad BI data is like bad debt and comes at a steep cost. An estimated $3.1 trillion is lost per year in the United States alone due to poor data quality and the costs and labor involved in realigning or repairing the data managers need.
Executives want confidence that they are seeing exactly the data they need — and that it hasn’t been changed or altered inadvertently anywhere in its journey from its source to the dashboard.
Read the entire BrainBlog here.
