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Lyndsay Wise

Hi and welcome to my blog! I look forward to bringing you weekly posts about what is happening in the world of BI, CDI and marketing performance management.

About the author >

Lyndsay is the President and Founder of WiseAnalytics, an independent analyst firm specializing in business intelligence, master data management and unstructured data. For more than seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay conducts regular research studies, consults, writes articles and speaks about improving the value of business intelligence within organizations. She can be reached at lwise@wiseanalytics.com.

Editor's Note: More articles and resources are available in Lyndsay's BeyeNETWORK Expert Channel. Be sure to visit today!

The role of governed data discovery is becoming increasingly important as organizations manage more complex and diverse data that they want to gain insights from. Self-service BI access and broader data discovery capabilities means that BI is deployed to more users who leverage data in the way that best suits them and not according to pre-defined analytics. Being able to trust this data is essential as it is one of the main ways to guarantee information validity and correct results. Unfortunately, I have worked with several organizations using BI that continue to develop their own analytics, yet admit to knowing about inaccuracies in their data. In these cases, establishing the value of analytics becomes difficult because without trust, it becomes impossible to validate analytical outcomes.

The goal of governed data access to support self-service and data discovery applications is to solve data related challenges and support validated data access. This access can be within a centralized data warehouse, through data virtualization, or by accessing approved data sources external to the analytics framework. With organizations being held more accountable to tie their BI initiatives to business value, the data used to develop insights driving results need to be tightly coupled with data that can be validated through governance.

Achieving this on a systematic level requires developing a strategy and taking data governance seriously. This requires involving the proper stakeholders, defining the processes required, and managing compliance over time. Additionally, the analytics infrastructure needs to support this initiative by providing the framework to manage data quality over time and provide steps to identify issues and support the organization as they try to fix them. Certain solution providers now focus more extensively on providing these capabilities as a part of broader offerings to help organizations overcome their data challenges. As organizations expand their data use and look at broader data sets to leverage as part of their analytics, the importance of data governance increases. Essentially, it is becoming impossible for organizations to ignore the role data governance plays within any BI, Big Data, or Information Management initiative.

Organizations need to ensure the validity and reliability of their data. The only way to do this is to ensure that data governance is an intrinsic part of any data related initiative. More detail on how to do this can be found at: Understanding The Role Of Data Governance
Additionally, here is a Webinar link developed with MicroStrategy that also shows a vendor’s stance on Governed Data Discovery and the importance of integrating a data governance framework within broader BI and analytics solutions: Understanding The Role Of Data Governance

Posted October 1, 2014 4:10 PM
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