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Analytics Center of Excellence when Everyone Considers Themselves a Data Scientist

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In a Forrester Research study called “Balance Self-Service Analytics and Governance to Achieve Business Success” they point out that powerful analytics tools have become visual, accessible and user friendly. In this environment of self-service analytics and data discovery employees can quickly derive greater insight into business performance on their own. Business people no longer want to turn to IT or an Analytics Center of Excellence for analytic capabilities.

What is the risk of this do-it-myself orientation?

All of these issues can reduce the effectiveness of data driven decision, inhibit economies of scale and result in higher costs of operations. Quicker may not mean more accurate.

  • New Set of Analytic Silos
    Individuals with their own data don’t align or communicate effectively with each other or across departments.
  • Lack of Standards for Models
    Are they statistically correct in their predictions or are the numbers massaged for desired results or from lack of statistical training or expertise.
  • Data Security
    Is privacy compromised and legal and regulatory compliance being met?
  • Data Quality
    Is there a comprehensive and accurate database? Cleaning data is typically a data scientists least preferred part of their job. Will the quasi-data scientist go to the trouble of testing their data?

 

How to create an effective Analytics Center of Excellence (ACE)?

  • Innovation Enablement
    A focus on desired outcomes that support business objectives like competitive advantage, profitability insights and transforming business processes. An R&D approach to explore new analytic hypotheses, rapidly see new information sources and quick development of new analytic models.
  • Business – IT Productivity
    Centralized IT results from technology cost savings but centralized IT reporting has failed to satisfy internal clients. Too much distrust, duplicated cost and effort and missed business opportunities doomed this model. Shared data assets without rigid governance allows innovation. Embrace decentralized development, knowledge sharing and user enablement through pooling resources.
  • Standards and Repeatable Analytics
    Economies of scale and improving standards should be the goal of any ACE. Shared data sources, offering analytic and dashboard templates, ensuring analytic consulting and defining a common metric framework are requirements of a successful ACE.

While the Analytics Center of Excellence needs to adapt to the self-service trend, it still can provide structure, collaboration and value.  Interested in a position in analytics or data science?  Contact the recruiters at Smith Hanley Associates.

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