Data-Driven Organization

A Data-Driven Organization is an organization that … data-driven maturity.

• Context:
  • It can (typically) require a Data Culture (e.g. promoted by a Chief Data Officer.
  • It can (typically) require well-defined Key Performance Indicators.
  • It can (typically) have Data-Driven Systems (that solve data-driven tasks).
  • It can (typically) have Data Science Teams.
  • …
• See: Data Scientist, Chief Analytics Officer, Data Science Maturity Model.

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References

2015

• (Anderson, 2015) ⇒ Carl Anderson. (2015). “Creating a Data-driven Organization.” " O'Reilly Media, Inc.".
  • QUOTE: What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture.

2013

• http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/three-keys-to-building-a-data-driven-strategy
  • QUOTE: In our work with dozens of companies in six data-rich industries, we have found that fully exploiting data and analytics requires three mutually supportive capabilities. First, companies must be able to identify, combine, and manage multiple sources of data. Second, they need the capability to build advanced-analytics models for predicting and optimizing outcomes. Third, and most critical, management must possess the muscle to transform the organization so that the data and models actually yield better decisions. Two important features underpin those competencies: a clear strategy for how to use data and analytics to compete and the deployment of the right technology architecture and capabilities.
    1. Choose the right data … That means upping your game in two areas.
       • Source data creatively .... Often, companies already have the data they need to tackle business problems, but managers simply don’t know how they can use this information to make key decisions.
       • Get the necessary IT support. Legacy IT structures may hinder new types of data sourcing, storage, and analysis. ...
    2. Build models that predict and optimize business outcomes
       • Data are essential, but performance improvements and competitive advantage arise from analytics models that allow managers to predict and optimize outcomes. More important, the most effective approach to building a model usually starts, not with the data, but with identifying a business opportunity and determining how the model can improve performance. We have found that such hypothesis-led modeling generates faster outcomes and roots models in practical data relationships that are more broadly understood by managers. Remember, too, that any modeling exercise has inherent risk. Although advanced statistical methods indisputably make for better models, statistics experts sometimes design models that are too complex to be practical and may exhaust most organizations’ capabilities. Companies should repeatedly ask, “What’s the least complex model that would improve our performance?”
    3. Transform your company’s capabilities
       • The lead concern senior executives express to us is that their managers don’t understand or trust big data–based models and, consequently, don’t use them. Such problems often arise because of a mismatch between an organization’s existing culture and capabilities and emerging tactics to exploit analytics successfully. The new approaches either don’t align with how companies actually arrive at decisions or fail to provide a clear blueprint for realizing business goals. Tools seem to be designed for experts in modeling rather than for people on the front lines, and few managers find the models engaging enough to champion their use — a key failing if companies want the new methods to permeate the organization. Bottom line: using big data requires thoughtful organizational change, and three areas of action can get you there.

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