Data Literacy as an Accelerator

Posted in: Business Insights, Technical Track
data literacy
Many organizations experience a stall during their data transformation journey. The stall is caused by a lack of skills and shared language across the organization needed (to) experiment and rapidly shift approaches so they can optimize their nimbleness.

This stall manifests in several ways, including a differing maturity level between teams causing a divergence in technical approaches, an inability for data products to be consumed by certain teams and an inability for team members to meaningfully contribute to identifying, deploying and testing new approaches in their respective market.

It is then amplified while teams hunt for difficult to locate data, struggle to rationalize conflicting or unclear policies, and struggle with the challenges of tech debt and difficult or complex systems implementations. Each of these can be addressed through better awareness, more consistent communication and constant reinforcement of organizational norms and operating models.

Data literacy is about raising the bar for the entire organization as it relates to data awareness, consumption, creation and understanding. To avoid this stall in an organization, as new technology and capabilities are rolled out, an organization-wide investment needs to run parallel to raise the organization’s skills around data, platforms, products, visualization, and data science. Data literacy brings together the ability to formulate ideas, leverage data effectively to prove or disprove those ideas, and communicate outcomes and ideas to others.

There are a variety of approaches to take when building data literacy programs, which we’ll get into in future discussions. Today, we focus on the key questions that data literacy programs must answer to ensure employees are appropriately skilled and empowered to leverage data effectively. These questions can become modules of their own in a data literacy training curriculum or become consumable services for teams to leverage as part of workflows. The method of dissemination is less critical than the accessibility of the information and regular reinforcement across the organization.

  1. What is our data strategy?
  2. How does our data strategy align to our corporate objectives?
  3. Where do I find data within our environment?
  4. How do I publish data for others to consume?
  5. What policies limit my use of data?
  6. What technology should I use for the various stages of data preparation, visualization and analysis?
  7. Under what circumstances can I use other technologies?
  8. What forums are available internally for collaboration?
  9. Where can I elevate business processes I have identified for improvement through data?

The more effectively team members can share data assets, discuss meaning of data trends and build new data driven applications, the more effectively an organization can deliver on their promise to customers. This effectiveness comes from a shared language and skill set enabling growth and maturity over time, while avoiding previous discussion topics, decisions, or “gut feelings” that aren’t quantified and measured.

Over time, building a data literacy program can enable your organization to more effectively collaborate, incrementally build new capabilities, measure outcomes, and onboard new team members.

I hope this post useful. Let me know if you have any questions in the comments, and don’t forget to sign up for the next post.

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About the Author

VP Analytics
Joey Jablonski is VP of Analytics at Pythian, he leads strategic engagements assisting customers in developing their data strategy, defining and executing on data governance programs and building analytical models to power the modern data-driven organization. Prior to Pythian, Joey was VP of Product at Manifold, where he brought a product mind-set is part of all engagements—allowing for delivery of value quickly in any project, and building over time to drive adoption of new data-centric capabilities in an organization. Joey led engagements across industries including high tech, pharmaceuticals and for the federal government. Before Manifold, Joey held executive leadership positions at Northwestern Mutual, iHeartMedia and Cloud Technology Partners. He brings 20+ years of experience in software engineering, high performance computing, cyber security, data governance and data engineering.

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