We have all heard the notion that data should be viewed as an asset, the “new oil”, a crucial business resource for the enterprise. When viewed in this context, a business’s investment in data must be assessed by the quality of its return. Measuring this return is more than just “how many cool-looking dashboards” you can now create; rather, a CTO/CDO must look beyond the technical capabilities and determine if data is having a true impact on the business culture. This means evolving a BI and Data Science community that engages employees at all levels of the organization. The path to this positive return is creating a modern data culture that permeates the enterprise.
The foundation of a modern data culture centers on three connected interactions:
- Enabling Technology
- Empowered People
- Governed Processes
Enabling Technologies are the modern data platforms and integration tools that have risen to the top in offering flexibility, scalability and analytical power. They are invariably cloud-driven and consumption-based, with low administrative overhead. Time-to-value in implementing these platforms is minimal compared to legacy on-premise systems. The triumvirate of Snowflake (data ingestion, computation and storage), Matillion (data integration management) and ThoughtSpot (augmented, self-service analytics) most effectively defines this platform for Pandata Group, especially when coupled with a potent data science environment such as Databricks or Amazon SageMaker.
Empowered People are created when the tools of data analytics are democratized, and the community around data is enbraced and strengthened. This comes about through skill enhancement, knowledge sharing, and data literacy. Building a strong DataOps development lifecycle also goes a long way towards fostering a strong data-driven culture. Ultimately, a shared library of practices and reusable technical resources results from this community-building. The fuel to drive this kind of resource empowerment is change enablement (more about that in part 2).
Governed Processes consist of data stewardship, quality management, metadata cataloging, lineage and data dictionary documentation (among other data governance disciplines). All processes related to data are identified, examined and streamlined. We favor a collaborative approach to governance as opposed to top-down “data policing”. This is accomplished through collective cataloging, curation and sharing of enterprise data, with a goal towards embracing data comprehension among all business users.
So how does your organization start the journey to a modern data culture? We recommend a three-phase process of Building the Foundation, Deployment and Governance, and Strengthening the Community. The foundation is built by identifying a use case, then moving from a proof-of-concept to a pilot that serves the needs of that case. A proof-of-concept is created to simply demonstrate the feasibility and application of the technical solutions and how it will meet the use case solution. The pilot project is a simple but complete production-ready implementation of the solution that can then be established as a template for subsequent development projects.
In the deployment phase, data teams within the business unit(s) are established and primed for data skills, expertise, and ownership. Finally, the data community is strengthened through data mentorship, data literacy, executive alignment, creating standards and reusable components, continuous upskilling, and constant communication (via online forums, blogs, workshops, etc.).
These are the ingredients to bring about an organization’s modern data culture with the four F’s: focused, fast, flexible and fun. But the barriers to entry are rarely technological . . . instead, resistance to adoption is often ingrained in the mindset of data workers and business users. A new approach is needed to overcome this: change enablement. Part 2 of this blog series will explore how this can be achieved at all levels of the enterprise.