Data Democratization for Highly Siloed, Complex, and Large Organizations

While data democratization seems an innate goal to support a successful, thriving business and its employees, achieving said democratization is easier said than done. In fact, a variety of processes and implementations await those companies that aspire to greater data accessibility—from breaking down data silos, to improving data governance and security, to enhancing data flow throughout an organization. 

DBTA recently held a webinar, “How to Democratize Your Data with DataOps,” with invited experts to evangelize on the methodologies and best practices that organizations can adopt to effectively guide them through their data democratization journeys.

Ben Herzberg, chief scientist and VP of marketing at Satori, began the roundtable succinctly: everybody wants data democratization.

According to a study from Satori, 75% of respondents are currently working on increasing access to data. Though this broad group of organizations appears to be on the right path, the difficulty of data democratization is evident, especially when considering the variety of teams that are responsible for securing access to sensitive data.

Ultimately, it takes a lot to get the right data to the right people, Herzberg explained. Between access, compliance, and security, data democracy is a simple term to describe a rather complex, labyrinthine process.

Herzberg turned viewers’ attention to Satori’s Data Security Platform, designed to provide frictionless, escure, just-in-time data access. The platform modifies the oftentimes knotted and in-direct processes of delivering data to a centralized, three-division system which innovates on the areas of access, compliance, and security to deliver data fast:

  • Access is simplified through automated, self-service, frictionless access workflows.
  • Compliance is simplified through auditing and monitoring features for all data activity.
  • Security is simplified through granular security and privacy policies universally applied to all data stores.

Jesse Miller, product manager at Monte Carlo, detailed Monte Carlo’s five pillars of data observability:

  • Freshness, which is where you seek to understand how up-to-date your data tables are, as well as the cadence at which your tables are updated.
  • Volume, which refers to the completeness of your data tables and offers insights on the health of your data sources.
  • Schema, where all schema changes are monitored in your environment and alerts you of added, removed, or updated fields and deleted tables.
  • Quality, where you ensure trust in your data by monitoring the values in your tables and alerts you if your data falls out of an accepted range.
  • Lineage, where you monitor the upstream sources and downstream ingestion of data to highlight what may be impacted by a break in your data.

According to Miller, these pillars emphasize that data observability is what powers democratization. Monte Carlo’s Data Observability Platform helps to drive organizations on the path toward data democratization by creating trusted, protected data that feeds access, discovery, and governance of that data into business, product, and analysis into users’ hands.

By creating an environment that embraces and produces good data, the organization invites greater trust and adoption across its teams. With Monte Carlo’s platform, 30-50% of time is saved from data incident fire drills; users see a 90% reduction in data issues, as well as a 90% reduction in product data downtime; and invites reliable self service and greater data trust, resulting in more widespread adoption.

Danny Sandwell, senior solutions manager at Quest, explained that the path to data value and monetization lies is mature data practices, data democratization, and adopting a “data-first” culture; these facets unite to propel better business outcomes, successful data scaling, and an environment consisting of trusted, reliable data flow.

To achieve a balance between these tenets, Sandwell argued that building a sustainable foundation enables agility, adaptability, and resilience to maximize the business impact of your organization’s data. He boiled it down into seven steps:

  1. Model: Design a data architecture that best serves your organization.
  2. Catalog: Create a catalog of data so that it is searchable and found easily.
  3. Curate: Enrich data with business context.
  4. Govern: Apply business rules and policies to govern your data.
  5. Observe: Raise data visibility to encourage proactive management.
  6. Score: Automate data profiling and quality scoring to ensure data is at its best.
  7. Shop: Make trusted, governed data widely accessible.

Furthermore, Sandwell explained that DataOps is a key enabler for successful data democratization—yet its adoption is few and far between. Why? Sandwell argued that it’s cultural; creating an atmosphere of a data culture that can support the process of data democratization is critical toward inviting widely accessible data.

How do you create a data-first data culture? By evangelizing, promoting, and operating on these four tenets: providing tools for optimized access; ensuring data is trustable and a source of truth; enabling all data consumers and producers are capable and equipped with the skills necessary to become data-first; and inviting, uplifting, and cultivating a culture that can continuously and readily sustain a data-centric approach.

For an in-depth discussion on strategies and tools for data democratization, you can view an archived version of the webinar here.