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How to Transform the Data Culture of Your Company

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What is Data Culture?

Data Culture is a shared belief in the organization to use data to improve decision making and performance. It has essentially three important characteristics:
  • People are empowered to use data.
  • Data is prioritized in decision making.
  • Data assets are managed as products.

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Why Data Culture is Important?

It is observed that data-driven organizations experience above-market growth, leading to increased revenue, profitability, and operational efficiency. In order to fully realize the benefit of a data-driven organization, data culture plays a crucial role. Clear ownership of data, customer sensitivity and keeping the data high quality and reliable should be part of the data culture. It cannot be an adhoc reactive measure. Long term data quality solutions require a strong data culture. Data is a shared responsibility of the organization and requires an end-to-end approach. Data producers and consumers can together work towards better documentation and classification. Here, collaboration is key to solving issues and to improve data. Also, an organization needs to have a clear understanding of where they are with data. So they can set clear goals, achieve those goals, and measure success.

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Key Aspects of Data Culture

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1. Data Needs Clear Ownership

All important data must be owned. Individuals should not own important data assets. Team ownership is preferred over User ownership. It also pushes the data responsibility to a team instead of an individual user.

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2. Measure What Matters

You cannot improve what you do not measure. It is important to set metrics to understand how your data is doing. Based on that, you can set goals towards improving your data.

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3. Treat Data as a Product

  • Maintain clear documentation of data with SLA and guarantees.
  • Understand your user and use cases and build the data accordingly.
  • Continuously improve data with discipline.
  • Track user feedback using surveys.

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4 Reduce Toil with Integrated Tools and Automation

Too many tools around specific use cases clutter the data landscape. It is better to use integrated tools that handle multiple scenarios like data discovery, collaboration, lineage, quality, etc. Automated data quality tools help to ensure updated and fresh data. Tools that identify the Tier 5 or least important data help to declutter with automted data deletion.

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5 Organize for Data

Data does not come free. You need resources the teams to keep the data of high quality, reliable, and trustworthy. Decentralize data for end-to-end domain based data ownership.

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How Collate Helps Enhance Data Culture

In order to enhance the data culture of a company, data need to be Trusted, Documented and Discoverable across the organization. Collate is an all-in-one platform for data discovery, collaboration, quality, governance, observability, lineage, glossary, and much more. Alongwith ensuring reliable quality of your data, you can use the collaborative features to maintain proper documentation, ownership, and appropriate tiering of your data assets.

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1. Centralize your Metadata in Collate

Collate helps to understand your data landscape. It captures all your metadata in a single place. It is a collaborative tool for both technical and business users.

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2. Set KPIs to Drive Data Ownership

The data insights feature allows you to set up KPIs using time-based goals to track ownership. Goal-based tasks can be set up for different teams. You can claim data asset ownership in Collate.

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3. Set KPIs to Drive Documentation

Data without description is hard to use, resulting in the loss of productivity. Similarly, invalid or missing descriptions result in poor data outcomes. Good descriptions help to discover data assets quickly. You can set up KPIs with a specific goal to cover data documentation.

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4. Develop Data Vocabulary

Data vocabulary helps in the consistent understanding of data. In Collate, using the Glossary feature, you can describe business terms and concepts in a single place. Also, the data assets can be labelled using these glossary terms in order to provide semantic meaning.

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5 Identify Important Data with Tiers

Tiering is an important concept of data classification in Collate. Using Tiers, data producers or owners can define the importance of data to an organization. In case of tiering, it is easiest to start with the most important (Tier 1) and the least important (Tier 5) data. Once the Tier 1 or most important data is identified, organizations can focus on improving the descriptions and data quality. The Data Insights in Collate helps identify the unused datasets as Tier 5. The Tier 5 datasets can be deleted periodically to declutter.

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6 Provide Feedback to Teams

Collate provides continuous feedback by way of weekly reports. The detailed reports help to track progress over time. It keeps the leaders well informed. It helps to recognize the teams that are doing well.

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7 Use Collate Browser Extension

By using the Chrome browser extension, users can consume the metadata in the tools of their choice. It provides consistent understanding of metadata at their fingertips, and helps improve productivity.

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8 Data as a Product in Collate

Collate helps customers understand their data with a 360° view. Admins can set up sample data, table and column profiling for the important data assets. Data quality is important and it is a shared responsibility in the organization. Admins can set up data quality tests in Collate to detect and fix the issues early on. Both data producers and consumers can collaborate to capture assumptions about data and set up tests accordingly. Go ahead, leverage Data Insights to transform the data culture of your organization! Watch the video to learn more about proactively honing the data culture of your company by setting targets, monitoring, and boosting teams to accomplish data goals with Collate.