Data catalogues often come in handy to know what data is available, what it means and who's responsible for it. You could say it's the ideal tool to skip the endless data discovery phase. And get right away to work with the data to generate valuable insights and make strategic decisions. Nonetheless, a data catalogue is a tool; implementing and maintaining it comes with challenges that are often difficult to manage.
But have no fear; with the DataOps discipline, you can easily face all these challenges and get the most out of your data catalogue. Continue reading to find out how.
What is a data catalogue, and why would you need it?
A data catalogue is a dictionary listing a detailed inventory of your organisation's data assets and describes and summarises this data (the metadata). This way, you can search for the available data assets and get information about what it includes and what the data stand for — enabling you to quickly find the most appropriate data for your analytical or business purposes.
Some benefits using a data catalogue could bring you are:
- Having a better understanding of the semantic meaning of the data by using context also improving data literacy.
- Improve collaboration: everyone can add comments to the catalogue to add more context and know whom to go to when you need more detailed information or insights.
- Increase the operational efficiency and productivity of the analytics and business teams, as they can find the most appropriate data quickly and without being dependent on someone else. Also, read our recent blog about how to overcome data accessibility roadblocks for successful analytics.
- Respond faster to the market and gain a competitive advantage because business users can more easily consult and interpret data and make the appropriate strategic decisions.
- Reduced risk, as an analyst will only be working with data, they're authorised to use and is compliant with the data privacy regulations.
- A higher success rate for impactful data management projects: as analysts no longer struggle to find the most appropriate and trustworthy data, business intelligence and big data projects have a better foundation to get started.
How DataOps makes sure your data catalogue & governance practices will be a success?
Data catalogues are often one part of data governance; nonetheless, governance on its own will not guarantee you success. It would be best if you had a data-driven culture, and to obtain this in the best way possible. To achieve this, implementing the DataOps discipline is your best choice. Let’s go through a couple of examples of how DataOps can enforce your data catalogue to be a successful tool within your company.
1. Define your data strategy and action plan first
Data catalogues and governance are often implemented without any strategic plan and remedy a lack of business focus: trying to be everything for everyone, having no clear definition of goals. Data catalogues and governance should be part of a bigger picture: your data strategy - what is most important? The data strategy will redirect resources to support, improve and streamline your teams, systems and processes.
By implementing DataOps, you set a data-driven environment and culture. You will automatically have a data strategy in place and build a data environment where everything is linked to each other and with self-service, automation and CI/CD. DataOps will also encourage the teams to experiment, iterate and adapt.
2. Involve all stakeholders when selecting a data catalogue
A technical team often decides upon data catalogues without any input from others. As a result, the technical team only has a limited view of what is required to implement. DataOps is all about open communication and close collaboration.
From the start, have all your stakeholders around the table for input. This will create a diversity of opinions, goals, needs and use cases. During and after the implementation of the catalogue, often request feedback on the chosen tools.
3. Break down the data silos
Data governance projects often are localised, fragmented and inconsistent, limiting the potential value and scalability of the investment made.
With DataOps, you set a culture where data silos between teams, stages of the data pipeline and process are broken down, and data is made available for all the appropriate persons (not only the local ones). It supports collaboration and communication across all stakeholders and implements the agile principles, ensuring that incremental value is created in the end. All data processes, tools and methods are designed to be scalable.
Next, you make data and data management capabilities faster available and in more places (cloud & edge environments). Another vital pillar of DataOps that comes in handy is the DevOps principles; they ensure a consistent and reliable data flow.
4. Become agile and scalable
Often, data governance frameworks are fragile to market disruptions and company reorganisation. The DataOps discipline enables you to build scalable and agile data governance practices that allow you to react and adapt quickly to changes within the market or company.
Next to this, DataOps allows you to communicate and troubleshoot better data pipeline threats. For instance, a DataOps Orchestration platform can quickly identify the root cause of data breaks for fast resolution and prevent future downtime.
5. Work with authorised, consistent and reliable data
Data cataloguing helps to only work with authorised data to make it more consistent and trustworthy. However, it does not guarantee the consistency and reliability of the data running through the pipelines.
With DataOps, your data becomes even more reliable: better communication and troubleshooting of data pipeline threats. For instance, a DataOps Orchestration platform will quickly identify the root cause of data breaks for fast resolution and prevent future downtime.
6. Focus on creating value for the business
Data catalogues can be overwhelming, as it is very complex to set everything up and maintain it. DataOps simplifies by having a clear focus and a common goal: creating value for the business.
To help you conquer the overwhelming feeling and complexity, the implementation of DataOps is done step-by-step. If you don't know where to start, read our eBook about the best practices implementing DataOps.
7. Also accessible for non-tech savvy users
Data catalogues are often too difficult to use for non-technical proficient users. By following the DataOps discipline, you will educate your stakeholders about data-driven topics and better understand how the raw-data-to-insights flow works and supports them.
This way, you will increase data adoption and literacy within the company. Moreover, you will improve the data (management) capabilities, communication and collaboration across people, teams and business functions.
8. Apply self-service, automation and CI/CD
Data catalogues are resource-consuming: the data team needs to do all the heavy lifting of manual data entry, updating the catalogues as data assets evolve. DataOps encourages you to implement self-service automation, and CI/CD flows to reduce the load of the teal.
DataOps Orchestration platforms, like Tengu, will help you install automated discovery, inventory, profiling, tagging, and creation of semantic relationships between distributed and siloed data assets. Tengu even uses watchers that spot changes and triggers to do an automatic synchronisation and deployment. They also enable users to leverage its insights in a self-service manner: self-service data discovery, add automation and workflow orchestration.
To conclude
We hope you have found all your answers on successfully working with a data catalogue by implementing the DataOps discipline. If we have forgotten something, or you are still uncertain how to get started, don't hesitate to reach out to us. We would love to help you make your data catalogue most successful.
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