Data is a land mine for growth, and more businesses are realizing the great potential it holds. The value and insights from data have seen a rise in demand within organizations, and with this rise comes the expectation of a rapid turnaround time. However, this poses a significant challenge to central data teams, who handle data management and analytics. This led to the concept of data mesh, a decentralized approach that reduces friction and gives business domains the ability to quickly access and query the data they need.

In this episode, Sonali Bhavsar, Managing Director for Global Data Governance at Accenture, joins us to talk about the four pillars of data mesh. She discusses how companies can start applying data mesh to their workflows. Sonali also shares how Accenture helps its clients achieve data-led transformations.

Tune in to the episode to know more about data mesh, its significance and some tips to apply it to any business.

Here are three reasons why you should listen to this episode:

1. Learn about the four pillars of data mesh.
2. Understand why a federated approach is favored over a centralized system.
3. Discover how Accenture walks the talk in data mesh and data-led transformation.

Resources

• Bonus track episode on data mesh
• Connect with Sonali on LinkedIn

Episode Highlights[00:33] The Four Pillars of Data Mesh

• Data mesh is an approach to reduce friction in data workflows in order to maximize the value of data.
• Its four pillars are data as a product, domain ownership, self-service data infrastructure, and federated computational governance.

[03:47] Federated vs. Centralized Approach

• A federated approach allows your line of business to make decisions on their operation and what they value to meet regulatory obligations within their jurisdictions.
• A centralized approach has not been a sustainable model for the longest time. However, it’s most likely to work for an isolated, less hierarchy-driven organization.
• Traditionally, data governance decisions made outside of the business lineup and restructuring can lead to delays or unwanted results.
• For more complex organizations, each line of business might need to implement data governance in a certain way that may change or evolve within those lines.

[07:14] Self-service Data Infrastructure

• A big wave of data literacy is happening and the end goal is self-service.
• Self-service means that the consumption of citizens’ data, whether internally or externally, is built on trust.

[08:13] Data Mesh Trends

• Data mesh is not a new concept, but it’s becoming a hot topic. There is now a stronger awareness of data as a product, which was more theoretical before.
• The decentralized form of data ownership came about because businesses now see more value in data and want to maximize it.

Sonali: “You really want to support that end data citizen to be flexible to use the data that they want to use it as, versus going through permissioning and asking for that data.”

• Some components will remain centralized. Federated simply means the catalog of data products leans toward business ownership rather than a centralized data ownership. 
• There is a big pivot on determining data quality and its lineage, which affects whether different industries can use data and what data product can or cannot be made from it.

[11:59] Businesses’ Data Mesh Readiness 

• Industries such as financial services, insurance, and pharmacy already apply data mesh.
• High-tech companies are following suit.
• The line of businesses and the industries they’re in are altogether getting disrupted.

[14:34] Readiness to Adopting a Self-service Infrastructure

• Self-service is about firms investing in data catalog and data quality tools. Formerly, this was only done to observe regulatory protocols.
• Financial services have been always ahead of that curve because of regulations. Younger firms are often more flexible to pivot into something new.
• When you formalize the trust factor when dealing with data, the core pillars of data governance become ingrained as part of your ecosystem.
• There is now a clear delineation among different departments on how they want to handle data from different perspectives.
• The ideal situation is that businesses will have a clear responsibility, but also a fluid or gray area.

Sonali: “You want data mesh to be sustainable and operational, and keep data mesh as data mesh and not as a data mess down the road.”

[22:09] Product Thinking Mindset

• A product mindset means looking at different domains from an agile point of view and going through them iteratively.
• Data quality control is standardized to keep track of the data’s lineage.
• Consumers must be on the same page as businesses that the data product is validated and trustworthy for consumption.
• Ensure access and security are already validated to avoid bringing in random products that would endanger data consumption.

[27:09] Data-led Transformation in Accenture

• Accenture is going through its own data mesh and transformation regularly.
• Accenture has built their own data marketplace as an asset that complements what Collibra brings from a catalog perspective.
• True data marketplace is an important aspect of data-led transformation. Accenture has an entire offering on data-led transformation for their clients across all industries.

Sonali: “If the data was not good, AI is never going to be your driver, and writing good machine learning algorithms and AI is amalgamation of your machine learning algorithms.”

• Accenture believes going through a data-led transformation is their major asset.

[29:36] Jay’s Key Takeaways

• The four pillars of data mesh are crucial for treating data as a product.
• Central data teams can and should still exist. The key is to act as an enabling force for the business.
• Change is hard; don’t try it all at once.
• Instead, make sure that the leadership is committed in the long haul. It will take restructuring and investment in skills and scalable technology.

About Sonali

Sonali Bhavsar is the Managing Director for Global Data Governance for Data and AI at Accenture. She helps enterprises reinvent businesses to be data driven and fully utilize their data with the latest thinking and solutions available for advanced analytics, data management, and data governance.

If you want to reach out, you can contact Sonali via LinkedIn.

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Have any questions? You can connect with me on LinkedIn. 

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