More than a 100 Data Products: Vista Leverages Valuable Data with a Data Mesh

At Vista, we strive to help small businesses grow, find new customers and live out their dreams. We give businesses the ability to create awesome looking printed products, beautiful social media posts, and professional websites without having to purchase special design software. At Datanova 2022: The Data Mesh Summit, I was able to discuss how we at Vista have been able to create increased customer value by implementing a Data Mesh.

Rewind to the days of old storage organization and data structure: We only had one central BI team that relied on monolithic on premise hardware, which created a technological and organizational ceiling. As a result, that team was the only one who could give access to data. Therefore, our velocity was nearly zero and data analytics and business decisions were painstakingly slow. We also had no cross-functional teams. We had siloed capabilities and many on the team did not have domain knowledge or the right skills to build scalable data products. Lastly, we treated data as a by-product and not something that could improve all of our business processes. As a result, we were not seeing any business value. 

To solve for this, we decided to have a “mesh” start, and make the transition to decentralized, democratized access to data. Our two main goals in implementing a Data Mesh were to build out the data team with top in field data professionals and to create sustainable and lasting impact through scalable data products. We started building our team in 2020 to create our DnA i.e., our Data and Analytics team. We set a bold mission for ourselves which was to become the most iconic data and analytics company. In order to do this, we had a core belief that data should be like water. It should be available by the gallon and easy to access, store, and transport. It is a democratized good that creates life and should be put into the hands of all the decision makers in a company. 

We transformed our component based teams to end-to-end teams that allowed for cross-functional collaboration. Our clunky, slow, and inefficient monolithic hardware changed to a distributed cloud infrastructure, and we began creating data products in order to make self-service data. However, this transition wasn’t seamless. We started from scratch and had a short three months to launch this new platform. 

Despite the challenge, the results we saw with the new Data Mesh made the arduous journey worth it. We were able to implement fully end-to-end, multidisciplinary, agile data teams that had both technical and domain knowledge. This way these data teams could work with their stakeholders and focus on their business problems in order to build a data product that solved the business questions. To do this, we had to introduce many different capabilities onto the data team, which now includes data product managers, data analysts, and data scientists. 

Transitioning to data products allowed us to focus on customer-obsessed innovation and business value. We prioritized a roadmap with high value product increments along data domains. As a result, our velocity ramped up significantly. Today, we’re one and a half years into our Data Mesh journey and have been able to make over 100 data products and implement data domains and a domain map. We have had a 95% reduction in manual efforts by creating the self-service dashboards, which has reduced the dependency on a single data team.

It can be overwhelming to start the process towards a Data Mesh. Based on the learnings that we have had at Vista, I recommend: 

#1 Have a strong foundation before you start

This is particularly important when it comes to data platforms and data governance. Having a strong data domain map and data stack are key elements in a Data Mesh. 

#2 Build tools around the data platform to make your data teams more effective

Using the leading technologies to create a strong data stack, allows everyone on your team to build the Mesh on that environment and make them more effective at building data products.

#3 Foster cultural change

It can be difficult to transition to treating data as a product. Therefore, it is important to have a culture of education while also creating incentives and aligning teams. However, this new environment will ultimately foster innovation at a much faster pace to ensure that we are providing the best product possible for our customers.

To conclude, the Data Mesh has opened our eyes to data driven business decision making. By democratizing data, creating data enabled innovations, and a world- class data stack, we have been able to win our customers’ hearts, minds, and loyalty.