ElevateData: Unpacking the Data Stack

Last week, we hosted our fourth (and last) ElevateData event of a tumultuous 2020. We brought data leaders together from companies like Etsy, Poshmark, LinkedIn, Udacity, Unravel Data, Bitly, SVB, and Zscaler to delve into what is working and what isn’t within the data stack. The early conversation was focused on Data Democratization, BI Tools, Data Quality, and Data warehouse. We concluded by asking everyone to share their technology and process wish list – just in time for Infrastructure Santa to get to work!

It was an incredibly lively and insightful conversation, with many best practices shared by industry-leading data executives. If you’d like to join one of our events in the future, don’t hesitate to reach out!

Here are the key takeaways from our conversation:

Data Stack

Business Intelligence Tools and the Democratization of Data

Unsurprisingly, many of the members of ElevateData believe that data-driven decision making is a competitive advantage. Many in the group also believe that in order to truly achieve this competitive advantage, the data needs to be in the hands of not just the C-Suite but everyone at the business. Business intelligence tools enable this democratization of the data.

Tools like Thoughtspot and Sigma Computing that allow for Natural Language Search on your data were recommended as technologies that enable data democratization through empowering employees outside the data org. In addition, business intelligence and analytics tools like Looker and Tableau that enable powerful dashboards created by analysts and data scientists were also recommended as part of the important toolkits for data democratization.

Data Quality & Remediation Tools

As we discussed the concept of data democratization, a key question came up around the trust in data. Choice of BI tool doesn’t matter if one can not trust data. 

It was also clear that the value of clean data is not always appreciated by others in the C-suite. Cara Dailey, CDO at SVB, summed up the need and value for data quality this way, “You expect the water in your house to be clean. You expect to be able to shower and cook with the water. But only when it’s not coming out of the faucet, people realize it is a disaster.” In other words, it is hard to make a case around the value of data quality until things go seriously wrong.

While many data leaders agreed that data quality and cleanliness is a problem that is best solved today by people and process, they shared names of a few technology companies who are developing solutions to tackle this critical area. One of them was Monte Carlo data, founded by one of our ElevateData leaders. 

Data Warehouse: A quick survey of our data leaders revealed that the top data warehouse choices in the group were Snowflake and Google Big Query. Many of the data leaders had or were in the process of moving away from AWS Redshift for various reasons – cost, instant scaling, maintenance, and more.

The $ Value of The Data Stack

It would not be fair to leave out the dimension of pricing when talking about the data stack and the leaders dove right into the return from the price tag of having a world-class data stack. All new tools promise to solve a new problem but the narrative needs to be the business value delivered from those tools. Moreover, sometimes the data leader can see the value of using a new tool but it is not an easy feat to explain the value of these tools in the context of the value creation for the broader company. The ElevateData group would love to see their technology vendors helping them project the business value of their solutions that go beyond solving a technical problem and/or the efficiency improvement of the data team. 

Technology vs. People and Strategy

One of the more enlightening takeaways that we had from this conversation and from other ElevateData discussions is the symbiosis that data leaders must cultivate between technology and People/Processes. While technology is a critically important part of the data stack, for better or worse, it is not a panacea. For the data leader, it is equally important to focus on the people and strategy to create value for the organization. 

One way to do it is to Champion Data-Driven Decision Making. Finding alignment around data-driven decision making with fellow members of the C-suite eliminates the need to justify each use case or project that the data team focuses on. Kathleen Maley, fmr SVP of Consumer and Digital Analytics at Keybank puts it this way, “Building these relationships happens over time. It’s a full-on marketing campaign that I never let up on. You need to show results that are tied to action.” Doing that makes the data leader’s life much easier! 

Wish List

Just in time for Christmas, the data leaders compiled a short, humble list of asks from the Data Santa. Here they are:

  • Technology:
    • Low Code/No Code to expand the universe of data analysts and engineers
  • People and Process:
    • Alignment on data-driven decision making, champion in the C-suite
    • Business partners to own their data and data quality
    • Analysts to focus more on the business problem/opportunity vs. the algorithm

Thank you to all of the ElevateData members for joining us and passionately exchanging your ideas virtually during this challenging time.

If you are a Data leader, please let us know if you’d like to join this amazing group at the next ElevateData event!

Your ElevateData Founders,

Barkha, Oren, and Alex

Published by Alex Choy

Director at SVB

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