Q2 ElevateData: Bottom Up Adoption of Data Tech

In recent years, we’ve witnessed the meteoric success of product-led growth (PLG) and bottom up selling motions in the enterprise software market. Since we strive to have a finger on the pulse of the data world, it made us wonder: Would PLG find success within the data space? To answer this question, we assembled a unique forum for our Q2 ElevateData event. We had the usual suspects: Leading Chief Data Officers (CDOs) from companies like Amazon Music, Earnest, Walmart, Frontdoor, Freedom Financial Group, AWS and Poshmark. To enrich the debate, we also hosted CEOs of emerging data startups like Monte Carlo, Streamlit, Mozart Data, Unravel, Nexla, and Acryl Data. Having this diverse group in one Zoom room led to a hearty conversation, exploring PLG in data and different go-to-market approaches. The discussion largely focused on the CDO perspective on PLG, specifically the dynamics that Vendors should be aware of within the data org and how to navigate them. 

Before we dive into the learnings, let’s unpack product-led growth. It is the concept that the product adoption by users starts the sales cycle and is the primary driver of growth. It changes  the strategies for customers acquisition, retention, and expansion. Zoom and Slack are prime examples of B2B productivity companies that were built on the foundation of PLG. Other examples include HashiCorp and Auth0, from the engineering world. These and other companies have been able to drive interest and value for the user, which in turn helps to drive value and ROI for the beneficiary/buyer of the organization. And indeed, companies that adopt a PLG approach generally supplement it with direct Enterprise sales. While PLG has worked well for collaboration and engineering, will it work for products that are critical for data infrastructure? As our own Barkha Saxena, CDO of Poshmark, pointed out, “as a data person, I look at collaboration tools very differently than tools that data flows through.” Let’s now explore what the group discussed:

Data leaders talked benefits of PLG:

  • Adoption in the data org: PLG allows data practitioners to educate themselves and choose their own solutions – through trials and open-source tools. According to Swaroop Jagadish Co-Founder of Acryl Data, the modern data engineers demand consumer-grade delight from products they use. Leveraging this model, the organization can evaluate and purchase tools with a higher degree of confidence that they will be adopted!
  • Champions for the vendor: Successful bottom-up companies gain zealous supporters of their product in the organization, and they spread it from there. This creates a community of champions that carries over when people switch jobs and bring their tool set with them.

And then the CDOs outlined their considerations for Vendors to be aware of:

  • Trenches vs. 60k feet: Shwetank Kumar, CDO at Freedom Financial Network, articulated one of the dynamics that PLG creates within the data organization: “This can become very adversarial at times. You might have a reasonable product in one domain, or a great product for a specific use, but there is a larger organization strategy around data infrastructure, analytics, and tools.” Vendors that take a PLG approach should not only understand how their product can help the end-user but also how it fits into the broader data strategy and stack. One of the best ways to accomplish this is by establishing relationships across the data org. If not, it could lead to an adversarial relationship that is counterproductive to the goals of each party! To see bottom-up success, it is critical that the user, beneficiary, and buyer are as close and aligned as possible.
  • The proliferation of data tools: At our last in-person ElevateData event, one CDO from a Global 100 company shared that there were two to three times as many seats for data tools as there were data analysts, scientists, and engineers. While this may be an extreme case, it is important for the organization to not lose sight of the management or standardization elements. More tools can create more flexibility, but at the expense of cost and maintenance.
  • Trust and consistency in Data across the Org: Any data tool which can lead to data discrepancies and bottom line impact on data quality at any level will eventually become obsolete in organizations striving for high data integrity and consistency. This is why it is critical to understand organizational dynamics, key influencers and overall data strategy to ensure right partnership as PLG companies work on expanding the adoption of the product through complimentary enterprise selling.
  • Infosec, compliance, and data privacy: When adopting new data tools the users need to make sure it is not creating security holes for the company’s security posture, nor does it violate any privacy / data sharing regulations. This of course becomes a critical factor in deal success in larger organizations. To mitigate that, many PLG data tools should consider an “on-premise” deployment model or minimize the type of information collected. In addition, the leaders agreed that SOC 2 is a must no matter how small or big the vendor is. It is also interesting to consider that the majority of tools are getting pushed out early on, with the Vendor not even knowing they were evaluated. So being stale on security, reliability, trust, etc. is a disadvantage.

The group then discussed how successful GTM could look like:

  • Success lies in finding the middle ground between different stakeholders and balancing motions. For example, Barr Moses of Monte Carlo noted there might be a combination of community-led and direct sales in a motion she describes as “customer-led” which emphasizes the need for thoughtful POC. Bringing elements from both strategies to allow practitioners to choose their solutions, educate themselves, try it out with a trial or open source. This convergence of approaches may address many of the concerns outlined by our CDOs.
  • It was also noted that the next wave of successful companies will need to figure out how to be good citizens of the ecosystem. It’s time to think about interoperability and weave in the concerns of data platform teams earlier on. If you’re not placing yourself as a citizen in the open ecosystem, you’ll do yourself a disservice
  • Another interesting point was made by Saket Saurabh of Nexla: There’s an awareness continuum, and somewhere in that, you want to try the product. And somewhere there is a stop where you need approval. Question is whether the goal of PLG is to get you in or end-to-end.
  • Our CDOs provided advice on converting users to customers: It’s key to find validation of use cases to a team (beyond the individual). Next part is what is the nature of these integrations, and making sure you can answer questions on compliance and security early on. The central data teams will likely pay if it’s something that solves 2-3 use cases well for a team over some time.

And wrapped up by talking about implications of bottom-up adoption of business facing data tools on the Data org:

  • Bottom-up adoption for business teams facing tools meets a need for a business user that the data team isn’t addressing with their current capacity – there is a gap in bandwidth and the data team cannot work through all the different use cases and the pressing business desire to move fast. This gap resembles the proliferation of Shadow IT tools, which eroded the dominance of the IT org
  • There are so many parts of an org that use data in some way, so you have these pockets of opportunities for bottoms-up in every part of the org which has some data competency. This will be value added to the organization as it increases ROI from data. 
  • But the CDOs don’t think the data teams are at risk of being substituted anytime soon: There will always be a need for a central data team and governance which will provide the foundation for further value addition by PLG companies.

As we reflect on our discussion, it is clear that just like other sectors, enterprise sales will need to compliment PLG in data sector as well. While the path from bottom-up adoption to Enterprise sales is not straight forward and would require investment and relationship building as in typical enterprise sales, PLG would provide an advantage by having internal champions in and outside the data orgs, eliminating/substantially speeding up POC processes and enhancing widespread adoption new technologies.  


Thank you to all of the ElevateData members for joining us and passionately exchanging your ideas, as well as to the inspiring founders who joined us for the event! If you’re a data leader and are interested in joining ElevateData, please reach out to one of us! 

Your ElevateData Founders,

Barkha, Oren, and Alex


Looking Beyond the Hype of Machine Learning – A Recap of the Q4 2019 ElevateData Dinner

Last week, we hosted an ElevateData dinner, the last 2019 gathering in the series that unites Data Leaders to delve into hop topics in the industry. Just in time for driving New Year’s resolutions, we focused on a headline-generating buzzword and a top-of-mind topic for businesses: Machine Learning (ML). Given the continued commotion around this topic, we wanted to foster a reality check on the progress and potential of Machine Learning with a group of data leaders from different industries and companies. The result was a wonderfully insightful and lively conversation. Here’s what we’ve found:

Machine Learning is still at the beginning of its journey

When asking the data leaders how they define Machine Learning, it was clear that there is still variance in how data science vs. ML vs. AI are defined. Yet, it was widely agreed that we’ve just started to scratch the surface of the potential of ML. It’s interesting to note that the group overwhelmingly agreed that not every business problem requires ML and it is not perceived as a magic solution to throw at any problem. In fact, there are so many business problems across industries where the right solutions are data-related but not ML-driven, like reporting/dashboards, forecasting, inference, experimentation, etc.

Current impediments to Machine Learning

The discussion then focused on what’s standing in the way of Machine Learning delivering on  its promise. Here are the four major obstacles that were identified:

  • Lack of organizational readiness: It seems that many companies and industries haven’t yet reached the maturity level in data infrastructure and optimization/efficiency needs warranting investment in ML. Clear frameworks for measuring the impact need to be defined, tested and implemented. There is also the human factor: in many scenarios, there is  friction around adopting ML, as intelligence and automation delivered by ML are perceived by many as a black box creating loss of control, as well as driving job loss. The group agreed that leadership is critical to ML success. We’ve observed that organizations where Data Science directly reports into the CEO (like Stitch Fix) often see their data endeavors propel.
  • Technology deficiencies: Technology needed to make ML ubiquitous is still in the development phase. This is true for both development and deployment technologies and is leading to inefficiencies in ML solutions production cycle. Many companies are making progress in that direction but we are still far from having a full end-to-end ML solution.
  • Continued ML expertise gap: Despite an abundance of training programs, we continue to have a shortage of true ML expertise which is a combination of multiple skill sets including but not limited to programming skills, full understanding of algorithms’ inner workings, expertise in statistical concepts, and business acumen.
  • Next wave of innovation not enabled: Another factor inhibiting growth is skewed perception of where ML is often considered for optimization and increasing efficiency as opposed to something with a promise to deliver true innovation.

Machine Learning in 2020 and Beyond

After discussing the obstacles standing in the way of ML, the data leaders shared their perspective on driving forces which will help scale ML to next level in 2020 and beyond. Here are the five key takeaways:

  • Moving beyond the buzzword: It seems that the ML overhype is stabilizing. With growing awareness around hype vs. reality, just adding “ML” to every pitch and story is no longer creating the perception of the proposed data product being extra valuable. We are getting better at asking questions to validate the how, what and when about ML. This will help build credibility for ML application.
  • Education and messaging to build real belief in ML: To help ML realize its full potential, data leaders will continue to engage in demystifying machine learning so that it is not considered to be a black box. This will require using the communication tools and strategies tailored to specific situations and org structures. By reducing uncertainty associated with ML solutions, we can truly help unlock the full potential of machine learning. Data leaders will continue to work on positioning ML-driven solutions as enablers for people to do better at their jobs to mitigate the fear of job loss.
  • Focusing on broader data skills in hiring: Industry players are starting to think more holistically about data skills, with a growing acceptance that all data skills are valuable. Data leaders will have proactive conversations on this topic, reenforcing the message that analytical and statistical skills combined with business domain knowledge are  critical to building credible ML solutions.
  • Technology advancement: Through continued investment in ML platform development, some players will emerge with affordable and comprehensive technologies. That would likely speed up the adoption of ML but as development takes off, we would need to remember that ML systems have deeper technical debt challenge compared to software engineering. Therefore, it will be critical to set up the right processes and shape perspectives to ensure sustainable development.
  • Data ethics evolution: ML can lead to negative outcomes due to poor quality data, biases in the modeling, and more. A recent example is the Apple Credit Card controversy, with reports that the algorithm was discriminating against women. There are other scenarios where simple lack of judgment or not thinking through the broader impact of ML can lead to negative consequences. The group wondered if the role of a Chief Ethics Officer will emerge to focus on the ethical usage of data, and it expects an ethics discussion around data usage to become more prominent.

Our guests: Thank you to our guests from Unity, Snowflake, Stitch Fix, Uber, KeepTruckin, Shipbob, ZScaler, Streamlit, SAP Aruba, and Ethos for joining us for this dinner.

Thank you to all of the ElevateData members for joining us and passionately exchanging your ideas, ElevateData would not be what it is without all of you! We can’t wait for what 2020 has in store.

Let us know if you’d like to join this amazing group at the next ElevateData dinner!

Your ElevateData Founders,

Alex, Barkha and Oren 

ElevateData: Making your Mark on the CEO

Last week, ElevateData hosted its first virtual event of 2021. Many of our previous ElevateData conversations have been deep dives into the role and impact that data has within organizations and how the Chief Data Officer can optimize those results. So, in a small departure from our typical round table discussion, we were lucky to have an interactive fireside chat with Manish Chandra (Founder & CEO, Poshmark) and Barkha Saxena (CDO, Poshmark & Co-Founder, ElevateData). 

Data leaders joined us from Disney, Airtable, Bitly, AirBNB, Amazon, The Gap, Etsy, Frontdoor, Ethos, JLL, Monte Carlo, and SVB to get a CEO’s perspective on driving advocacy for the data organization. The result was a wonderfully rich and engaging conversation with many insights shared. We’ve summarized several of the key takeaways below, but there’s nothing like joining the conversation in person! Let us know if you’d like to join this amazing group of data leaders at the next ElevateData event!

Data is Foundational

The mission of Poshmark is to put people at the heart of commerce, empowering everyone to thrive. The team at Poshmark makes this happen by bringing the physical joy of shopping to the world of online marketplaces and commerce. We’d say Manish, Barkha, and the team has been executing well towards that mission, attracting more than 70 million users across the US, Canada, and Australia! 

As with any success story, there are no silver bullets or panaceas to that success. Instead, you have to peel back the onion and understand all of the layers that enabled Poshmark to achieve success. Manish was very generous in sharing one of the elements of Poshmark’s success – data. 

“So much of our business is unseen. When you have millions of people using the Poshmark app and millions of transactions on top of that, it’s impossible to look at what’s going on in the business qualitatively, so data has been foundational for us.”

Manish Chandra

Not only was data foundational to Poshmark, but it was also foundational to Manish. He started his career as a database engineer. So our first key takeaway, find a data-driven CEO! 

Creating a Data Culture

So, how do you build out a data-driven culture? At Poshmark, a thoughtful vision for the data organization was created in partnership between Manish and Barkha, and the execution towards that vision was (and remains) a key priority for the business. 

There are three key pillars to the success of Poshmark’s data-driven culture. 

  1. Leadership & Organizational Buy-in 
  2. Data Infrastructure and Technologies 
  3. Team 

We believe that this blueprint is applicable to many organizations and to data leaders and CEOs alike! We’ll dive deeper into each of these three areas below. 

Leadership & Organizational Buy-in – It Starts at the Top, but it can’t end there

Manish is a self-proclaimed data addict, “I would say out of my nine-hour day, if I’m not in a meeting (and many of my meetings are focused on data), I spend at least three to four hours in the data. My morning starts with looking at various data dashboards and often the night ends with that.” 

This laser focus of the CEO on utilizing data to measure the business and to ultimately leverage that information to drive decisions has permeated through the organization. One great example of how this has manifested itself at Poshmark is the “Core KPIs” meeting that Barkha organizes. This monthly meeting brings a cross-functional group of leaders together to evaluate the Key Performance Indicators (KPIs) in the business across all business functions. The data is a single source of truth and presented as facts for participants to synthesize and discuss. In Manish’s own words, “instead of data giving an opinion, the data is presented as fact, and everyone chimes in with their opinions.” 

Manish has only missed this meeting once in the past five years. 

Buy-in from leadership is critical, but in order to truly create a very data-driven organization, you have to enable data across every level of the organization. Leveraging tools like Looker and homegrown dashboarding, real-time insights and A/B testing tools have really helped Poshmark democratize its data. Barkha proudly can say, “despite being a large company, every single person at Poshmark uses data to make decisions.” 

Data Infrastructure and Technologies – The Plumbing for the Single Source of Truth

In the early days of Poshmark, the Company had a very basic data stack – leveraging Google Analytics and RJ Metrics. These tools served their purpose but had limitations so, in partnership with Gautam Golwala (Poshmark’s co-founder and CTO), Barkha created an ambitious vision for turning data into an operating tool for all business functions at Poshmark and set out to build a comprehensive data infrastructure and technology stack to deliver on that vision powered by high-quality big data. 

Today, that stack at Poshmark includes some great homegrown and some off-the-shelf solutions built on top of a massive data infrastructure that supports a wide range of use cases, including:

  • Analytics data warehouse
  • Real-time KPI monitoring
  • Maximizing ROI from operational business initiatives 
  • Seller tools
  • Machine Learning (ML) models

Given how foundational data is to Poshmark, it was critical to be incredibly disciplined with data quality and data consistency. Barkha and team have built processes to minimize data quality issues and surface any potential issues at the beginning of data pipelines. 

With confidence in clean data pipelines and data that represents a single source of truth, data users from the C-suite to the marketing team to the product team can leverage Business Intelligence (BI) tools to inform their day-to-day decision-making. Poshmark leverages various third-party tools as well as various homegrown data tools and data platforms for dashboarding to A/B testing to ML modeling built on top of open source technology.  

Team – Building and Sharing the Gospel of Data

As CDO, Barkha reports to Manish. Initially, she reported to Poshmark’s Chief Operating Officer (another very data-focused executive) further demonstrating the foundational and critical role of data at Poshmark. 

However, building the team was a journey of many steps. In 2014, Barkha’s data team consisted of one – herself. Over time she built advocacy and demonstrated the ROI of data within the Company. Today, Barkha’s team is larger than the total number of employees who worked at Poshmark back in 2014 when she joined the Company.  

Barkha’s team is organized into 5 operationally focused vertical data science teams and one horizontal Machine Learning team, each led by a strong leader. The vertical teams are each partnered with a business function within Poshmark (E.G. Marketing, Product, etc). These integrations allow the vertical data science teams to better understand specific business objectives for each org. They then bring their expertise in data science, data management, and data tools initiatives to help drive success. The horizontal ML team partners with ML Engineering team to focus on longer-term ML priorities which span across all business functions. Barkha’s team closely partners with the Data Engineering team in the CTO org to continue to scale data at Poshmark.

Barkha credits this highly collaborative environment to Poshmark’s culture. It’s uncommon to find this collaboration at large organizations and even rarer to find a culture that rallies around data in the same way. It was clear that Manish and Barkha are incredibly proud of the data organization they’ve built, and so they should be!

Cost and ROI of Data 

Building out what we just described above is not cheap. As many know, the rising costs of data tools, infrastructure, and people have made it increasingly difficult for data leaders to get the appropriate resource allocation. Despite Manish’s affinity to data, he still has a fiduciary responsibility to the business like any other CEO. As a result, there were many uphill battles that Barkha had to fight. Here’s how she did it. 

Start Small & Demonstrate ROI

Manish credits Barkha with being able to create a remarkable data infrastructure stack & organization by consistently demonstrating high ROI. In the early days, this started with building out dedicated teams to bring data to identified problems with clear and measurable parameters of success. 

This is an important point to unpack. Rather than collecting all of the data and trying to surface ideas, problems, and/or strategies, Poshmark instead focused on first identifying the highest priority business problems and then worked to bring the right data and right data solution to solve them. 

Barkha will be the first to acknowledge there are merits to collecting all of the data, but it was this sort of creative thinking (and budgeting) that enabled her to build advocacy with Manish and the rest of the C-suite at Poshmark. 

Once this model and ROI were proven, it enabled Barkha to build out a bigger and bigger team. Despite the growing costs, Manish believes that “data is very very leverageable so I don’t think it’s expensive from an ROI perspective.”


Special thanks to Manish for joining the ElevateData community to share your perspective as CEO and your thoughts on how to build a data-driven culture. Thank you to all of the ElevateData members for joining us and passionately exchanging your ideas. ElevateData would not be what it is without you! 

Your ElevateData Founders,

Barkha, Oren, and Alex

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

ElevateData: COVIData and Beyond

For the past two years, we’ve brought members of ElevateData together over dinner each quarter to discuss the latest events and trends, to share practical knowledge, and to predict what the future holds for the world of data. Due to the special circumstances of 2020 and thanks to Zoom, we took ElevateData virtual for the first time last week.

This quarter’s ElevateData featured data leaders from companies like Stitchfix, LinkedIn, Zscaler, Fico, Bitly, Frontdoor, SVB, and Poshmark. The discussion was centered around the impact of COVID-19 to the data organization and their priorities in the foreseeable future.

Here are the top takeaways from our conversation:

COVID-19 Impact

Digital Transformation Acceleration

Many of the Global 2000 have been on the digital transformation journey for the past several years. COVID-19 has catalyzed and validated the importance of digital transformation. This was best said by Satya Nadella during Microsoft’s earnings call in April, “We’ve seen two years’ worth of digital transformation in two months. From remote teamwork and learning, to sales and customer service, to critical cloud infrastructure and security—we are working alongside customers every day to help them adapt and stay open for business in a world of remote everything”. 

The ElevateData group noted that one of the drivers of this accelerated digital transformation has been that the unpredictable and rapidly evolving business environment has brought gaps in digital transformation journey to the front and center of critical decision making. 

As a result, C-suite of many organizations have become fully aware of its value and reprioritized resources to accelerate digitization. Cara Dailey, Chief Data Officer at SVB, shared SVB’s story, saying “COVID-19 really hit the go button on digitization and the ability to have data at your fingertips immediately”. Cara and her team at SVB have long known that data is at the heart of SVB and how they, and financial institutions like SVB, interact with their clients. However, COVID-19 has helped to emphasize this existing priority for SVB’s C-suite.

Data Orgs Resizing

During the Financial Crisis of 2008, Data and Data Science organizations were a shadow of what they grew to in 2020 and during this crisis we’re seeing how they are being affected by a downturn. Some data orgs have continued to grow given their alignment with the C-level and the overall support for data-driven decision making. However, there are other data orgs that have been particularly affected by COVID-19 related layoffs. The group discussed a few potential reasons for this.

  • Data and Data Scientists are expensive resources, particularly with the recent hype around data and data science.
  • Many data and data science bets are longer-term bets. These projects can be deprioritized quickly in a crisis when due to uncertainty, immediate revenue, expense control and profit become the central focus.
  • Given that a small (although growing) number of data executives are in C-suite, value of data in helping to navigate tough waters and prepare for when businesses would turn around is sometimes overlooked by management teams.

As data leaders continue to master succulently and proactively conveying the value of their organization to the C-suite, data teams will strengthen their positioning within the company. The key to conveying the data team’s value is this: Make it simple and don’t go too deep into the technical know-how. In addition, ensuring a good balance between near term and long-term value creation projects will also help data leaders strengthen the value perception of their teams. 

Post-Covid Impact

Data will continue to drive decisions

While we had a thoughtful discussion on COVID-19 impact on Data orgs, the group was long-term optimistic about the value of data to all organizations. The only way to efficiently solve problems with high ROI is with data, analytics, and data science. In times of uncertainty and rapid changes, as expected in the Post-COVID world, data will be even more critical and will continue to grow alongside a digital-first mindset resulting from COVID-19. Things that worked previously might not scale with the spike in demand and the shift to distributed work. Data will continue to be a huge opportunity and the lifeline of many SaaS companies. 

Data Orgs will become even more distributed

The group also expects a positive impact on data organizations from this forced experiment of remote work. Given that all organizations have had to learn to work around the constraints of offices and geographies, there will be more openness to recruiting data talent from a global talent pool. We expect that this in turn will drive the field of data forward. However, innovation is still required in remote working technologies to fully activate efficiencies of distributed data teams. While Zoom and Slack mitigate a lot of data teams collaboration challenges in the new world, at minimum, a good remote whiteboarding tool is needed to augment the remote experience of the data teams.  


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

The State of Data – A Recap of the Q1 2020 ElevateData Dinner

Last week we kicked off our ElevateData dinner series for 2020 with our traditional first event of the year, The State of Data. Despite concerns of Covid-19 and increasing financial volatility, we were joined in San Francisco by data executives representing companies like KeepTruckin, Metromile, United Healthcare, Lime, Ethos, Salesforce, Torodata, Layer9, DataCoral, and SVB. We reflected on the progress data made in 2019 and got out our crystal balls to form predictions on how 2020 would unfold. 

Review of 2019 Predictions

First we reviewed a set of 2019 predictions that the ElevateData group made a year ago, and then assigned a grade for each prediction. See how we did below!

  1. Our group had a diverse set of perspectives on consumer sentiment of data privacy in 2019. Several people in the group predicted that 1) consumers will continue to fight for more control over their data, visibility into where it is, how to delete it, and more. On the other end of the spectrum, others predicted that 2) consumers will fatigue on data privacy. And somewhere in the middle, 3) consumers will become more comfortable with less privacy, but will become smarter about their data and what privacy means.

Grade: B | The group believes that we are currently in a paradigm that falls somewhere between the second and third predictions. So while we were not totally accurate in our prediction, we covered the spectrum! 

  1. Blockchain and distributed ledger will help to solve data privacy

Grade: D | The group still believes these technologies could potentially help with  data privacy, but this certainly did not happen in 2019. Instead, we saw a host of data privacy tools like Transcend gain popularity in 2019. 

  1. Big Cloud providers will lead a consolidation in the business intelligence and analytics space (Looker, Periscope, etc.)

Grade: A | Not only were Looker (acquired in 2019 by Google) and Periscope (acquired in 2019 by SiSense) both acquired, but Salesforce announced the acquisition  of Tableau for $15.7bn! We’re excited to see how these cloud leaders will integrate the different businesses and how the next wave of BI and analytics innovations will look like.

  1. More and more data orgs will report directly into the CEO

Grade: C | While the group believes that this is an ideal end-state, it has not yet happened at scale. Many data leaders believe their organizations are still early on the data-driven journey, with roles and hierarchies being reshaped on an ongoing basis. More on this later. 

  1. Bonus prediction we had: AWS will acquire Snowflake

Grade: F | Snowflake remains a private, independent company at the time of this writing so this was a miss. In addition, the Company recently announced a $479MM fundraise at a $12.4bn valuation. The group still believes that Snowflake will continue to be an extraordinarily valuable asset for any of the major cloud providers. 

2020 Predictions

We then set about making our predictions for 2020: 

  1. Decisions and data will be centralized. There is more data and it’s becoming more challenging to know what data users in a decentralized data organization have and what they’re doing with it. A centralized function will enable high ROI from investment in data. 
  2. Data scientists will spend less time reporting out of Business Intelligence and Analytics tools. Instead, data will undergo a process of democratization in 2020 with Self-serve data becoming more prevalent. 
  3. While AI, ML and related technologies will continue to be a critical focus, we will move from hype to a more grounded reality and will find conversations broadening to include other critical topics around data catalogue, quality, accessibility and governance etc which are necessary pillars for all data products including AI and ML. Governance, cataloging, visibility, and monitoring will be critical in 2020 and beyond.
  4. While the ultimate org structure will still report to the CEO, the group was split between Data org directly reporting to CEO vs COO. We agreed that in the organizations where COO is fully running business operations, data orgs will fit well with-in COO org as well.
  5. We’ll see a growing number of novel data technologies born out of tech leaders that are known for their data prowess like Uber, Airbnb, Pinterest, and more. 

Bonus prediction: The Houston Rockets will win the NBA Championship. **Nearly a week after our event, the NBA announced that the 2019-2020 season would be suspended indefinitely due to Covid-19. Not a great start for this prediction but time will tell!

We are curious to see how ElevateData’s 2020 predictions unfold, and we are excited to dive deeper into a few of these topics at our subsequent events this year. One of our favorite themes we discussed at this dinner was that data leaders had grown tired of the hype surrounding AI/ML and other “shiny object” technologies. Instead, data leaders are focused on implementing processes and technologies that can deliver immediate ROI. Given the excitement around this topic, we’re going to focus on it at the next dinner and dive into subjects like Data Cataloging, Management, Governance, insights/inference automation and more. If you’re a data executive and interested in joining our lively events, let us know! 

Your ElevateData Founders,

AlexBarkha and Oren 

ElevateData: Q3:2019 – The Next (Big) Data Tech

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We hosted ElevateData’s Q3 2019 Dinner where we posed the question to the group – What is the Next (BIG) Data Technology? Our discussions covered a full spectrum of data technologies from ingestion to real time/batch processing to machine learning to privacy and security. We loved to hear unique perspectives from companies of all shapes and sizes on what each considered to be most valuable data technology innovation in the last few years – clearly shaped by different business goals and challenges. Some valued the scaling of relational databases while others were more focused on Machine Learning democratization technologies, data governance, and real time data streaming. 

Equally exciting were the opinions shared around what’s to come in data technology disruption in the next few years. We had a lively discussion around technologies like. Data Robots, Serverless, Streaming databases, and the verticalization of SaaS and infrastructure. 

For dessert, we ended with a fascinating discussion on how organizations like Earnest, SVB, Medallia and Poshmark have different frameworks for identifying the need for new data technologies, evaluating the age old question of build vs. buy, and then integrating it in their environment. 

Thank you to all of the ElevateData members for joining us and passionately engaging in exchanging ideas. This event would not have been such a success without your involvement. Here is a list of companies that were represented in the room, let us know if you’d like to join this amazing group at the next ElevateData dinner!

Akita Software, Amazon Music, Bank of the West, DataCoral, DataGrail, Doddle.ai, Earnest, Medallia, SVB, Tonic.ai, Transcend, Unravel Data

Many thanks,

Your ElevateData Founders – Barkha Saxena (Poshmark), Oren Yunger (GGV Capital), Alex Choy (SVB)

Attendees at ElevateData Q3 Event: The Next (Big) Data Tech
ElevateData Founders: (From left to right) Alex Choy (SVB), Barkha Saxena (Poshmark), Oren Yunger (GGV Capital)

Welcome to the ElevateData Blog!

Welcome to the ElevateData blog! ElevateData is an initiative started by Alex Choy, Director at Silicon Valley Bank, Barkha Saxena, Chief Data Officer at Poshmark, and Oren Yunger, Investor at GGV Capital. The mission of this initiative is to scale practical knowledge sharing among data leaders in an environment that fosters open and lively discussions in order to elevate role of data to the next level across all organizations.

ElevateData hosts quarterly dinners on important and relevant data topics. We’ll be sharing the key insights and takeaways from these dinners on this blog.

Finally, we’re always looking to add great data leaders to our community. Please feel free to nominate any one that you think would enjoy joining ElevateData!

We look forward to sharing what we’ve learned and hope to see you at our upcoming dinners and events!

The ElevateData Team,

Barkha, Oren, and Alex