Cindi Howson, the Chief Data Strategy Officer at ThoughtSpot, is an analytics and Business Intelligence expert with more than 20 years of experience and a flair for bridging business needs with technology. Cindi was previously the Vice President in data and analytics at Gartner, where she was the lead author of the Analytics and BI Magic Quadrant. Additionally, she led the data and analytics maturity model, as well as research initiatives in data and AI for good, NLP/BI Search, and augmented analytics. Prior to this, she was the founder of BI Scorecard, a contributor to Information Week, and the author of several books, including (1) Successful Business Intelligence: Unlock the Value of BI & Big Data and (2) SAP BusinessObjects BI 4.0: The Complete Reference. She has an MBA from Rice University and a BA from the University of Maryland.
My conversation with Cindi was recorded back in April 2021. Since the podcast was recorded, a lot has happened at ThoughtSpot:
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
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Here are highlights from my conversation with Cindi:
Back in college, I was an aspiring writer who majored in English and minored in Sociology. My favorite classes would be reading or writing literature — whether creative writing, short-form writing, or persuasive writing.
Because I had that strong foundation of communication skills, combined with the analytical, I could teach myself. Much of my early technical learnings were reading books while sitting on the train. People generally do not care much about the data; it’s the story that ties to the data that compels someone to take action. I believe that communication plus analytical skills is a winning combination, so I don’t regret the circuitous route I have taken to the space of data and analytics.
During the mid-90s, the BI category was emerging. There were no standards, and there was no education. There has always been data, but where did you get it from? Initially, we would get it from mainframe systems at Dow Chemical. However, our regional decision support system would go dark all the time, and we would need to rewire them. New tools such as Client-Server computing enabled us to do that. We knew that we needed to have a better solution when we implemented SAP at the time because we knew the mainframe systems would die. This is where BI came to the market, and we started seeking a solution.
I remembered going to one conference to decide how to buy BI tools. Should we go with more established brands like SAP or Oracle? Or should we go with newer, privately-held companies like BusinessObjects or Cognos? These two startups went on to become leaders in the industry by market share. It took about ten years for the industry consolidation to happen in the 2007–08 timeframe — when IBM acquired Cognos, Oracle acquired Hyperion, and SAP acquired BusinessObjects. That’s when BI was not just come department’s desktop tool. That’s when BI became the concern of the CIO.
I wanted to go back to graduate school because I did not have a typical college experience. I worked three jobs to put myself through undergraduate. I felt like I got a degree but not an education. I recognized holes in my education where I wanted to go back and fill. At Dow, people would ask me to design a business balance sheet reporting system, and I’m like: “What’s the heck is a business balance sheet?”
I knew enough about technology, but I needed to understand business, power, and politics better. I believed naively then that the best person would always get the job, and people would behave logically and do the right things for the company or the greater good. So I needed to learn more about how power and politics come into play in decision making.
My MBA Thesis was about how the Internet would reshape the first-generation BI tools. In 1997, people were still trying to figure out what this Internet thing was. How do you make money on the Internet? There were not many buying activities, as it was more about having an online presence (a marketing front even). Previously, BI was all desktop client-server solutions. With the Internet, people could access a report from the browser without installing software on their desktops. I felt like this could be the way to democratize data and BI since the technical barriers to deployment had been removed.
A couple of months into my MBA program, I learned that I was pregnant. My son was born during finals, and it was very unusual. My professors were brilliant — they let me take exams from home. My classmates were phenomenal — they were great about collaborating with me during team projects. My time management and ruthless prioritization skills were effectively enacted here.
Initially, I had a few clients — small physician practice groups in Michigan. I helped them implement data analytics reporting solutions. I was also writing and teaching for The Data Warehousing Institute (TDWI) full-time. Clients usually found me via my writing. One of the first clients that I was proud of was the Washington Post. They read an article that I wrote for TDWI and asked me to do a consulting project. So writing was my primary marketing mechanism.
The highs were always some of the firsts: the first phone call from the first client, the first payment via a subscription service, the first pioneering research notes on cloud adoption in the data and analytics market, etc.
There are two axes evaluated in the magic quadrant: the ability to execute and the completeness of vision. People like to assign their own definitions to these things, so I think readers have to be careful about interpreting them. From a vendor perspective, it continues to bother me how thoughts get manipulated when people assign their own definitions.
Critical Capabilities report feeds the Magic Quadrant. The product capabilities are one element of the ability to execute in Gartner’s magic quadrant. So really, the critical capabilities are just a deeper analysis of the product capabilities.
Our culture of selfless excellence puts others or the team (including, by extension, the customers) ahead of oneself. This fits my work style. I often say it’s not about me; it’s about helping customers innovate. I don’t get upset about being the bearer of bad news, and I’m genuinely trying to help our clients move their data strategy forward.
Internally, ThoughtSpot has a Slack channel where one can call out an act of selfless excellence from any colleague in the world, at any level in the organization. People will vote on it for additional recognition. This also means no politics — No matter what function or level you are within the organization, you are coming together with the team to succeed.
ThoughtSpot’s mission is to create a fact-driven world. We believe that a fact-driven world is a better world. You can use facts to better navigate working capital in a pandemic for a company that’s trying to keep its operations running without laying people off. You can use facts to reveal details about diversity, inclusion, and equity by showing the differences in pay gaps or hiring trends. The way to get more people to be fact-driven is to remove technical barriers to using data. ThoughtSpot pioneered using search as a way to ask questions of your data without having to write SQL or do point-and-click. The product is a combination of search and NLP that lets the non-analysts interrogate their data.
AI is infused in our search capabilities — ranking your typeahead, telling you trending content others have created, surfacing that using a social graph to tell you who in your team is creating the best content, etc. That’s how AI informs all of the insights and content generation. Search is our core differentiator. When ThoughtSpot was initially launched and founded, the product used search to create new insights. When ThoughtSpot One was released in December 2020, the product now could use search to explore existing content. Whether you’re finding an insight that a colleague has made or creating a new insight, you start with just one interface.
Every year, I write top trends notes, and what’s more important is to figure out what to do about them. These notes are your New Year’s resolutions whether you are a data analytics leader or a BI professional in the space.
Data for good brings together a lot of different concepts. I think there’s greater attention being paid now to social issues. Some of it can be used for diversity, inclusion, and equity initiatives. But it also can be used to solve challenging problems like homelessness — how to make sure you have the right food at a particular shelter or enough bets at what occupancy rates? One of our customers in the data for good space is enabling oncologists to see what trends relate to different cancer types. A wide range of problems can be solved with data, and data for good is where organizations will contribute (whether software, expertise, or data).
In the early phase of your career, remember that it’s rarely about you when something happens. It’s more often about the other person, their insecurities or unconscious biases that they don’t want to acknowledge that they have. If somebody slights you, don’t take it personally. It’s not about you or your accomplishments or your sense of self-worth. It’s about the other person.