The 74th episode of Datacast is my conversation with Cindi Howson — the Chief Data Strategy Officer at ThoughtSpot. She is an analytics and Business Intelligence expert with over 20 years of experience and a flair for bridging business needs with technology.
Our wide-ranging conversation touches on her love for writing growing up, the evolution of BI tools over the decades, how to balance academic study and parenthood, her work at BI Scorecard and Gartner, her current journey as the Chief Data Strategy Officer at ThoughtSpot, the consumerization of enterprise analytics, data for good, and much more.
Please enjoy my conversation with Cindi!
Listen to the show on (1) Spotify, (2) Apple Podcasts, (3) Google Podcasts, (4) TuneIn, (5) RadioPublic, (6) Stitcher, and (7) Breaker.
Key Takeaways
Here are highlights from my conversation with Cindi:
On Studying English
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.
On Business Intelligence in the mid-90s
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.
On Her MBA
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.
On Balancing Academic Study and Parenthood
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.
I got the work ethic from my father. He was a workaholic and an incredibly hard-working and overachieving perfectionist. I tried to dial some of that back and pursued more excellence rather than perfection.
In terms of time management, I would be very strict about what my core study and working hours were by time-boxing them. I learned how to switch off given the time that I had. Otherwise, I’d miss a deadline or cut into family time.
On Founding BI Scorecard
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.
On Gartner’s Magic Quadrant for Analytics and BI Platforms and Critical Capabilities
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.
For the ability to execute, we looked at customer feedback based on customer experience, user enablement, business benefits, quality of operations, product functionality, etc. We also did hands-on testing of the software.
For the completeness of vision, we looked at their marketing strategy, sales strategy, and product roadmap.
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.
On The Culture of “Selfless Excellence” at ThoughtSpot
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.
On The ThoughtSpot Product
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.
On Top Trends and Predictions for Data, Analytics, and AI in 2021
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.
Fight or replace flight or fear: 2020 was a lot about reacting. 2021 is about being more strategic, where customer experience analytics takes center stage. A lot of organizations currently look at customer data in a siloed way. They need to adopt a customer 360 approach to anticipate customer needs and design products/services that customers are most likely to want and stay with.
Data sharing is what I’m super excited about: Technologies like Blockchain or Snowflake Cloud enable external data sharing. The concept of data marketplaces has been around for a while, but it’s been hard to do that in an accomplished world. I like a quote from a customer saying that data sharing in the cloud is the end of FTP as we know it.
People change management becomes a core CDO responsibility: This is where we keep throwing tech out there but ignore the people impact adequately.
Data science loses its luster and sex appeal.
Data exposes the wide gap in equity, but it also empowers people to drive change.
On Data For Good
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).
On Advice for Female Data Practitioners
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.
Timestamps
(02:03) Cindi briefly shared her early interest in writing and her decision to major in English at the University of Maryland in the mid-80s.
(05:22) Cindi talked about her move to Zurich for a Business Systems Specialist role at Dow Chemical.
(07:35) Cindi recalled the state of Business Intelligence tools and their adoption level in the enterprises during the mid-90s.
(10:53) Cindi went over her decision to pursue an MBA from the Jones Business School at Rice University, in which her MBA Thesis was about how the Internet would reshape the first-generation BI tools.
(16:30) Cindi discussed how she balanced academic study and parenthood during her MBA.
(20:57) Cindi talked about her proudest accomplishments as a manager at Deloitte — building BI and analytics practice in Houston.
(22:48) Cindi went over her time running her independent analyst firm BI Scorecard, which advised clients on BI and analytics tool selections via rigorous evaluation criteria.
(26:14) Cindi brought up her time teaching classes at The Data Warehousing Institute, which educates business leaders on the proper deployment of data warehousing strategies and technologies.
(27:49) Cindi mentioned her move to become the Vice President in data and analytics at Gartner.
(30:33) Cindi walked through the end-to-end process of creating Gartner’s Magic Quadrant for Analytics and BI Platforms and Critical Capabilities.
(33:47) Cindi explained the culture of “Selfless Excellence” at ThoughtSpot — where she currently serves as a Chief Strategy Officer.
(36:11) Cindi explained the concept of “What’s In It For Me” (WIIFM) that helps bring a data-driven culture to organizations.
(39:34) Cindi gave a tour of ThoughtSpot’s core capabilities, ranging from SearchIQ and SpotIQ to ThoughtSpot One and ThoughtSpot Embrace.
(43:04) Cindi broke down her responsibilities as a Chief Data Strategy Officer working with internal and external stakeholders.
(44:40) Cindi emphasized the role of partnerships between startup vendors to empower the future of BI analytics (Read her article A New Era in Analytics and BI”).
(49:58) Cindi recapped takeaways from ThoughtSpot’s ebook that presents 6 Top Trends and Predictions for Data, Analytics, and AI in 2021.
(53:07) Cindi gave advice to companies that want to bring consumerization to enterprise analytics.
(56:35) Cindi gave her two cents on the movement of Data For Good in the progress of analytics and AI in the near future.
(58:58) Cindi recapped insights that she has observed from hosting The Data Chief Podcast (which features interviews with some of the most successful data leaders).
(01:03:29) Cindi gave advice to female data practitioners in the early phase of their careers (Read her article on the challenges that keep women out of tech).
(01:05:30) Closing segment.
Cindi’s Contact
Mentioned Content
Blog Posts
“Why I Joined ThoughtSpot” (April 2019)
“A New Era in Analytics and BI” (August 2019)
“Perfect Storm or Transformative Triumvirate: Data for Good, Data for Evil, and AI Ethics” (Nov 2019)
“We Can Put a Man on the Moon, But We Can’t Keep Women in Tech” (Sep 2019)
6 Top Trends and Predictions for Data, Analytics, and AI in 2021 (2021 E-Book)
Published Books
“Successful Business Intelligence” (Nov 2013)
“SAP BusinessObjects BI 4.0” (Nov 2012)
Data for Good Resources
Women in Data Resources
People
Joy Buolamwini (Computer Scientist and Digital Activist at MIT Media Lab, Founder of the Algorithmic Justice League)
Cathy O’Neil (Author of “Weapons of Math Destruction”)
Kate Strachnyi (Founder of DATAcated)
Ralph Kimball (Original Architect of Data Warehousing)
Ajeet Singh and Amit Prakash (Co-Founders of ThoughtSpot)
Recommended Books
“Moneyball” (by Michael Lewis)
“Freakonomics” (by Steven Levitt and Stephen Dubner)
Notes
My conversation with Cindi was recorded back in April 2021. Since the podcast was recorded, a lot has happened at ThoughtSpot:
They unveiled their new vision for the Modern Analytics Cloud — a simple, actionable, and open approach to cloud analytics that’s redefining how companies deliver value from across the entire modern data stack.
They acquired Diyotta & Seekwell. With Diyotta, they’re expanding the number of integrations with other cloud companies, while Seekwell gives customers the ability to operationalize insights by connecting analytics to other systems to trigger action.
ThoughtSpot Everywhere launched as the first development platform to build interactive data apps with search and AI-driven analytics.
Cloud growth: They announced major growth in their SaaS and cloud offerings, including their first 100 SaaS customers, 250% ARR growth from cloud products, and planned headcount growth.
About the show
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.
Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.
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