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ThoughtSpot Aims To Build The Democratic ‘Modern’ Data Stack

Technologists loves democracy. As an over-arching trend, we can say that IT product owners (be they individual software developers, wider teams or entire communities) want their technology services, applications and physical or virtual devices to become so widely adopted and ubiquitously deployed that they reach a level where they need to be democratized.

The democratic process in these scenarios is not centered around ballot papers and votes (although it is in a cerebral and slightly neural sense, because ‘the next Twitter’ is only ever created through public perception leading to mass-market adoption), this is democracy in the sense of open to all, inclusive ease-of-use and freedom of access.

In the modern era of computing – and let’s make that roughly the post-millennial web-centric cloud-native mobile-enriched age that typifies the last quarter-century – we have seen smartphone access, application usage and now even a degree of Artificial Intelligence (AI) all become democratized.

With all that being said then, what element of the total IT stack needs to enjoy democratic freedom next? The answer is of course data – and the ability to use data analytics.

Democratic data analytics for all

Going to market with what it calls its Modern Analytics Cloud service, ThoughtSpot is aiming to make data analytics the next key democratic access point for all users. As well as expanding its own platform’s scope in terms of data workflow competencies, compatibilities and connections, the company is also now working to put its Google-search-like data analytics tools in the hands of more people, across more tiers of a business across more types of organizations from small to medium to large… and wider, to educational institutions and nonprofits.

According to Ajeet Singh, co-founder & executive chairman for ThoughtSpot, the proliferation of cloud, mobile and new data sources over the last ten years have set the stage for transformational change for every business and sector in society. Now, the rise of new technologies like AI, web3 and 5G are taking this transformation further than ever before, creating what he calls ‘a generational shift’ in power brought about by the way we use and interact with data at its core.

“The more we talk to customers, the more it’s clear they need an entirely new experience layer for [data] analytics if they want to realize the potential of the modern data stack. The tools built for desktops that deliver static [information] visualizations simply won’t cut it in the decade of data,” said Singh. “With our [new platform] capabilities, we’re answering the call of customers. Whether they’re looking to bring insights to everyone in their organization, build products and apps with Live Analytics, or launch new use cases, we’re ready to help them make that possible with the Modern Analytics Cloud.”

A new (data) experience layer

If we hear firms like ThoughtSpot talk about a new ‘data experience layer’, the term itself may be at risk of being fuelled by marketing spin and branding. But cheesy or not, it does mean something in this context because it seeks to explain the way actual businesspeople and consumers will now come into contact with cloud-based data analytics in their everyday lives.

Properly used and appropriately embraced, this is the kind of data power that will now change the way organizations build and operate their very business models; it’s also the kind of intelligence that might influence whether or not you miss a flight, what kind of automobile you decide to buy next and maybe even how fast your geo-location-powered pizza delivery gets to your door… and whether or not you got the special free anchovy-pepperoni topping due to customer loyalty.

Whether it’s major business supply chain shifts or pepperoni perks, it’s all about how we experience every layer of data analytics as it manifests itself in modern IT stack services from the cloud.

“Organizations that insist on operating using last decade’s data playbook will fail to take the next step forward into the data decade. Enterprise employees need to realize that proposing a bold move to embrace a modern data stack doesn’t automatically mean getting pushed back – more often, it is the traditionalists hiding behind old methods that will now find themselves ultimately getting pushed out,” said ThoughtSpot CEO Sudheesh Nair.

What is a ‘modern’ data stack?

This talk of the modern data stack is meant to mean an IT function that works with live analytics that functions in the moment and at the fulcrum of where actual business decisions are made. The ‘old’ data stack (if there actually were such a term) is meant to express the more staged and scheduled world of database-modelling-analytics-visualization & reports that we knew as the standard in pre-millennial times.

“If enterprise data is just a pawn and does not form the fabric of a modern data-driven business, then it may still make sense to use the old methods, but this is not progressive forward-thinking business sense,” added Nair.

Data-centric decision making could (if we follow the suggestions tabled here) see us live in a future where everything down to our television watching habits are finely tracked on a bi-directional basis; this could mean that TV script writers ‘steer’ not just storylines, but also the amount of screen time an individual actor or subject matter gets coverage. It could even change the way the press write stories to attract readers, as if SEO-optimized contrived headline management wasn’t bad enough already.

Will the new digital world with eminantly data-driven decision making be a place with less color? Perhaps it may admits Nair, but a lot of it is simply reinforcing human behavior and, equally, we can also use these same controls to put new color into our lives and hopefully make the world a better place.

Deep in the ThoughtSpot data motherlode, the company is now working to bring new elements and functions to its Modern Analytics Cloud service to enable organizations, whether startups or established enterprises, to connect, build, launch and scale the power of data.

Data connection central

It is important we realize that data on its own that resides disconnected from any meaningful business IT system and so is of little value. Seeking to forge secure connection points to the most relevant integration points in the modern IT landscape, ThoughtSpot notes its connection to the Snowflake Data Marketplace. Snowflake is a cloud data organization that offers a single fully managed Software-as-a-Service (SaaS) platform for data warehousing, data lakes, data engineering, data science and data application development.

ThoughtSpot’s work with the Snowflake Data Marketplace service will enable ThoughtSpot users to use the new Snowflake Data Explorer application, a software tool designed to create insights from third-party data that may reside in the Snowflake Data Marketplace. This makes it possible to ‘rapidly prototype insights’ with new data sources for leading indicators; a term itself which extends our previous notion of what a prototype might be i.e. this is not a new shaped can opener, this is a new shape for data analytics and the way it might be applied to a working business use case.

ThoughtSpot also highlights its connections to and support for AWS Redshift serverless so customers can use its Modern Analytics Cloud to run and scale analytics on Amazon Web Services without having to provision and manage data warehouse clusters. Further important connections also exist to Databricks.

Build with data, like software

Aiming to promote and strengthen the ‘create and build’ element of its technology proposition, ThoughtSpot highlights CodeSpot. This is a searchable repository of open source ThoughtSpot blocks and code samples for software developers to accelerate the process of embedding analytics into application development with ThoughtSpot Everywhere, which is the company’s low-code embedded analytics offering.

CodeSpot includes reusable, best practice examples of the most common [data analytics] development tasks, such as coding for custom actions, creating data visualizations or various types and working with Application Programming Interfaces (APIs) and associated tooling and formulas etc.

Covering the launch cornerstone in the quest for data analytics, we know that data professionals and analytics engineers are facing increasing demands from businesses to get use cases from development to production quickly. New features from ThoughtSpot aims to arm these professionals with the capabilities they need to do so at scale.

These include integration with dbt Labs, a transformation workflow that lets teams collaboratively deploy analytics code following software engineering best practices like modularity, portability, Continuous Integration & Continuous Deployment (CI/CD) and documentation.

This technology enables data analytics engineers to translate dbt models to TML (ThoughtSpot Modeling Language). With dbt, data professionals can manage increasingly complex data engineering and analytics workflows to model data quickly, then make these immediately consumable.

Also here we find SpotApps, a technology that makes getting up and running with use case templates for critical use cases simpler. These new SpotApps include different templates for transaction systems like ServiceNow, Snowflake, HubSpot, Okta, Google Analytics, Google Ads, Jira, RedShift and Databricks.

Scalability tablestakes

So to scope for scaling and scalability, which really now should represent the concluding validating chapter in any rational technology proposition.

In the company’s own words here then, “The transformative value of data can only be realized when it’s possible to scale these [data analytics] initiatives to every corner of the business quickly and efficiently. To make this achievable, we have introduced new capabilities, such as ThoughtSpot Sync, which enables companies to operationalize insights by using them automatically trigger actions in other applications and services through APIs.”

Additionally in the scalability arena here we find a key monitoring function. This is intended to allow organizations to scales analytics further by eliminating the need to log into an analytics platform. Instead, users get automated insights that alert them to changes as they happen. Users can simply create Key Performance Indicators (KPIs), then the system continuously scans for changes, outliers and anomalies.

4-cornerstones: connect, build, launch & scale

Can we apply ThoughtSpot’s four cornerstones of ‘connect, build, launch and scale’ to any modern enterprise technology mission with any vendor? Possibly yes, although that may be too simplistic a template to lay down – and the waters could become muddled or muddied depending on how much of any given IT stack is in-house developed vs. acquired and bought.

Democracy in the context of this discussion is of course meant to express a technology’s ability to be used by everyone in an organization. The way this story has elements of portability, connectivity and extensibility do (arguably) give ThoughtSpot enough credit to lay claim to data analytics inclusivity and extended usability.

Data analytics should certainly be by the people, of the people and for the people, so let’s at least vote for that.