We’re thrilled to announce Tellius has been named a Visionary in the 2022 Gartner® Magic QuadrantTM for Analytics & Business Intelligence Platforms — a rigorous independent evaluation of 20 analytics software vendors based on their Ability to Execute and Completeness of Vision.
We feel the true achievement here is not just the placement, but instead in how it was accomplished. We have seen companies struggle to bolt-on innovations such as natural language interfaces onto their traditional reporting and dashboarding platforms. Meanwhile, modern organizations who wish to incorporate AI into their analytics programs have to rely on a separate set of automated machine learning tools. Customers are left taking a piecemeal approach to data analysis, stitching together a complex workflow connecting various tools and multiple copies of data. Tellius takes a different approach, built with AI/ML at the core, architected for cloud scale, combining ad hoc exploration, visualization, insights generation, and machine learning, in a unified experience for business users and data experts to collaborate. The promise of decision intelligence is delivered.
The 2022 Gartner® Magic QuadrantTM for Analytics and Business Intelligence Platforms is based on the rigorous evaluation of 20 vendors on both the Completeness of Vision each vendor sets forth and their Ability to Execute on it.
At Tellius, we’ve been rapidly expanding and advancing our decision intelligence platform to enable data and business teams to drive new data analytics and AI Use cases and to unlock the value in all of their data, and we’re very pleased to see that work recognized. While we are just scratching the surface, we believe these are the biggest strengths that contributed to our placement in the Gartner® Magic QuadrantTM.
One platform to answer ‘what’, ‘why’, and ‘how’
The critical last mile of the data to decision journey — the analysis layer — remains frustratingly inefficient, despite significant advances in upstream layers of the modern data stack such as ingestion, storage, and transformation. Analysis is manual, time-consuming, and fractured amongst numerous tools and teams. One person cannot use one tool to answer the necessary ‘what’, ‘why’, and ‘how’ business questions to make better data-driven decisions. Each shard of the fractured analytic layer has unique challenges:
- Dashboards provide some visibility into the ‘what’ for business users — but are guard railed experiences with limited drill-downs on backwards-facing, aggregated data.
- SQL slicing & dicing allow analysts to get to the ‘why’ but are inefficient, unscalable, and impossible to analyze thousands of variables of granular data without automation.
- Data Science tools help with ‘why’ and ‘how’-tye questions but require coding skills and/or statistical know-how and produce outputs largely out of reach to the business.
The result is a growing and costly insights gap where 76% of leaders rely on gut or heuristics rather than data for decision making.
Instead of incrementally improving pieces of the analysis layer separately, our approach is a single platform optimized for seamless transitions between ‘what’, ‘why’, and ‘how’-type questions to short-circuit the analytics layer and drastically cut down the frustrations and time between questions and answers; people, analysis modes, and technologies. Our platform helps users answer:
- “What?”: NLQ search interface, AutoViz, and drag-and-drop dashboards.
- “Why?”: automated key driver, cohort comparison, and root cause analysis insights accompanied by NLG summaries.
- “How?”: automated underlying segments and signals detection and AutoML predictive analytics to reveal potential areas for decision makers to take action.
Our unified platform short-circuits the analytics layer. It dramatically reduces ad hoc exploration, analysis, and insights generation time to allow businesses and analytics teams to rapidly ask and answer business questions and focus their time on making business-impacting decisions.
Siloed tooling for business users (dashboards, reports, spreadsheets), analysts (SQL), and data scientists (code notebooks and DSML platforms) hinder critical domain expertise from working it’s way into analysis and drives up the risk of uninformed or biased decision making.
The Tellius platform was designed from the start for multi-persona usage, combining the ease of use of a Google-like search interface (for ad-hoc analysis) with the power and scalability of an Apache Spark-based distributed compute engine (for robust ML-powered automated insights and predictions). This combination enables anyone — regardless of their analytical skills — to quickly ask and answer ‘what’, ‘why’ and ‘how’-type questions from granular business data thereby leapfrogging manual analysis to ultimately make better, faster, insights-driven decisions.
No other analytics vendor provides the breadth and depth of automated insights that we do.
Our platform automates complex data analysis to uncover root causes, understand key business drivers, compare cohorts, and identify meaningful segments in data, going beyond first-order facts/drivers. Out of the box, Tellius is equipped with a variety of insights:
- Cohort Analysis provides reasons for differences among values or cohorts (e.g. what attributes delineate high customer lifetime value customers vs low one?)
- Trend Drivers provide reasons for differences in metrics/KPI over time (e.g. what are sales declining quarter over quarter?)
- Segment Drivers use machine learning to identify key drivers (e.g. why is customer retention lower in Q4 vs Q3?)
- Anomalies & Outliers automatically spot unexpected shifts in business (e.g. are there fraudulent transactions?)
Unaggregated Data, Cloud Scale, and Intelligent Compute
Everyone claims their products are highly-performant and operate at enterprise scale. At Tellius, our architecture was purpose-built for cloud scale and AI.
We’re the first decision intelligence platform to handle ad hoc query and compute-intensive ML/AI workloads, raising data scale by 100x and speeding discovery of insights from hours to seconds. We achieve this through our unique dual analytics engine which intelligently routes queries to the most efficient engine:
- Live Query/Insights: When performing ad hoc analysis or deriving deep insights from TBs of data in cloud warehouses like Snowflake, Redshift, and Google BiQuery — Tellius pushes down queries and insights directly to the underlying cloud source in a zero movement manner, saving time and resources
- Spark-based Distributed Compute for Deep Insights/ML Jobs: Tellius utilizes Apache Spark for scalable data processing
- Subsecond in-memory compute: Tellius’s lighting-fast columnar engine manages in-memory ad hoc queries when querying data across multiple locations.
Tellius’s intelligent architecture handles the underlying NLP, NLG, and ML-based technologies so the analytics experience just works for end users.
All Industries, Countless Use Cases
Customers across industries and use cases derive value from Tellius each day:
- A multinational CPG firm faced a growing analytics backlog from the brand management teams for new dashboards and one-off analysis such as “why did market share from [Market X] decrease compared to other stores?”. Slicing and dicing the firm’s 40+ TBs of granular sales and inventory data across all brands to spot insights — literally billions of rows and thousands of columns — was so overwhelming and time-intensive that most brand teams resorted to heuristic-driven decision making rather than data-driven decisions, cumulatively threatening customer retention and sales goals. Tellius enables the brand team users to explore TBs of data in an ad hoc, Google-like way to quickly get at the ‘why’ behind the dashboard’s ‘what’. Tellius’s automated insights have helped the firm identify up to 65% more sales opportunities which translates into millions of dollars in revenue. Furthermore, business users and the analytics teams alike have improved capacity and capabilities to track KPI performance across accounts, brands, channels with a newfound ability to understand key drivers of per-store sales lifts across a variety of dimensions while assessing market share performance fluidly.
- A major life science firm’s rebate team was manually joining and culling through multiple internal datasets and 3rd party plan and formulary data in an attempt to spot disputable rebate claims. Costly mistaken payments unfortunately regularly occurred, due to analysis capacity constraints to spot disputable claims in the contractually allotted time window. Tellius now orchestrates and automates Invoice data processing, enrichment, and monitoring. Automated insights uncover root cause analysis across all sources to spot disputable rebate dollars. Analysts easily answer ad-hoc questions from the leadership team prior to paying out any rebates. The result is that the rebate team now identifies $5M+ a year in disputable rebate dollars. And this is just the tip of the iceberg as Tellius is used for a number of other use cases including optimizing investment in Market Access; identifying more growth opportunities and automating brand performance deep dives to improve commercialization efforts; and reducing analysis time significantly while identifying bottlenecks in Quality Process Cycles to optimize Supply Chain and Quality efforts.
- A tech startup’s 2-person data engineering team (part of the IT department) felt trapped generating manual corporate-level operational reporting and dashboarding across 40 brands from 150+ disparate data sources — a process that took about 3 weeks. Requests for new and updated reports and dashboards had formed backlogs, and brands increasingly grew misaligned, crippling firm performance. The IT team had minimal time and money to fix the issue. Tellius’s self-service augmented analytics capabilities drastically reduced the analytics backlog (freeing IT of reporting) while unlocking a 360° view of business across brands/data resulting in faster decision making and better customer service. In one case, they experienced a 90% time reduction analyzing churn and reporting (80h → 3h via automation). Tellius serves as the corporate level reporting across the brands as well as brand-level reporting
We’ve had an exciting journey since launching in 2016 and emerging from stealth in 2018.
Tellius has gained considerable traction in the growing decision intelligence market, empowering customers across industries — including multiple Fortune 500 companies — to augment human expertise with AI/ML for better and faster business decision-making. The company was also named a 2019 Gartner Cool VendorTM and has raised more than $17M in funding to date.
We believe that the recognition today from Gartner is further validation of our vision to reduce the prerequisites for drawing deep insights from granular data down to one element: curiosity. And we’ve only just begun scratching the surface.
We are committed to continued innovation and pushing the boundaries of what’s possible once siloes among people and tools are broken and the barrier to analysis is finally lowered for everyone to participate.
We hope you will join us on our journey to a world of better, faster insights-driven decisions.
View a complimentary copy of the 2022 Gartner® Magic Quadrant™ for Analytics & Business Intelligence Platforms report to learn more.
Gartner “Magic Quadrant for Analytics and Business Intelligence Platforms,” 22 March 2022, Austin Kronz, et. Al
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