Data Analytics Companies and Competitive Analyses

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Data Analytics Companies and Competitive Analyses


Improved Employee Productivity

  • One unnamed top 10 U.S. University used Incorta to analyze large amounts of complex data. By implementing the Incorta system, they were able to greatly improve employee productivity and efficiently. In the words of their Technical Manager for their Central IT Department, "We set aside four hours for the initial proof-of-concept meeting with Incorta. But in just one hour we were able to load and analyze the data from millions of transactions — and see new insights. We were very pleasantly surprised, to say the least."
  • Broadcom uses Incorta to analyze "billions of rows of data and provide faster analytical insight to employees." This has increased employee productivity and thus improved the customer experience.

Fast Implementation Time

  • Customers also appreciate the fast implementation time to get Incorta systems up and running.
  • Guittard Chocolate was able to implement Incorta in just 45 days.
  • Nortek was able to implement Incorta in six weeks, which was faster than all other options they considered.

Limited Visualizations

  • Some customers feel that Incorta could add additional visualizations to improve their product.
  • One customer states "There is room to add more visualization capabilities."
  • Another user recommends that Incorta add "un-materialized views, OOTB JDBC/ODBC, [and] open visualizations."

Mobile Exploration and Authoring

  • The research team was unable to identify another Incorta feature rated poorly by more than one source after reviewing multiple customer reviews and competitive analyses.
  • However, we were able to identify that Incorta rates low (3.8) in Mobile Exploration and Authoring. This was their lowest rated subject area.


Highlighting Business Opportunities

  • Looker helps customers analyze data to find business opportunities.
  • For example, Deliveroo used Looker's self-service dashboards to "drill down to the details they need with a click [and] rapidly deepen their insights and drive the next phase of growth."
  • With Looker, Get Your Guide "quadrupled its business in two years time due to improved insights into conversion optimization and marketing results."

Self-Service Access to Data

  • Looker customers love that by using the system, they can allow their employees self-service access to data, rather than employees having to wait for reports from data analysis teams.
  • GetYourGuide states that by implementing Looker they "reduced ad-hoc requests to Data Platform team by 75%, freeing up three to four full-time employees."
  • Futureplay implemented Looker which allowed "team members acquire Self-service access to trusted data with Looker’s consistent, governed metrics."
  • Hubspot states that the self-service access made available by Looker "puts an end to waiting for analyst reports."

Challenging Implementation

  • Customers using Looker have complained about the implementation process.
  • One user states that Looker's "implementation is very difficult.... Looker requires a lot of development to create beautiful dashboards that are a breeze to design in Tableau or Power BI."
  • Another customer states that they made some mistakes in the implementation phase, "which in hindsight were probably the wrong call."

Need for SQL Knowledge

  • Another common complaint with Looker was the need for advanced SQL knowledge to implement the product.
  • One user states "It requires good knowledge of SQL. (but once everything has been built, the users do not need it.)... It is less suited for companies with no data team and no SQL knowledge."
  • Another user stated that "Custom SQL writers can be slow."


Ability to Access Data Anywhere

  • One major benefit of Starburst is the ability for companies to access their data from anywhere, whether it is stored on-premises or in multiple cloud locations.
  • According to Matt Fuller, co-founder and VP of engineering at Starburst, "Presto is everywhere data engineers and administrators want to be, whether it's any of the major clouds, containers, or on-premises. Data architects do not have to conform their environments to Starburst Presto; it just plugs right in and is ready to go, querying data no matter where it lives."
  • According to Xconomy, Starburst "allows users to run fast, efficient analytics on multiple types of data, wherever the data live (for example, in Hive, Cassandra, or relational databases). And that means a lot less hassle preparing the data for analysis, which traditionally has been done using the “extract, transform, load” process for managing databases."

Query Optimizer

  • Starburst's query optimization tool allows for faster queries. According to Justin Borgman, co-founder at Starburst, "Internal performance testing and customer bench marking [of the Cost-Based Optimizer (CBO)] show a greater than 10x performance improvement for many analytical queries such as those in TPC-H and TPC-DS."
  • The CBO tool also ensures all queries are completed — as opposed to some queries returning incomplete with the CBO tool disabled. As such, the CBO tool was a great improvement for Starburst.


  • The research team attempted to find weaknesses for Starburst by reviewing third-party media and looking for customer reviews. We were however, unable to find any customer reviews. In the third-party media, we were able to find some highlighted issues with past versions of Starburst Presto, such as incomplete queries, but were not able to find any complaints with the current version.



  • Customers appreciate that Databricks is scalable, so the product can grow with the company into the future.
  • Daniel Jeavons, General Manager for Data Science at Shell, states: "What we really like about Azure Databricks is that it runs on top of a very stable and mature installation of Spark. It also interacts closely with Kafka. We can easily scale up and retrain our models on a continuous basis and deal with our intensive computing needs. And because Azure Databricks is highly elastic, we get really powerful spin up/spin down capabilities, and our developers love its neat, elegant user interface."


  • Customers love that Databricks is a very flexible product, and can be quickly set up to handle new projects.
  • LINX also appreciates this flexibility. In the words of Thomas Gianniodis, their General Manager IT, "Some of the datasets we get from the business aren’t standard. We couldn’t have loaded that data without a platform as flexible as Azure Databricks."


  • Many customers stated that while they loved the Databricks product, it was expensive.
  • One customer stated: "The costing is high as compared to other tools available in market which makes it less favorable when it comes down to making a selection."


  • Many customers complained about the speed of the product. For example, one customer stated: "UI could be better. UI is slow which I believe would be because of many functionality at one place making it heavy."
  • Another user provides a specific example: "Union on 50GB+ csv file took several hours, hopefully we can focus on performance in the future. Mostly performance on data fetching from Database through API should be worked upon, it sometimes takes hours to get few gigabytes of data."

Amazon SageMaker


  • Customers appreciated how scalable SageMaker is. For instance, a GE Healthcare representative states: "The scalability of Amazon SageMaker, and its ability to integrate with native AWS services, adds enormous value for us."
  • According to VooDoo, "By standardizing our machine learning and artificial intelligence workloads on AWS, we’re able to iterate at the pace and scale we need to continue growing our business and engaging our gamers... With AWS machine learning, we were able to put an accurate model into production in less than a week, supported by a small team, and have been able to build on top of it continuously as our team and business grow."

Reduce Costs

  • Customers have used Amazon SageMaker to reduce operating costs. Formosa Plastics, for example, used SageMaker to reduce manual labor costs in production.
  • Zendesk states, "Amazon SageMaker will lower our costs and increase velocity for our use of machine learning."
  • Slice states, "We use a wide variety of AWS services to support our business, including AWS Machine Learning to help connect customers with the best insurance options given their needs. In our work with insurers and technology companies seeking to build and launch intelligent insurance products, we’ve seen tremendous cost savings and productivity benefits with AWS. For example, we’ve reduced procurement time by 98%, from 47 days to 1 day."

Limited Resources

  • Customers desired additional documentation and resources for learning about and working with SageMaker. One customer states: "I feel there could be more helpful resources available for the getting started that would help."
  • Another writes "Not enough documentation available on latest features like Batch Transform."

User Interface

  • Some customers complained about the UI for SageMaker, stating comments like "features like drag and drop are missing which are available with other tools. On a larger dataset sometime it takes a longer time than expected."
  • Another user states: "UI doesn't exactly tell you if you've got unused instances or deployed models lying around unlike the GCP equivalent."

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