âData is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals to create a valuable entity that drives profitable activity. So must data be broken down, analysed for it to have value.â
These are the words of British mathematician and entrepreneur Clive Humby. Clive said these words in 2006, but, as anyone in the technology sector will know, these sentiments continue to ring true.
In the era of Web 4.0, data is the currency du jour and the ability to interpret it is paramount.
Data that has been analysed and organised can be the foundation stone for decision-making in the world of business, helping executives to solve problems and read patterns and trends that would otherwise be indecipherable.
As the gold rush for data has steamrollered its way through the past few years, data analytics platforms have been in higher demand than ever.
In this weekâs Top 10, we examine some of the most prominent and impressive data analytics platforms in use today.
10. Oracle Analytics Cloud
Founded: 1977 (Oracle)
CEO: Safra Catz
Notable feature: Augmented analytics and natural language processing
Oracle Analytics Cloud is a comprehensive data analytics platform that draws from the sheer power of Oracle’s enterprise software and database expertise.
The platform offers users a range of services, from self-service data visualisation to augmented analytics features.
It uses a combination of AI and ML to produce automated insights, anomaly detection and natural language query capabilities.
This allows users to ask questions of their data in plain English, making sophisticated analysis more accessible to a wider business audience.
9. Alteryx
Founded: 1997
CEO: Kevin Lynch
Notable feature: Code-free and code-friendly analytics process automation
With Alteryx, users will get an end-to-end analytics service, renowned for its intuitiveness and its ability to automate complex data processes.
It empowers analysts to prepare, blend and analyse data from a wide variety of sources using a visual, workflow-based approach.
Alteryx’s system is also designed to be easy to use, made with a “low-code, no-code” philosophy that enables users without a programming or coding background to build sophisticated systems themselves.
8. SAS Viya
Founded: 1976 (SAS), 2016 (Viya)
CEO: Jim Goodnight
Notable feature: Advanced analytics and AI capabilities built on a long-standing statistical foundation
SAS has been a titan in the world of analytics for decades and SAS Viya is its most modern offering.
Viya is a comprehensive suite of tools, giving users a way to do almost anything they can imagine, from data visualisation to advanced ML processing.
Its ability to handle large analytical workloads and its strong focus on model deployment and management make it especially well suited for industries like finance, healthcare and politics, where data analytics are especially crucial.
7. Qlik Sense
Founded: 1993 (Qlik), 2014 (Qlik Sense)
CEO: Mike Capone
Notable feature: Associative Engine for exploring data relationships
Qlik Sense is a powerful self-service data visualisation and business intelligence platform.
One of Senseâs standout features is its Associative Engine, which allows users to see not only the data theyâve selected, but also the data that is not associated with their selections.
This unusual feature can reveal hidden insights and unexpected relationships within the data, opening the doors to new levels of understanding.
Sense is another platform that values the democratisation of data analysis â one that empowers users of all technical abilities to freely explore data and create rich, interactive visualisations and dashboards.
6. Databricks
Founded: 2013
CEO: Ali Ghodsi
Notable feature: Unified platform for data engineering, data science and machine learning based on Apache Spark
Databricks was founded by the original creators of Apache Spark, another high profile analysis platform.
Databricks itself is built around Apache Spark’s powerful open-source distributed computing system and provides a unified environment for data engineering, which makes teamwork far easier.
The Databricks Lakehouse Platform combines the best of data lakes and data warehouses, offering a single source of truth for all data and analytics workloads.
This integrated approach simplifies complex data pipelines and accelerates the development and deployment of AI models.
5. Snowflake
Founded: 2012
CEO: Sridhar Ramaswamy
Notable feature: Cloud-agnostic data platform with decoupled storage and compute
In recent years, Snowflake has soared to prominence with its unique cloud-native data platform, quickly becoming one of the most widely used data technologies around today.
Its architecture that separates data storage from compute resources, allowing users to scale each independently and pay only for what they use.
Part of the reason for Snowflake’s meteoric rise has been its compatibility with some of the world’s most commonly used cloud technologies — AWS, Microsoft Azure and Google Cloud.
Integration with these providers has meant that Snowflake can offer businesses a level of freedom, ease and cost control that is naturally very appealing.
4. AWS Analytics
Founded: 2006 (AWS)
CEO: Matt Garman (AWS)
Notable feature: Comprehensive and integrated suite of analytics services
AWS has a vast portfolio of analytics services which has made it a dominant force in the data game.
From data warehousing with Amazon Redshift to real-time data processing with Kinesis and interactive querying with Athena, AWS has a tool for almost any analytics need a client might have.
The platform’s strength, though, lies in its ability to deliver bespoke solutions for companies.
AWS’ tech can be modular, it can be scaled according to business’ needs, it can be customised however it is needed.
In other words, AWS’ data software is pretty much comprehensive.
3. Google BigQuery
Founded: 2010
CEO: Thomas Kurian (Google Cloud)
Notable feature: Serverless, highly scalable cloud data warehouse
Googleâs BigQuery is a key part of Google Cloudâs offering.
With BigQuery, users can expect access to a serverless data warehouse, designed for speed and scale.
First announced in 2010 and made generally available in 2011, the basis of BigQuery was Googleâs internal Dremel data analysis programme, which served as its precursor.
It was built to handle petabyte-scale datasets with incredible speed, making it a go-to solution for organisations dealing with massive volumes of data.
BigQueryâs architecture separates storage and compute resources, allowing for independent scaling and a cost-effective 'pay-as-you-go' model.
Its integration with other Google Cloud services and its built-in machine learning capabilities enable advanced analytics and predictive modelling directly within the data warehouse.
2. Salesforceâs Tableau
Founded: 2003
CEO: Ryan Aytay (Salesforce)
Notable feature: Intuitive drag-and-drop data visualisation
Tableau, now a cornerstone of Salesforceâs offerings, has been a celebrated analytics platform for a long time, especially for its role in making data analysis more accessible.
Its core philosophy revolves around the power of data visualisation, enabling users to explore and understand their data through an intuitive drag-and-drop interface.
Founded in 2003 by researchers from Stanford University, Tableauâs patented VizQL technology translates user actions into database queries which allows users to manipulate data at high speed.
Visualisation is also a key feature of Tableau and has been an important part of its success, with graphs and images helping users to understand their data in a far more intuitive way.
It helps to uncover insights that might be missed in traditional spreadsheet-based analysis, fostering a culture of data-driven decision-making within organisations of all sizes.
1. Microsoft Power BI
Founded: 2011 (as Project Crescent)
CEO: Satya Nadella (Microsoft)
Notable feature: Seamless integration with the Microsoft ecosystem
First place goes to Microsoft Power BI, a platform that has made the Washington-based tech giant a global leader in the intelligence and analytics market.
Power BI was born from a Microsoft initiative named Project Crescent, which was first released to the public in 2011 before its official launch and rebrand in 2015.
Of its many strengths, one of Power BI’s most powerful is its user-friendly interface, which allows users to create interactive and immersive dashboards without needing to know how to do complicated coding or programming.
Then there’s Power BI’s seamless integration with other Microsoft products. Being able to link it up with Excel, Azure, Dynamics 365 or any other Office application makes Power BI a no-brainer for organisations already invested in the Microsoft ecosystem.
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