Here’s our rundown of what makes these 10 analytics vendors the biggest power players within the enterprise.
The landscape is in flux as web scale businesses continue to displace old-guard business analytics vendors and those vendors in turn seek to modernize and innovate to maintain their footing.
Here’s our list of the 10 most powerful companies in data analytics, offering everything from traditional BI to cutting-edge artificial intelligence and machine learning capabilities.
Amazon
Why they’re here: Amazon Web Services (AWS) offers more than 50 services for storing, processing, and visualizing data. For data lakes it offers Amazon S3 for object storage, Amazon Glacier for backup and archiving, and AWS Glue for data cataloging. On the analytics front AWS offers Amazon Athena for interactive analytics, Amazon Elastic MapRedue (EMR) for big data processing, Amazon Redshift for data warehousing, Amazon Kinesis for real-time analytics, Amazon Elasticsearch Service for operational analytics, and Amazon QuickSight for dashboards and visualizations. It also offers AWS Deep Learning Amazon Machine Images (AMIs) and Amazon SageMaker for platform services.
AWS was recently named a leader in Gartner’s Magic Quadrant for Cloud AI Developer Services. Gartner cited Amazon’s broad portfolio, visibility into the business sector from its storage and computing solutions, and visibility into the consumer sector via its online retail business and Alexa product.
AWS boasts that more data lakes and analytics are built on its services than anywhere else. Its customers include parent Amazon, as well as Nasdaq, Zillow, Yelp, 3M, and Vanguard.
Recent power moves: According to a scoop by The Information, AWS plans to double its sales team in 2020 as part of an effort to counter competitive pressure from Microsoft. The hires will focus on salespeople with deep technical knowledge in artificial intelligence and data analytics (and also cybersecurity).
By the numbers: According to statistics portal Statista, in 2019, 61 percent of technical professionals said their organization was currently running apps using AWS and another 16 percent said they were experimenting with the use of AWS apps. In a report released in February 2020, global technology market analyst firm Canalys found AWS held 32.4 percent market share in Q4 2019, slightly down from 33.4 percent market share in Q4 2018.
Outlook: Amazon is a powerhouse, but it’s facing increasingly stiff competition in the cloud from Microsoft and Google. Still, it’s large and ever-expanding portfolio means it has a lot to offer any customer.
Why they’re here: Google is locked in combat with Amazon and Microsoft for control of the cloud AI space. Its Google Cloud Platform offers natural language processing, computer vision, and AutoML services. Google competes in the data warehouse space with BigQuery. With its recent acquisition of Looker, Google has also stepped up its presence in the BI market. Looker won a lot of converts to its in-database design and native support for a wide range of cloud-based analytic databases, including Amazon Redshift and Athena, Google BigQuery, Microsoft Azure, and Snowflake.
Recent power moves: In 2019, Google acquired Looker Data Sciences in a $2.6 billion cash deal. The deal closed in February 2020. Looker competes with Tableau, Domo, and Microsoft Power BI. In March 2020, Google cherry-picked a high-profile deal from AWS when it announced that Google Cloud would become Major League Baseball’s new cloud services and cloud data and analytics partner for business operations.
By the numbers: In its fourth quarter fiscal 2019 results, released in February, Google broke out Google Cloud revenue for the first time: $2.6 billion in revenue, representing 53 percent year-over-year growth. Google specifically cited its data and analytics platform and infrastructure offerings as driving that growth. According to Canalys, Google Cloud had 6 percent market share in Q4 2019, up from 4.9 percent in Q4 2018.
Outlook: Much of the recent innovation in the analytics space can be traced to work initially done inside Google, but the company is still finding its footing against competitors such as Amazon and Microsoft. Looker may give Google’s BI efforts a shot in the arm, but it remains to be seen how Google will integrate the platform with the rest of its offerings.
IBM
Why they’re here: Once a high-flying BI innovator, IBM is now more of a niche BI player with IBM Cognos Analytics. To its credit, Cognos Analytics supports the entire analytics lifecycle, from discovery to operationalization. IBM is also continuing to build out analytics capabilities, including a Cognos Analytics Cartridge for Cloud Pak for Data to support analytic deployments and DataOps leveraging OpenShift containers. While IBM has slipped in the BI sphere, the company’s star still shines when it comes to AI services. Gartner points to IBM Research as a powerhouse in thinking about AI, though it notes the company is struggling to turn its leading-edge innovations into products. IBM Watson Assistant lets customers build, train, and deploy conversational interactions into their applications. Watson Visual Recognition helps customers process images, and Watson Video Enrichment analyzes audio, textual, and visual data within multimedia content. IBM Watson Studio is an environment to develop, train, manage models, and deploy AI-powered applications.
Recent power moves: In July 2019 IBM closed its $34 billion acquisition of Red Hat. Red Hat’s open hybrid cloud technologies were a key element of the acquisition, and they pave the way for IBM Cognos Analytics customers to migrate to the cloud.
By the numbers: IDC’s Worldwide Semiannual Artificial Intelligence Tracker for 2H18 found IBM held the largest slice of the AI market in 2018 with 9.2 percent. IDC pointed to new Watson solutions and services aimed at agriculture, customer service, human resources, supply chain, manufacturing, building management, automotive, marketing, and advertising as contributing to its dominance.
Outlook: In the BI space, IBM is currently playing catchup, according to Gartner, which notes that only a fifth of IBM’s reference customers considered Cognos Analytics their sole enterprise-standard platform for analytics and BI. That said, at the end of last year IBM introduced a new Cognos Analytics and Planning Analytics offering that promises unified planning and “what if?” analysis, which could give it a boost. In the AI services space IBM has a big presence but also a large and perhaps confusing array of AI service elements handled by different teams and using a variety of pricing schemes.
Microsoft
Why they’re here: Microsoft’s Azure platform is second only to AWS in terms of cloud-based data and analytics, machine learning, and cognitive computing products and services. It offers data preparation, visual-based data discovery, interactive dashboards, and augmented analytics through Power BI, which is available through Azure or on-premises. Power BI’s inclusion in Office 365 means it already sits in many enterprises.
Microsoft was recently named a leader in Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms as well as the Magic Quadrant for Cloud AI Developer Services.
Microsoft Azure, like AWS, boasts a slew of high-profile customers, including eBay, Boeing, Samsung, GE Healthcare, and BMW.
Recent power moves: In November, Microsoft unveiled Azure Synapse Analytics, a service that promises to merge enterprise data warehousing and big data analytics.
By the numbers: According to Statistica, 52 percent of technical professionals in 2019 said their organization was running apps using Azure, and 16 percent said they were experimenting. Canalys reported Azure held 17.6 percent market share in Q4 2019, up from 14.9 percent in Q4 2018.
Outlook: Microsoft crushed its second quarter fiscal 2020 numbers (reported in January) with much of that success attributed to growth in its Azure business. Microsoft reported 62 percent year-over-year growth for Azure in fiscal Q2 and 26.5 percent growth for its intelligent cloud segment. In the BI space, its on-premises offerings are not as complete as what’s on offer through the Power BI Pro cloud service, but it has staked out a strong position in that space and is vying for leadership with Amazon and Google in the cloud AI space.
MicroStrategy
Why they’re here: MicroStrategy has been a strong competitor in the business analytics space for more than 30 years, going head-to-head with other entrants on this list, including IBM, Oracle, and SAP. Gartner says MicroStrategy offers one of the most comprehensive analytics and business intelligence (ABI) platforms available, supporting both Mode 1 and Mode 2 analytic and reporting requirements. It complements its data connectivity, data visualization, and advanced analytics products with complementary mobile, cloud, embedded, and identity analytics products. The research firm says HyperIntelligence, MicroStrategy’s semantic graph, is among the most innovative product features of an ABI platform in the past two years.
Recent power moves: MicroStrategy 2020, the new version of the company’s flagship enterprise analytics platform, was announced in February 2020. It brings HyperIntelligence to the forefront. The HyperIntelligence semantic graph can be used as an overlay on websites, applications, and mobile sites to dynamically generate predefined insights. It adds Jupyter and RStudio connectors to support data scientists and adds support for deploying on AWS and Azure environments.
By the numbers: $133.5 million. That’s the fourth quarter 2019 revenue reported by MicroStrategy in January. That represents a 1.2 percent year-over-year increase, despite a 3.6 percent decline in product license revenue. The result, which beat analyst expectations by about $2.5 million, largely came down to increased adoption of cloud offerings and HyperIntelligence, according to CEO Michael Saylor.
Outlook: MicroStrategy hasn’t been gaining much traction with new customers, at least compared with its competitors, according to Gartner. That said, HyperIntelligence may prove a much-needed shot in the arm. It has opened its semantic layer to competing ABI platforms, which Gartner notes is a significant break from the tradition of proprietary architecture in that space.
Oracle
Why they’re here: Oracle is a powerhouse in the BI space, though it has faced increasingly fierce competition in the past few years. It has a strong focus on augmented analytics, leveraging natural language query and chatbot integration to provide more consumer-like experiences to users. It offers a full-stack enterprise cloud that features an integrated design experience for interactive analysis, reports, and dashboards.
Recent power moves: Oracle unveiled the Oracle Cloud Data Science Platform in February 2020, built on the foundation of DataScience.com, acquired by Oracle in 2018. The platform is geared for teams of data scientists working collaboratively, with capabilities including shared projects, model catalogs, team security policies, reproducibility, and auditability.
By the numbers: >100 percent. That’s the growth rate for Oracle’s Autonomous Database running in its public cloud, as related by Oracle CTO Larry Ellison when the company released its second quarter fiscal 2020 financial results in December 2019.
Outlook: Oracle has regained its position as a data analytics and BI visionary, according to Gartner. Oracle and Microsoft have been getting cozier lately with a cloud interoperability partnership that allows customers to connect Azure services, such as Analytics and AI, to Oracle services, including Autonomous Database.
Salesforce
Why they’re here: Salesforce is the largest CRM provider around, with a market share of 17.3 percent according to IDC’s Worldwide Semiannual Software Tracker. That’s more than triple the share of nearest competitors Oracle and SAP. It recently staked an even stronger claim with the acquisition of Tableau, a leader in data analytics and BI in its own right. Gartner considers Salesforce a visionary in the data analytics and BI space based on its Einstein Platform, which offers services such as a prediction builder, sales analytics, service analytics, and more. Salesforce is more of a niche player when it comes to AI cloud services. The company is positioning Einstein as a way to make AI accessible to every CRM and front-office user.
Recent power moves: In 2019 Salesforce acquired Tableau, one of the most powerful data visualization players around. Tableau has been innovating on its augmented analytics capabilities, including new features called Ask Data and Explain Data that offer natural language query and automated insights.
By the numbers: $15.7 billion. That’s how much Salesforce shelled out to acquire Tableau. It closed the all-stock deal in August 2019.
Outlook: Competitors are aggressively seeking to close the gap with Salesforce when it comes to embedded analytics, which is one of Salesforce’s key strengths. Salesforce continues to innovate in an effort to maintain its advantage. The Tableau acquisition gives Salesforce a lot of momentum, but there’s a fair amount of overlap in the product line. It remains to be seen how Salesforce will integrate Tableau with its existing Einstein Analytics offerings.
SAP
Why they’re here: Like Oracle, SAP is one of the giants in BI. SAP HANA is commonly found powering in-memory data marts and data warehouses. Its SAP Analytics Cloud integrates with its broader array of SAP business applications and offers a unified platform for planning, analytical, and predictive capabilities. It also offers SAP Digital Boardroom, which gives executives access to “what if?” analyses and simulations. Gartner says customers especially score its advanced analytics functions highly, including natural language generation and natural language processing functionality, automated insights, and support for automated time-series analysis and explainable findings. On the cloud AI front, SAP’s main offerings are Leonardo Machine Learning and SAP Conversational AI. Leonardo provides pretrained models and customizable models that can be used as a web service, while Conversational AI in an end-to-end platform for building conversational bots.
Recent power moves: SAP introduced its SAP Data Warehouse Cloud in May 2019, offering analytical and persona-drive data-warehouse-as-a-service geared for both business and IT.
By the numbers: >32,000. The number of SAP HANA customers cited by SAP in February 2020.
Outlook: SAP has a reputation as a stalwart of traditional AI and faces an uphill battle in terms of convincing customers to take a look at the new advanced analytics capabilities of SAP Analytics Cloud.
SAS
Why they’re here: SAS is another member of the old guard of analytics powerhouses, but one that has aggressively moved to add advanced analytics capabilities to its portfolio. Visual Analytics, part of the SAS Viya platform, offers a single, integrated visual and augmented design experience for data preparation, data visualization and analysis, and for building and operationalizing data science, ML, and AI models. SAS has been investing heavily in automation on the platform, including support for voice integration with personal digital assistants, chatbot integration, and natural language generation.
Recent power moves: SAS is finally moving to the cloud in a big way: The company has announced that its next-generation software stack will have a cloud-native architecture. The company has also moved to support the open source data science and ML ecosystem.
By the numbers: 47. SAS has a physical presence in 47 countries and a global ecosystem of system integrators.
Outlook: While SAS is moving to the cloud and tentatively embracing open source, it has been slow to do so, allowing competitors to steal a march on it. Now it’s playing catchup. Gartner notes that customers also say the cost of Visual Analytics limits broad deployment in their organizations.
Teradata
Why they’re here: Teradata has been a competitor in the business analytics space for more than 40 years. Teradata Vantage is the company’s data intelligence platform, which delivers analytics, data lakes, and data warehouses and can be deployed on-premises, in the cloud (including via the Teradata Cloud and public cloud options such as AWS, Azure, and Google), or in a hybrid model. Teradata QueryGrid is tightly integrated with Vantage. It’s a data analytics fabric that provides seamless data access, processing, and movement across multiple data sources.
Recent power moves: Once known for its specialized hardware appliances, Teradata has gone with a cloud-native architecture for Vantage and is trumpeting the ability to elastically and independently scale computer or storage, leverage low-cost object stores, and integrate analytic workloads.
By the numbers: 16 percent. That’s the decline in year-over-year revenues reported by Teradata in February. Recurring revenues, which now constitute the lion’s share of the company’s revenue mix, were up 6.7 percent year-over-year, but perpetual software license and hardware revenues were down 69.1 percent.
Outlook: Teradata continues to attempt to reshape its business. Forrester says some customers say Teradata’s zero administration functions and data modeling for specific use cases have limitations. But it also notes that customers like its ease of use, hybrid cloud and independent storage, and compute processing capabilities. Customers tend to turn to Teradata for hybrid deployments in which scalability and availability are critical factors.
Credit: Thor Olavsrud, CIO.com
