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My view on Oracle’s position in Gartner Magic Quadrants for Analytics and Business Intelligence Platforms

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Gartner has published its Magic Quadrants for Analytics and Business Intelligence Platforms (Gartner, 2023) report for 2023, on April 5th. 

As usually, it presents how various analytics and BI vendors position regarding the completeness of their vision and ability to execute. It is considered as “the go-to resource” when evaluating and comparing these technologies.

In this post, I am trying to look at Oracle’s position and explain why Oracle is placed where it is placed - in Visionaries quadrant. Of course, everything from here on, is my personal view and experience.

Personally, as I said, I am mostly interested in Oracle’s position which is (if I recall correctly) in Visionaries quadrant for the 3rd straight year in the row. It seems that Oracle with its Oracle Analytics Cloud has the strongest completeness of vision among all Visionaries, and in general only Microsoft seems stronger in this respect. But on the other hand side, there is quite a crowd of vendors in Visionaries quadrant.

Getting back to Oracle’s position, Gartner says that the key Oracle’s strengths are in (Gartner, 2023):

  • Enterprise cloud data and analytics, 
  • Augmented capabilities throughout and 
  • Comprehensive data management.

 So what exactly is meant by that?

Enterprise cloud data and analytics

Oracle offers an end-to-end cloud solution, including infrastructure, data management, analytics and analytic applications, with data centers in cloud regions globally. In addition, Oracle Fusion Analytics offers native integration and closed-loop actions for Oracle’s ERP, human capital management, supply chain and NetSuite products, making it an excellent choice for Oracle business application users (Gartner, 2023).

Oracle’s cloud service are currently available in 41 public cloud regions (centres) in 22 countries, and in each region Oracle Cloud offers more than 100 cloud services from infrastructure and platform to applications. 

For example, the following Gartner’s document describe this in more detail:

When Gartner says end-to-end analytics solution, the key Oracle’s cloud services are:

And if I look further into analytics, then things to point out is definitely Semantic Modelling (or as it was called Repository or Metadata Modelling in the past) which has traditionally been one of the key Enterprise features of Oracle Analytics (or formerly known as Oracle Business Intelligence). And still is. It provides users a secure, centrally managed, metadata models that are used in majority of pre-built or custom-built analytical applications, regardless of the fact if they are still using “old” Interactive Dashboards and Reporting (from OBIEE days) or use modern Data Visualisation tools.

Data Management (dataset management that are not modelled in semantic models) support is another great “enterprise” feature which has been added with the new generation of tools that were introduced in Oracle Analytics. With data management, (business) users are able to use data files or direct SQL queries as datasets that can be used in analyses without the need to bring these data sources to the semantic model (only possibility in the past), without loosing the governance required for enterprise type of reporting. Users are able to define their own datasets (ie. query to database or uploaded CSV file), make their own data transformations and data enrichment in order to prepare the data as they require. Additionally, “kind of a mini ETL tool" called Data Flows gives users additional capabilities in data preparation. Something that was long desired in the old days, but never provided.

Augmented capabilities throughout

Oracle enhanced its already-strong augmented analytics capabilities by enhancing its data storytelling capabilities. This further advances its integrated graph analytics capabilities such as subgraphs, shortest path and page rank, as well as enhancing explainability of ML models generated. Oracle is committed to expanding the use of OAC to less technical users, demonstrated by its leading-edge vision for the future of business consumer analytics. OAC is also the only platform on the market to support NLQ in 28 languages (Gartner, 2023).

Augmented analytics is a combination of analytics and AI, in the form of embedding machine learning and natural language processing into “traditional” analytics. Augmented analytics has been part of Oracle Analytics Cloud (and Oracle Analytics Server, on-premise version of Oracle Analytics) strategy for several years. 

This all began, in earlier versions of Oracle Analytics, with cool features like explain, data enrichment or natural language queries, and has continued with machine learning (for business users) including AutoML, graph analytics and auto-insights for automatic datasets analysis and visualisation. 

Comprehensive data management

Oracle offers a powerful and cohesive view of data for its customers and every persona across the D&A continuum. By using ML techniques and technology based on the underlying OCI platform, the DBMS is able to tune, patch and upgrade itself to provide stronger security. Clients who invest in Oracle products across the D&A pipeline will see reduced efforts in data management and integration (Gartner, 2023).

Oracle Autonomous Database is designed to deliver the above benefits across 3 primary categories, all accomplished with minimal to zero administration:

  • Self-driving: The Autonomous Database automates database and infrastructure provisioning, management, monitoring, backup, recovery and tuning.
  • Self-securing: The Autonomous Database is more secure than a manually operated database because it automatically protects itself from internal and external vulnerabilities and attacks. The Oracle Cloud provides continuous threat detection, while the Autonomous Database automatically applies all security updates online and provides “always on”, end-to-end encryption. 
  • Self-repairing: The Autonomous Database provides preventative protection against all unplanned and planned downtime – and rapid, automatic recovery from outages without downtime. Autonomous Database availability and performance management is taken to the next level thru the use of AI-based autonomy that integrates multiple areas of diagnostics and enables analysis and action to be taken at runtime to minimize or eliminate operational disruption.

Besides using Oracle Autonomous Database for secure and scalable data storage, Oracle Analytics Cloud is tightly integrated with Oracle Autonomous Databases and directly uses several database services, such as:

  • Oracle Machine Learning (classification and regression machine learning algorithms, anomaly detection, market basket analysis, sentiment analysis, AutoML) and
  • Graph Analytics (clustering, node ranking, shortest path, subgraph).

But Autonomous Database is not the only OCI service that integrates with Oracle Analytics. There are other services, for example:

  • OCI AI Services: collection of services with pre-built or custom-built ML models, for image classification and object detection, text analysis, document understanding, real-time speech recognition, anomaly detection, forecasting and digital assistant (with more to come).
  • OCI Data Science:  fully-managed platform for teams of data scientists to build, train, deploy, and manage machine learning models using Python and open source tools using JupyterLab-based environment to experiment and develop models that can be registered and used with Oracle Analytics.

Cautions

Of course, Gartner lists also key cautions for those who are looking into Oracle Analytics and deciding to start using it. The list of three cautions consist of (Gartner, 2023): 

  • Momentum in a crowded market: In spite of all Oracle efforts, Oracle Analytics is still not seen as the “first-to-look-at” analytics tool, especially if there is no Oracle applications system in use. In these cases, Oracle is often overlooked.
  • Oracle application-centric: OAC practically hasn’t got any interfaces for non-Oracle applications. It seems, this is changing (for example Salesforce connector), however it still might take some time to get these on board.
  • Pricing: In terms of pricing, Oracle has very straight forward model that is based on (mostly) Universal Credits. Users might experience, that in comparison to old times, when high discounts were normal with Oracle, this has changed. There are basically none, but the model is therefore transparent, and in my view also competitive compared to other cloud vendors. Existing customers can also protect their investments in on-premise licences, as they are able to use these licences and use “Bring-Your-Own-Licence” subscription model which significantly lowers monthly costs.

Conclusion

I have been closely following the development of Oracle Analytics from 2018/2019 when the new product management team took things over. The work that has been done since then is huge and has basically turned the old OBIEE completely around. 

To me, it is very important that existing users have option to preserve their past investments with OBIEE and can gain everything what is new in Oracle Analytics. On the other hand side, in 2022, Oracle demonstrated leading-edge vision with innovative new features including composability, action frameworks, and a human realistic avatar presenting analytics news. Its roadmap shows significant investment in external connectivity to popular applications like ServiceNow, Mailchimp, SurveyMonkey and Yelp (Gartner, 2023). Using other words - opportunities are almost endless.

The development and advancements have been really impressive over the last couple of years and  Oracle will continue down this path. No doubt, in my mind.