Oracle Advanced Analytics provides powerful data customization and integration capabilities, making it suitable for businesses looking to enhance their analytics within Oracle ecosystems and beyond.
| Product | Mindshare (%) |
|---|---|
| Oracle Advanced Analytics | 4.8% |
| IBM SPSS Statistics | 16.2% |
| IBM SPSS Modeler | 16.0% |
| Other | 63.0% |
| Type | Title | Date | |
|---|---|---|---|
| Category | Data Mining | Jun 23, 2026 | Download |
| Product | Reviews, tips, and advice from real users | Jun 23, 2026 | Download |
| Comparison | Oracle Advanced Analytics vs KNIME Business Hub | Jun 23, 2026 | Download |
| Comparison | Oracle Advanced Analytics vs IBM SPSS Statistics | Jun 23, 2026 | Download |
| Comparison | Oracle Advanced Analytics vs IBM SPSS Modeler | Jun 23, 2026 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| KNIME Business Hub | 4.1 | 10.7% | 94% | 63 interviewsAdd to research |
| IBM SPSS Statistics | 4.1 | 16.2% | 89% | 41 interviewsAdd to research |
Oracle Advanced Analytics offers features like centralized reporting, predictive modeling, and integration with more than ten algorithms for data mining. Despite its strengths, challenges include complexity and licensing issues that affect ease of use and data processing. Users often deploy it to streamline data analysis, support cloud cost assessment, and integrate with SD-WAN environments for security-enhanced transitions. Its compatibility with OBI, ODI, and OBIA versions facilitates its adaptability in handling extensive data lakes.
What are the key features of Oracle Advanced Analytics?Consulting firms employ Oracle Advanced Analytics for integrating secure transitions in SD-WAN environments, focusing on management and security aspects. In marketing, teams leverage it for projects that require analyzing multiple data sources to understand consumer behavior. It assists businesses in managing extensive data lakes, facilitating historical data analysis. Organizations benefit from its compatibility with Oracle tools like OBI, ODI, and OBIA, driving efficient operations in diverse industry contexts.
Oracle Advanced Analytics was previously known as OAA.
Orbitz, Marriott, SGS Life Science, Masdar, AlliantEnergy Corporation, British Standards Institute, Skybox Security, Triple PointTechnology, and Coca Cola.
| Author info | Rating | Review Summary |
|---|---|---|
| External Consultant at a consultancy with 51-200 employees | 3.5 | I've used Oracle Advanced Analytics for over a decade to centralize reporting and dashboards, but while its integration and customization are strong, the interface is complex and setup requires significant configuration due to organizational and security needs. |
| Enterprise Solutions Architect at CONTPAQi | 4.5 | We use Oracle Advanced Analytics to manage and analyze data lakes accumulated over 40 years. Its efficient data organization and predictive insights are valuable, but the user experience could improve, especially during navigation. We switched from AWS due to cost. |
| OBIEE/ODI senior consultant at Kastech | 3.5 | I use Oracle BI, ODI, and OBIA, but find performance, scalability, and data distribution are significant issues needing improvement. I appreciate working with Oracle support on workarounds, though each new project starts from scratch. I rate it 7/10. |
| Director : Oracle GRC at Technology Risk Partners | 5.0 | I find this solution very good; its intuitive dashboard yields excellent results and performance, and it's stable. My main issues are sometimes not finding transactions and scalability beyond 65,000 users due to memory errors. |
| Customer Interaction Consultant at Dulaney Consulting Associates LLC | 4.5 | As a consultant, I value this solution's ease of use for non-technical people to pull diverse information. It’s stable and scalable with good support. I’d like better integration for customized data sources, but overall, I rate it 9/10. |
| CEO with 1,001-5,000 employees | 4.0 | No summary available |
| Consultant with 51-200 employees | 4.0 | No summary available |
| IT Administrator at a tech services company with 501-1,000 employees | 5.0 | I find Oracle Data Mining powerful for insights and predictive models, integrating them easily into SQL for predictions. However, it struggles with huge datasets and statistical processing, making performance benchmarks difficult. |
| -- at a university | 3.0 | I found it easy to use, simple, and had a quick learning curve. However, I believe its capabilities are basic and less professional than expected from a company dealing with databases. |
| Database Expert at a tech company with 51-200 employees | 3.0 | I used Oracle Data Mining for fraud detection and login trend analysis, appreciating its analytical functions and dashboard. However, data loading and cleansing were time-consuming, and licensing was tricky. |
I currently use the solution just for gathering information, and I'm working on a side project detailing cloud costs. I downloaded the information to get some information about the situation.
We typically use Oracle Advanced Analytics to deploy dashboards and deliver dashboards and reports to the end-users.
What I appreciate about Oracle Advanced Analytics is the ability to customize it.
The integration of Oracle Advanced Analytics with existing Oracle tools or with third-party tools is quite good.
Oracle Advanced Analytics helps us to steer the company, to create centralized reports and dashboards so that we're all using the same information, and we're not relying that much on Excel and different custom reporting solutions. We are now able to centralize it much more.
The organizational context makes it difficult to deploy Oracle Advanced Analytics because we have all kinds of contract maintenance parties; however, the software installation itself is something we're used to doing, so that's no problem for us.
It is not a really easy product to use, so you have to do a lot of configuration.
The user interface is somewhat complex, and while they're working on that, for the end-user it is not really intuitive, so that could be improved.
Currently, we are not using the predictive analytics capabilities, so I cannot say anything about it.
I have been using Oracle Advanced Analytics in my organization for about 11 years now.
There are not really any issues with integrating the product.
The technical support of Oracle depends on who you speak to, but I would rate it a seven.
Positive
The time needed to deploy Oracle Advanced Analytics depends on what you define by the installation. The software installation itself is done in a few hours, but setting up all the SSL certificates, security measures we need to take, and testing the whole environment takes a long time.
We are doing the setup and configuration of Oracle Advanced Analytics with about eight people, where two people are working primarily on it, and then some people around it for the configuration, security measures, and that kind of work.
For us as a government organization responsible for the army, it is really important that we can deploy Oracle Advanced Analytics on-premise due to the security measures we have to take, so we cannot go to the cloud at this moment.
For a governmental organization, the cost is not that important to us, but Oracle Advanced Analytics helps us to make the organization more efficient. We do not have any ROI or RCL calculation or return on investment.
I am currently satisfied with the pricing for Oracle Advanced Analytics. It's a little bit high maybe, but I think it's acceptable.
I know Oracle Data Integrator and Oracle Advanced Analytics.
We leverage the support for SQL certainly, and we do not use the R and Python support at this moment.
My current job title is an external consultant.
On a scale of one to ten, I rate Oracle Advanced Analytics a seven.
Our primary use case for the product is managing and analyzing our business's data lakes, which have accumulated over nearly 40 years.
The platform's most valuable features are the efficient organization of data and the ability to extract specific insights for predictive analysis.
One area for product improvement is the user experience, particularly when navigating through screens, especially on Windows.
I have been using Oracle Advanced Analytics for about three to four years.
I rate the product stability as a nine, as our usage has been very stable.
The product's scalability is a five. Approximately 50 executives in our company use it, primarily for data analysis, including data scientists and analysts.
While I have not used technical support, my team has, and they have found it to be very good.
Positive
We switched from AWS to Oracle Advanced Analytics because, while AWS was easy to use, it was more expensive.
On a scale of one to ten, I rate my experience with the initial setup process as a seven, as it was somewhat complex to start using. The deployment in our organization was very fast, taking approximately ten to fifteen minutes.
Oracle Advanced Analytics effectively prevents threats and helps maintain a secure network environment. Experienced personnel are crucial for handling the solution effectively after installation.
Overall, I rate it as an eight, considering its strong functionality but room for improvement in usability.
We use the 12c version of OBI and ODI and 11.1.1 of OBIA.
The performance, scalability and queries should be addressed, as well as the data distribution of certain data techniques.
I'm IT and have a total of 13 years of experience working with these tools, including ten with those of data warehousing, such as Oracle BI Analytics, ODI: Oracle Data Integrator, Oracle BNS Analytics and OBIA: Oracle Business Intelligence Analytics. I have also worked on Power BIH and in Tableau, for visualization purposes.
The scalability should be addressed.
When it comes to scalability, we require an area of application, meaning we need to maintain things in different application servers and to do so for certain agents, like network distribution. This means that when the issue comes in on the server, it will immediately be picked up from other servers and some clustering techniques will be maintained.
When an issue arises, we first analyze it on our end. If we see that it is not feasible for us to handle it on our own, then we will avail ourselves of Oracle support by creating requests and will coordinate the issue with them. When needed, we will work closely with Oracle support and implement their workaround in our application.
When it comes to data mining, I am acquainted with most or all of the Oracle tools and have had a positive experience with them. I include in this list SQL database, SQL PL SQL, ODI, Oracle Data Integrator and Oracle Business Intelligence.
We must start from scratch for each new project, which entails assessing the data, data scrubbing, data modeling and implementing data mining techniques. There is also involved association, clustering techniques and regression.
There is a need to analyze the data and put it into the documentation.
I rate Oracle Advanced Analytics as a seven out of ten.
The dashboard interface is intuitive and the user is able to interact with it to receive good results from the analytic. We have been getting high performance out of the solution and the tools are easy to use.
There are some transactions we have not been able to find through the dashboard.
I have been using the solution for years.
The solution is stable, we have not had any downtime.
The solution is scalable for up to 65,000 users then after that, I was getting an error related to memory or buffer size being too low.
Oracle is number one in the world for security, safety, and privacy. This solution is very good.
I rate Oracle Advanced Analytics a ten out of ten.
We're a consulting company. For now, everything's in transition, more and more people are moving toward an SD-WAN type of environment. It all requires more management, more security, and most of the things can be reduced down to one or just a few panes of glass to manage. We're trying to understand how to best guide our customers to make sure they carry out best practices in the most economical and efficient way. I've just helped somebody set up an AWS Connect small call center with a chatbot-type environment. They're a group of about half a dozen marketing people that were focused on consumer products. They were pulling together diverse sources of information for marketing purposes. We are customers of Oracle products.
One of the valuable features is the ability to pull together multiple sources of information and the tools are easy to use which is the main factor. They're easy to use for non-technical, non-programming people. Whether you're a venture capitalist or a giant marketing firm or whether you're a political action committee, it doesn't matter. You don't have to be a technical expert to make use of the tools once you're set up. You just have to know your sources of information and start pulling things together.
I would like to see some refinement in the area of getting things that are not standard cloud applications, but are rather more customized. That's where some of the non-technical people trigger up and ask: "How can we have a local database or a private database, or huge Excel spreadsheets that need to be transformed?" A lot of the database ETL stuff that's behind the scenes is just about getting that stuff out. I think Oracle Analytics is working toward refining a lot of that, so that it requires less technical expertise to incorporate that kind of data source. It's really about initially setting it up and adding to it. As you start new campaigns, you can run across new sources of information. Not everything is in salesforce.com.
I would like to see an improvement in the ease of implementing customized data sources and the like. I would suspect Oracle has other things in the works along those lines. They're constantly trying to refine and improve their products.
The solution seems to be stable although I don't use it on a regular basis myself. The client didn't complain of any major disruptions or outages.
It appears to be very scalable simply because if you're using it on an Oracle cloud environment, it's as scalable as the Oracle cloud will allow it to be.
Technical support seem to be very good.
There are some complexities behind the scenes when you're doing more customized stuff. But if you're pulling from public data sources that are cloud source data then it's pretty straightforward.
I know the company has both enterprise-level pricing and pay-as-you-go type pricing, like any of the cloud-based network solutions. You have to manage your pricing based on the number of users and how much you plan to use the solution. The more you use, the more it costs. So you have to plan ahead for that. And that's an area that's becoming its own industry, helping people figure out how to manage the budget for those things. It can get very complex very quickly.
I would rate this solution a nine out of 10.
Oracle Advanced Analytics is a new Oracle database option (announced today) that bundles Oracle R Enterprise and Oracle Data Mining (reviewed previously). With this release, R becomes a first class native interface for the Oracle database along with SQL and the graphic interface that ships with Oracle Data Mining. This allows analytic modeling code to be written in 100% R with the tables and views in the Oracle database appearing as R objects directly. There is no need for modelers to write SQL – they can just write R code and manipulate the data in the database. This makes it easier for R programmers to access the database (no extracting the data to a file, no writing SQL) and the availability of data extends to Oracle OLAP Cubes that can also be accessed from the R code.
Performance is good with the approach for a number of reasons. First, with this set up, the database computing hardware is used and all the R packages are being executed on the database server. The approach further improves performance by allowing the data to be accessed without extracting or moving it. Finally all the ODM algorithms are available to Oracle R Enterprise so that R packages can use the ODM algorithms already deeply embedded and optimized for the Oracle database as well as the Oracle Exadata and Oracle Exalytics machines.
Besides improving data access and performance for R, Oracle R Enterprise also allows a piece of R code that builds a model, makes a forecast or scores a customer to be treated as a database function. Once deployed to an Oracle database function this R code can then be called by any piece of SQL (in a BI tool like OBIEE or Java code or business rules). Any arbitrary R code can be executed in this way with no constraint on inputs or outputs or the code/packages being used. This supports the increasing focus of modelers on real-time scoring by making it easy to embed R code as SQL-friendly functions that can be called to calculate a score or make a prediction right when the decision is being made.
This release builds on previous work around making Big Data available to R. The Oracle R connector for Hadoop is available in conjunction with the support for R in the Oracle Big Data appliance. These allow you to run R against both Oracle’s own Big Data appliance and against a generic Hadoop/HDFS installation with no need to convert it to MapReduce for execution. This means that a user can develop a single R script that brings in data from an Oracle database, Oracle Big Data Appliance and HDFS and have it all look like R objects to the script. These scripts can then be deployed to the Oracle Database or Oracle Big Data infrastructure for real-time scoring against this diverse set of data sources.
This will clearly be a product considered in the forthcoming Decision Management Systems Platform Report.
I’m part of a small group of mathematics enthusiasts in Kansas City who meet about once a month on Saturday mornings to drink coffee and discuss mathematics. This past weekend it was my turn to do a presentation to the rest of the group and I chose to speak on the mathematical foundations of the Support Vector Machine algorithm in Oracle Data Mining. While I wasn’t surprised that some in the group had a better handle on Vapnik-Chervonenkis theory than I and gently “guided” me a few times, I was somewhat surprised at their positive reaction to my characterization of the “Oracle” approach to data mining in contrast with the “SAS” approach. While gross simplifications are always “gross”, here is my take on what I believe to be very different philosophies. Let’s use classification as an example since we’re talking about SVMs.
I think of the “SAS” approach to be similar to that of a “statistician” or classic data scientist. That is, there is a desire to understand the algorithm in context of the data set. The main objective is to identify and understand the source(s) of error in the model and to characterize the algorithm through the use various coefficients and ratios. A good deal of effort is spent in the evaluation process of the algorithm and in understanding the impact of different choices in methodology. The SAS perspective emphasizes understanding the data preparation and the algorithm. The more detail, the better.
The “Oracle” approach to data mining is characterized by a broader set of business concerns from data security and processing time to the business value of results. An “ODM” approach would be to invest time to develop and test several different models and see which ones have more predictive power and to spend relatively less time in evaluation of the individual models. Another way to say this is that error is error and rather than understanding sources of error, we should take that same time and try to understand business implications of the positive “working” part of the model. The more business value, the better.
The fundamental concept behind Support Vector Machines is to take a highly dimensional data set and to separate two classes of data by a hyperplane that maximizes the marginal distance between the data points (i.e. perfectly in the middle). Let’s contrast this with a “neural net” algorithm which is another classification approach. Neural nets use iteration and a wide variety of statistical techniques to “tune” their algorithm to minimize the predictive error across a particular training data set. Support Vector Machines tend to be more robust and work well across a broad range of new data sets. Neural Nets are more precise, but also are prone to “over fitting” their training data set and typically are less robust. I think of the “Oracle” data mining approach to be like the Support Vector Machine. It uses a very strong mathematical foundation that is highly generalizable and works well across a broad range of data sets. The “SAS” data mining approach uses highly complex mathematical techniques specific to a given situation. The “Oracle” approach minimizes the “structural risk” of classification or it uses an approach that is least likely to produce error. The “SAS” approach minimizes the “empirical risk” of classification or it uses an approach minimizes the total error.
I have nothing but respect for SAS practitioners. They are true experts and tend to personify the “if it’s worth doing, it’s worth doing right” approach to analytics. Of course the contrasting position is, “the perfect is the enemy of the good.” Time spent perfecting a model is forever lost and often more value is delivered by moving more quickly and accomplishing more. The Support Vector Machine algorithm deployment in Oracle Data Mining is good, very good. It recognizes that there is an inherent tradeoff between algorithmic complexity and the ability to generalize across new data sets. It uses a sensible automatic data preparation process that makes good choices and then leverages a replicable, explainable, foundationally solid methodology for balancing tradeoffs. In short, even proof-obsessed, dyed-in-the-wool mathematicians can recognize the inherent value of Oracle’s Support Vector Machine strategy.
Disclosure: My company is a Oracle Gold Partner
• Oracle Data Mining provides a powerful functionality as it enables users to discover new insights hidden in data.
• It enables building of predictive models.
• Its model can be included in SQL queries and entrenched in applications to offer improved business intelligence.
• It has more than ten data mining algorithms that can be used in data mining.
• Problems in handling huge data sets
• Cannot be used to process statistical data
• Performance benchmarks are difficult in data mining
I use the models of this product to derive predictions and descriptions of behavior from the database system. It eases work of moving data from relational tables into the analytical workbenches. It simplifies model implementation by submitting Oracle SQL functions to score data stored within the database.
Easy to use, simple. Quick learning curve.
Basic capabilities, less professional than would be expected from a company dealing with databases as a core business.
Oracle Data Mining (ODM) provides data mining functionality as a part of Oracle Advanced Analytics Option. I have used this tool to model database login activity which was used to establish database activity trends and help in fraud detection and prevention. Once the data was loaded, used Oracle supplied analytical functions like Regression and “attribute Importance” to create a predictive model. Viewing the data on the Dashboard with charts is a great option this tool provides. Data Mining models can be included in SQL queries and embedded in applications to offer improved business intelligence.
Since a separate Oracle database is needed, the data loading and cleansing the raw data can be a time consuming process.Licensing can be tricky with all the options in the Oracle OLAP suite.It is better to get the whole OLAP suite and use the features one needs rather than buying piece meal.
One should try to automate the whole data loading and cleansing process using scripts to get a quick turnaround. Using shell and sql scripts one can automate this process. This product was chosen to establish database login patterns as a custom solution for a DAM (Data Access Monitoring) tool.
