Executive Summary

Augmented Analytics

For decades, organizations have been improving and expanding the way they use analytics and data software, commonly referred to as business intelligence (BI), to improve their operations. Vendors have made dramatic improvements to BI products with highly interactive visualizations and the ability to process and display very large volumes of data quickly. However, organizations quest for more. Artificial intelligence and machine learning (AI/ML) has gained in popularity with 87% of participants in our Analytics and Data Benchmark Research indicating they have adopted or plan to adopt AI/ML.

Adopting AI/ML has proven more complicated than many had expected. Ideally, BI software products could simply be extended to include a full set of AI/ML capabilities, but that has not yet happened. AI/ML requires skills that are beyond the reach of many analysts, and organizations have had difficulty finding skilled resources. As a result, we expect through 2025, AI and ML approaches will remain largely independent of BI approaches, requiring three-quarters of organizations to maintain multiple, separate skill sets. 

Faced with this separation, BI software vendors have invested in ways to make AI/ML more accessible by augmenting the capabilities in their products. These combined capabilities have been referred to as augmented analytics or augmented intelligence. This report uses the term augmented analytics.

AI/ML can augment analytics in a variety of ways. Natural language processing (NLP) utilizes AI/ML to parse and understand language to support conversational analytics with natural language queries and narrative responses. Automated machine learning (AutoML) automates the process of creating ML models, making more sophisticated analytics such as customer segmentation using clustering techniques accessible to more individuals. And, AI/ML can be applied to many tasks in analytics and data processes to make those tasks easier to perform.

Perhaps the most common area to apply AI/ML assistance is in data preparation. Data preparation continues to be the area where organizations spend the most time in their analytics processes. AI/ML can be used to suggest which tables of data to combine and how to combine those tables. It can be used to automatically construct a logical data model from a physical data model. AI/ML can augment data quality processes, identifying outliers and anomalies in the data, even recommending potential corrections for those data points.

While efforts to apply AI/ML have been underway for some time, the sudden explosion of generative AI capabilities has fueled more interest in how to augment BI. Generative AI is being used to generate SQL to access data sources, and to enhance NLP and conversational analytics. In some cases, generative AI is being used to produce documentation of data pipelines used in analytics processes, enhancing the understanding and lineage of the data. In some ways, it is the wild, wild west, with vendors racing to outdo each other in the ways in which they can apply generative AI. The technology holds much promise, and we expect it will have a significant impact on the analytics market, but it is early days still.

Generative AI is likely to make conversational analytics more common and more capable than it is today.

Augmented analytics will continue to evolve. Generative AI is likely to make conversational analytics more common and more capable than it is today. It may enable better support for multilingual capabilities currently lacking in most analytics products and will likely lead to increased automation in data preparation processes and in creating initial analyses, making analysts much more productive. More products will offer AutoML capabilities. Among the vendors we evaluated, AutoML is most often used to generate forecasts and to perform customer segmentation analyses. Over time, AutoML capabilities will expand to support more types of analyses and will produce models with improved accuracy. The exact intersection between AutoML in augmented analytics products and the models produced from more sophisticated AI/ML products remains to be seen. Today some of the augmented analytics products can work with these models, but it still a loosely coupled process.

Organizations should be aware of the changes going on in the market, and should understand what capabilities their vendor currently offers. They should also be comparing their current offering with what other vendors have to offer. In evaluating augmented analytics, one must consider the underlying analytics capabilities. Great augmentation can only do so much if the foundation of underlying analytics is weak. Consequently, this Buyers Guide combines an assessment of augmented analytics capabilities with core analytics capabilities to determine the vendor’s overall rankings. Organizations can then use this report not only to help guide purchasing decisions, but to guide conversations with vendors about their roadmap for augmented analytics. The market is still evolving rapidly, but organizations can realize value today that will improve their analytics processes.

This research evaluates the following analytics and data vendors that offer products that include key elements of augmented analytics as we define it: AWS, Cloud Software Group, Domo, GoodData, Google, IBM, Idera, Incorta, Infor, insightsoftware, Microsoft, MicroStrategy, Oracle, Pyramid Analytics, Qlik, SAP, SAS, Sigma Computing, Sisense, Tableau Software, ThoughtSpot and Zoho.

Buyers Guide Overview

For over two decades, Ventana Research has conducted market research in a spectrum of areas across business applications, tools and technologies. Ventana Research has designed the Buyers Guide to provide a balanced perspective of vendors and products that is rooted in an understanding of the business requirement in any organization. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select technology vendors and products. The findings of this research undertaking contribute to our comprehensive approach to ranking and rating vendors in a manner that is based on the assessments completed by an organization.

Ventana Research has designed the Buyers Guide to provide a balanced perspective of vendors and products that is rooted in an understanding of business requirements in any organization.

This Ventana Research Buyers Guide: Augmented analytics is the distillation of over a year of market and product research efforts. It is an assessment of how well vendors’ offerings will address organizations requirements for augmented analytics software. The index is structured to support a request for information (RFI) that could be used in the RFP process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with technology vendors. An effective product and customer experience with a technology vendor can ensure the best long-term relationship and value achieved from a resource and financial investment.

In this Buyers Guide, Ventana Research evaluates the software in seven key categories that are weighted to reflect buyers’ needs based on our expertise and research. Five are product-experience related: Adaptability, Capability, Manageability, Reliability, and Usability. In addition, we consider two customer-experience categories: Validation, and Total Cost of Ownership and Return on Investment (TCO/ROI). To assess functionality, one of the components of capability, we applied the Ventana Research Value Index methodology and blueprint, which links the personas and processes for augmented analytics to an organization’s requirements.

The structure of the research reflects our understanding that the effective evaluation of vendors and products involves far more than just examining product features, potential revenue or customers generated from a vendor’s marketing and sales efforts. We believe it is important to take a comprehensive research-based approach, since making the wrong choice of a augmented analytics technology can raise the total cost of ownership, lower the return on investment and hamper an organization’s ability to reach its potential performance. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on a short list of vendors that does not represent a best fit for your organization.

To ensure the accuracy of the information we collected, we asked participating vendors to provide product and company information across the seven product and customer experience categories that, taken together, reflect the concerns of a well-crafted RFI. Ventana Research then validated the information, first independently through our database of product information and extensive web-based research, and then in consultation with the vendors. Most selected vendors also participated in a one-on-one session providing an overview and demonstration, after which we requested they provide additional documentation to support any new input.

Ventana Research believes that an objective review of vendors and products is a critical business strategy for the adoption and implementation of software.

Ventana Research believes that an objective review of vendors and products is a critical business strategy for the adoption and implementation of augmented analytics software and applications. An organization’s review should include a thorough analysis of both what is possible and what is relevant. We urge organizations to do a thorough job of evaluating augmented analytics systems and tools and offer this Buyers Guide as both the results of our in-depth analysis of these vendors and as an evaluation methodology.

How To Use This Buyers Guide

Evaluating Vendors: The Process

We recommend using the Buyers Guide to assess and evaluate new or existing technology vendors for your organization. The market research can be used as an evaluation framework to establish a formal request for information from technology vendors on their products and customer experience and will shorten the cycle time when creating a RFI. The steps listed below provide a process that can facilitate best possible outcomes.

1. Define the business case and goals.
   Define the mission and business case for investment and the expected outcomes from your organizational and technology efforts. 
2. Specify the business needs.
   Defining the business requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
3. Assess the required roles and responsibilities.
   Identify the individuals required for success at every level of the organization from executives to front line workers and determine the needs of each. 
4. Outline the project’s critical path.
   What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan. 
5. Ascertain the technology approach.
   Determine the business and technology approach that most closely aligns to your organization’s requirements. 
6. Establish technology vendor evaluation criteria.
   Utilize the product experience: Adaptability, Capability, Manageability, Reliability and Usability, and the customer experience in TCO/ROI and Validation. 
7. Evaluate and select the technology properly.
   Weight the categories in the technology evaluation criteria to reflect your organization’s priorities to determine the short list of vendors and products.
8. Establish the business initiative team to start the project.
   Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources. 

The Findings

The products we evaluated vary in capabilities, but not all the capabilities offered by a technology vendor are equally valuable to all types of workers or support everything needed to manage products on a continuous basis. Moreover, the existence of too many capabilities may be a negative factor for an organization if it introduces unnecessary complexity. Nonetheless, you may decide that a larger number of features in the product is a plus, especially if some of them match your organization’s established practices or support an initiative that is driving the purchase of new software.

Factors beyond features and functions or vendor assessments may become a deciding factor. For example, an organization may face budget constraints such that the TCO evaluation can tip the balance to one vendor or another. This is where the Value Index methodology and the appropriate category weighting can be applied to determine the best fit of vendors and products to your specific needs.

Scoring of Vendors Across Categories

The research finds Oracle first on the list with SAP in second place and IBM in third. Companies that place in the top three of a category earn the designation of Leader. Oracle has done so in six of the seven categories; SAP in five; IBM in four; Domo and Qlik in two and Sisense and Tableau in one category.

The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the vendors. Those vendors whose Product Experience have a higher weighted performance to the axis in aggregate of the five product categories place farther to the right, while the performance and weighting for the two Customer Experience categories determines their placement on the vertical axis. In short, vendors that place closer to the upper-right on this chart performed better than those closer to the lower-left.

The research places vendors into one of four overall categories: Assurance, Exemplary, Merit or Innovative. This representation classifies vendors overall weighted performance.

Exemplary: The categorization and placement of vendors in Exemplary (upper right) represent those that performed the best in meeting the overall Product and Customer Experience requirements. The vendors awarded Exemplary are: Domo, IBM, Oracle, Qlik , SAP, Tableau and Zoho.

Innovative: The categorization and placement of vendors in Innovative (lower right) represent those that performed the best in meeting the overall Product Experience requirements but did not achieve the highest levels of requirements in Customer Experience. The vendors awarded Innovative are: Microsoft, MicroStrategy, Pyramid Analytics and SAS.

Assurance: The categorization and placement of vendors in Assurance (upper left) represent those that achieved the highest levels in the overall Customer Experience requirements but did not achieve the highest levels of Product Experience. The vendors awarded Assurance are: GoodData, Incorta, insightsoftware and Sisense.

Merit: The categorization for vendors in Merit (lower left) represent those that did not exceed the median of performance in Customer or Product Experience or surpass the threshold for the other three categories. The vendors awarded Merit are: AWS, Cloud Software Group, Google, Idera, Infor, Sigma Computing and ThoughtSpot.

We warn that close vendor placement proximity should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every organization or for a specific process. Although there is a high degree of commonality in how organizations handle augmented analytics, there are many idiosyncrasies and differences in how they do these functions that can make one vendor’s offering a better fit than another’s for a particular organization’s needs.

We advise organizations to assess and evaluate vendors based on their requirements and use this research as a reference to their own evaluation of a vendor and products.

Product Experience

The process of researching products to address an organization’s needs should be comprehensive. Our Value Index methodology examines Product Experience and how it aligns with an organization’s life cycle of onboarding, configuration, operations, usage and maintenance. Too often, vendors are not evaluated for the entirety of the products; instead, they are evaluated on market execution and vision of the future, which are flawed since they do not represent an organization’s requirements but how the vendor operates. As more vendors orient to a complete product experience, the more robust of an evaluation can be conducted.

The research based on the methodology of expertise identified the weighting of Product Experience to 90% or nine-tenths of the total evaluation. Importance was placed on the categories as follows: Capability (50%), Usability (10%), Reliability (10%), Manageability (10%) and Adaptability (10%). This weighting impacted the resulting overall rankings in this research. Oracle, SAP and IBM were designated Product Experience Leaders as a result of their commitment to augmented analytics technology. Vendor rankings for Qlik, Microsoft and Domo also showed these vendors met a broad range of augmented analytics requirements. SAS, Pyramid Analytics and Idera performed well in Capability.

While augmented analytics were given more weight in our assessment, core analytics capabilities were also considered a requirement. Therefore, vendors that performed well in other categories, such as Tableau with its top ranking in Manageability and strong performances in Reliability and Usability, were boosted in their Product Experience rankings.

Customer Experience

The importance of a customer relationship with a vendor is essential to the actual success of the products and technology. The advancement of the Customer Experience and the entire life cycle an organization has with its vendor is critical for ensuring satisfaction in working with that vendor. Technology providers that have Chief Customer Officers area more likely to have greater investments in the customer relationship and focus more on their success. These leaders also need to take responsibility for ensuring the marketing of their commitment is made abundantly clear on website and in the buying process and customer journey.

Our Value Index methodology weights Customer Experience at 10%, or one-tenth, as it relates to the framework of commitment and value to the vendor-customer relationship. The two evaluation categories are Validation (5%) and TCO/ROI (5%), which are weighted to represent their importance to the overall research.

The vendors that rank the highest overall in the aggregated and weighted Customer Experience categories are Oracle, SAP and Qlik. These category leaders in Customer Experience best communicate their commitment to customer needs. Vendors such as Tableau, Domo and IBM were not Overall Leaders, but have a high level of commitment to customer experience.

Many vendors we evaluated did not have sufficient information available through their website and presentations. While many have customer case studies to promote their success, they lack depth on their commitment to an organizations’ journey. This makes it difficult for organizations to evaluate vendors on the merits of their commitment to customer success. As a result, nearly half of the vendors’ performances were scored at less than 50% of the maximum in our evaluation. As the commitment to a vendor is a continuous investment, the importance of supporting customer experience in a holistic evaluation should be included and not underestimated.

Appendix: Vendor Inclusion

For inclusion in the Ventana Research Augmented Analytics Buyers Guide for 2023, a vendor must be in good standing financially and ethically, have more than 50 dedicated workers, at least $50 million in annual or projected revenue verified using independent sources, sell products and provide support on at least two continents, and have at least 100 customers. The principal source of the relevant business unit’s revenue must be software-related and there must have been at least one major software release in the last 18 months. The product must be capable of accessing data from a variety of sources, modeling the data for analysis, analyzing the data using a variety of techniques, communicating the results in a variety of ways and supporting the data and analytics processes within an organization. 

The research is designed to be independent of the specifics of vendor packaging and pricing. To represent the real-world environment in which businesses operate, we include vendors that offer suites or packages of products that may include relevant individual modules or applications. If a vendor is actively marketing, selling and developing a product for the general market and is reflected on its website that it is within the scope of the research, that vendor is automatically evaluated for inclusion. 

All vendors that offer relevant augmented analytics products and meet the inclusion requirements were invited to participate in the research evaluation process at no cost to them.

Eleven of the 22 vendors responded positively to our requests for additional information and provided completed questionnaires and demonstrations to help in our evaluation of their subscription management products. We categorize participation as follows:

Complete participation: The following vendors actively participated and provided completed questionnaires and demonstrations to help in our evaluation of their product: Domo, GoodData, IBM, insightsoftware, Oracle, Pyramid Analytics, Qlik, SAP, Sisense, Tableau and Zoho.

Partial participation: The following vendors provided limited information to help in our evaluation: Incorta, Infor and MicroStrategy.

No participation: The following vendors provided no information or did not respond to our request: AWS, Cloud Software Group, Google, Idera, Microsoft, SAS, Sigma Computing and ThoughtSpot.

Vendors that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have significant impact on their classification and rating, we recommend additional scrutiny when evaluating those vendors.

Products Evaluated

Vendors of Note

We did not include vendors that, as a result of our research and analysis, did not satisfy the criteria for inclusion in the Buyers Guide. These are listed below as “Vendors of Note.”