Moving to the cloud, embedded BI, and embedding augmented intelligence and machine learning are three of the top analytics trends.
Doug Henshen
That’s according to a new report by Constellation Research analyst Doug Henschen called “2023 Analysis and BI Market Overview” report.
For decades, analytics have been the core domain of teams of data professionals, who dominated an organization’s data, did all the analysis themselves, and delivered reports on demand. Then came the era of self-service analytics, with vendors like Tableau and Qlik enabling developers to build amazing dashboards that allowed their users to interpret the data.
However, most of the data is still kept on-premises, and analytics can afford to set up users with licenses for the BI tools of their choice and provide data literacy training to those users so they can work with the data. confined to the organization. .
Over the past decade, and especially in the last few years, things have changed. Vendors have pushed their customers to become more data-driven by making their analytics tools more accessible to more users.
For more than two decades, adoption of BI within organizations has been relatively stagnant, hovering around 25%. But now the vendor offers his native version of the cloud platform that does not limit the number of licensed users. In addition, we are allowing the customer to embed her BI into other applications so that the user does not have to search for relevant data. It also incorporates AI and machine learning (ML), reducing the level of data literacy previously required to work with data.
Recently, Henschen discussed the findings of his report, which focused on how well vendors are responding to key analytics trends, as well as other BI trends underway. He also talked about over- and under-appreciation trends, and which trends will matter most in a few years.
Your report identifies three ways BI/analytics platforms are evolving. First, let’s look at them holistically. Can moving to the cloud, embedded analytics, and AI and ML technologies combine to enable organizations to do what is so important in today’s business environment?
When it comes to the impact these three trends have on customers, one common theme is empowering more users with data-driven insights.
Doug HenshenAnalyst, Constellation Research
Doug Henschen: These three trends are interrelated and in many ways support each other. For example, cloud computing supports embedded approaches, especially service-based architectures and fine-grained APIs. The cloud has also enabled AI, ML, and extensions with scalable compute and storage.
When it comes to the impact these three trends have on customers, one common theme is empowering more users with data-driven insights. A cloud approach makes it faster and easier to achieve larger, broader deployments. An embedded approach enables us to provide concise insight into where people work within applications, productivity and collaboration tools, workflows and business processes. Enhanced features such as natural language query and explain make analytics and her BI easily accessible to all users, including novice business users.
Looking at each of the top analytics trends individually, what is different from a few years ago to better enable the move to the cloud?
Hengsheng: Just a few years ago, many vendors offered cloud marketplace services to support cloud deployments, making it easier to deploy software, but it was up to the customer to manage the deployment. It was up to you. The Software as a Service option doubled for him three years ago. All 17 vendors mentioned in the report now offer SaaS in at least one public cloud.
Many vendors also use so-called cloud-native services to develop containerized images that facilitate deployment to public clouds. This helps, but assumes that customers are willing to manage the software themselves in the absence of a service-based offering. The new “Multicloud Analytics and Business Intelligence Platforms” shortlist includes eight vendors offering their platforms as SaaS or managed services on two or more public clouds.
A similar question with embedded analytics – how are vendors enabling customers to embed BI and analytics? And how has it evolved over the years since we first heard about embedded BI? have you been
Hengsheng: Independent software vendors and SaaS companies have long used embedded analytics options to accelerate development and data-driven software and services. Today, innovative organizations of all kinds (banks, insurance companies, etc.) are developing software and services that use built-in capabilities to add analytics to custom apps, enterprise apps, productivity and collaboration apps, workflows and business. is woven. process.
9 leading vendors on the ‘Embedded Analytics’ shortlist with support for microservices architecture, fine-grained application programming interfaces, RESTful interfaces, and flexible embedding of data, metrics, visualizations, and dashboards to various external destinations It supports an embedded approach that leverages software development kits that It also pursues an automated DevOps-style deployment approach, low-code/no-code development options, event architectures, and in some cases workflow and process integration to automate actions and implement analytics based on analytical insights and thresholds. Trigger a human review.
And how are vendors incorporating AI and ML into their platforms to better serve their customers? How are they different?
Hengsheng: Our 2019 analysis and overview of the BI market focuses on the enhancements that were emerging at the time. Data preparation options included suggested joins. Discovery and analysis capabilities included outlier detection and influencer/root cause analysis. Natural language dialogue included NL queries and NL descriptions.
The breakthrough here in 2023 is the use of generative AI, and vendors like Microsoft, Salesforce, and ThoughtSpot are currently previewing generative AI capabilities. Generative AI has amazing potential to enhance natural language interaction, sentiment analysis, data visualization, and query code generation. See the report for details. However, it’s important to note that all generation capabilities found in the analytics and BI space are currently in private or limited preview.
Beyond these key analytics trends, what other ways are vendors currently trying to help their customers perform analytics better and more efficiently and ultimately make better decisions?
Henschen: Many vendors have introduced or improved data cataloging, data modeling, metadata management, and governance capabilities. Some companies are advancing predictive analytics and data science with new enhanced and automated ML capabilities. We also see incremental improvements and refinements to more mature features such as data preparation, dashboards, storytelling, and reporting.
Digging deeper into generative AI, what do you think will be its role in analytics in 2023? And how will the relationship between analytics and generative AI evolve beyond 2023?
Hengsheng: The potential generative AI applications are numerous and potentially powerful. Generative AI promises to enhance natural language interactions. This allows you to truly bring data-driven insights to both your internal and external partners and customers.
But a word of caution here. All vendors previewing generative AI capabilities are focused on incorporating human review procedures. Data privacy concerns need to be addressed, and continuous monitoring and human review are required to ensure the accuracy of AI-generated content. Analytics and BI are just one potential use case for generative AI. There is a lively debate within the tech industry and the public about the appropriate use of generative capabilities and the need for regulation and safeguards.
If cloud deployments, embedded analytics, and AI/ML enhancements are the three most important analytics trends as of May 2023, what do you think will be the most important in a year or two?
Hengsheng: It’s easy to speculate that breakthroughs in generative AI will continue to ripple through all technology markets and societies in the years to come. Despite talk of six-month moratoriums, region-specific bans, and potentially harsher regulations, I think it’s hard to keep such a powerful technology. , and we are optimistic that it will result in net job creation. In the realm of analytics and BI, if everything shifts to conversational analytics, forecasting, what-if scenario planning and optimization, traditional reports and dashboards will become stone knives and bearskin rugs in five years. It may look like
Staying on the analytical trend for one more question. What are the most underestimated and most overestimated trends?
Hengsheng: The most underappreciated trend is the emergence of data catalogs, collaboration capabilities, and ML-driven data and content recommendations, all of which allow users to leverage existing trusted data sources, measures, reports, and dashes. Helped me find my board. I think Extended Trends didn’t work as expected because many of these features were too complex to be widely adopted.
NL Queries and NL Explains are the exception and are seeing widespread adoption and use because they are user friendly.
Looking back at historical trends, which platform do you think is the overall best at meeting your customers’ modern needs?
Hengsheng: Every customer has different needs, so we must absolutely oppose one-size-fits-all analysis. Can one of those options be on the shortlist if it’s not available in the cloud you primarily or exclusively use? We think not. If your organization is deeply invested in a particular enterprise application suite, you should definitely put the integrated analytics and BI offerings from that vendor on your shortlist. Consistent data models and pre-built data integrations, dashboards and reports, and built-in analytics accelerate deployment and enable rapid return on investment.
If you’re only using one public cloud and plan to keep it going, you’ll want to consider the tightly integrated analytics and BI offerings available in that cloud. Large, technology-diverse organizations tend to adopt stand-alone products that help them make sense of information across their technology landscape and, very often, across multiple clouds. Many organizations want to move from insight to action. [embedded analytics] Leaders can help them do just that.
Finally, are there any vendors that are far behind their peers in terms of the needs of today’s customers?
Hengsheng: Certain types of customers may need to consider vendors that aren’t on the shortlist. For example, customers who operate solely on AWS and are looking for particularly large deployments should consider Amazon QuickSight. Infor customers should consider Infor Birst. Also, any cost conscious organization, and any customer using his Zoho applications should consider Zoho Analytics.
this is where it pays [do due diligence] Don’t just look at the plots in the box and form a superficial opinion of what analytics and BI products your organization should consider. The report is just the starting point or stepping stone to the organization’s shortlist and final selection.
Editor’s Note:This Q&A has been edited for clarity and brevity.
Eric Avidon is a Senior News Writer at TechTarget Editorial and a journalist with over 25 years of experience. He is responsible for analytics and data management.
Doug Henshen
