So, you want to see what an AI analytics platform can do for your organization?
From enabling better decision-making to driving a competitive advantage, to enhancing customer experiences, organizations are leveraging the power of data and AI to significantly impact their operations and overall business outcomes.
Although the journey to investing in the right AI-powered analytics platform might seem daunting, it’s a critical step in unlocking the full potential of your data. By understanding your objectives, evaluating features, and future-proofing your decision, you’ll be well on your way to making an informed choice that aligns with your organization’s long-term strategy.
After all, the right platform isn’t just an investment in technology—it’s really an investment in the future success of your business.
Written by: Betsy Lillian,
Content Marketing Manager at Tellius
In This Post
Why AI analytics over traditional BI?
Decision-makers aren’t looking for tools, per se—they’re looking for answers they can act on. Using dashboards and traditional business intelligence tools, decision-makers are left with gaps in their data analysis and, in turn, unanswered questions.
Dashboards and traditional BI tools contain a wealth of information, but decision-makers need to know more than just what’s happening at a high level—i.e., not just what happened, but why and how it happened.
But to do that, turning to BI teams or data scientists to get this level of analysis can be time-consuming and unrealistic for many organizations, faced with bottlenecks of report creation via the limited resources of the data team.
That’s why they’re implementing modern AI analytics platforms to not only uncover critical data insights, but also democratize access to this data—in turn, driving business actions through better decision-making.
Automated insights and reporting
When Gartner recently evaluated capabilities of modern analytics and BI (ABI) platforms, the generation of automated insights was at the top of the list.
After all, there’s been a long-time struggle for BI and analytics vendors to translate data into consumable insights for non-technical users and other stakeholders.
But that’s not the case today: Your AI analytics tool should have machine learning algorithms that automate—and, in turn, simplify—the process of generating insights for business users without extensive ML expertise.
Further, automated insights accelerate complex data analysis to identify the aforementioned why and how behind the what and provide direction as to how to improve outcomes by automating root cause analysis, analyzing key drivers, comparing cohorts, and identifying meaningful segments in data that go beyond first-order facts/drivers.
Using ML to automatically generate insights isn’t just a “nice to have”—instead, it’s paramount to simplifying the communication of complex insights.
“No longer the stuff of sci-fi, leveraging ML capabilities to make sense of ever-growing datasets is now table stakes within the ABI space,” Gartner said.
(Source: Gartner Insights: What to Look for in an Analytics and Business Intelligence Platform, Colin Reid, 2023)
Natural language search
Let’s talk about the magnitude of another key component of your AI analytics platform: natural language query.
Natural language search enables any user to flexibly explore and analyze unaggregated enterprise data through a Google-like search, enabling deeper ad hoc analysis.
Learning advanced SQL takes extensive training, and understanding the data can be another hurdle to effective decision-making. But natural language search lowers the barrier to data exploration and analysis for non-technical users—while also saving technical users time they’d spend developing complex queries.
In the analytics world, teams can’t always be defined discretely into categories of technical vs. non-technical—what about business and analytics teams who are somewhere along the spectrum?
This is why your AI analytics platform should be able to meet you in the middle, acting as a multi-persona solution where both teams can collaborate. Aided by natural language search, you can carry out the right data analysis no matter where you are along the technical spectrum.
Keep in mind that there are many analytics tools out there promising an easy-to-use “natural language search”—however, many come up short on implementation. They’re sometimes based on a simplistic keyword search, which limits the discovery of insights, or they’re implemented in a bolt-on manner with a large language model (LLM) like GPT. The reality is LLMs inevitably can introduce the risk of hallucinations, leading to inaccuracies or misinterpretations in analysis. GPT should be seen as an enrichment, not a basis, for the critical capability of natural language search.
Automated data visualization
Natural language search is also vital for the generation of automated data visualizations, further streamlining complex analysis and insights to influence data-driven decisions for the business.
Gartner, including data visualization in its critical capabilities list, particularly cites the importance of users to “more creatively visualize meaningful insights for non-technical audiences or cross-functional teams.”
Specifically, best-in-class AI analytics tools feature automated data visualizations that offer concise, decision-centric insights and visualizations. Users—no matter their technical expertise—should have the ability to create reports and interactive charts with the click of a button.
Typically, in a traditional, manual process, you’d have to spend considerable time getting trained on how to use a BI or analytics platform, memorize keywords to get the right dimensions you want to visualize, pick from hundreds of chart types, and so forth.
Instead, using automated data visualization generated by natural language narratives, you simply input a question, and it automatically visualizes the query. This significant reduction in time and effort leads to improved business outcomes across your organization, as data teams can spend more time on high-value projects while business teams reduce their time spent waiting for analysis—a win-win across the board.
(Source: Gartner Insights: What to Look for in an Analytics and Business Intelligence Platform, Colin Reid, 2023)
Easy data prep, connectivity, and scalability
Your AI analytics platform should also enable you to easily transform and enrich your data at scale and across multiple sources.
Analysis frequently requires you to bring in multiple data sources, and some lightweight data prep is necessary. However, it’s far too complex to use several tools to aggregate data and manually create KPI metrics. Plus, siloed workflows and tools just aren’t scalable for the complex data needs of businesses today.
Instead, AI analytics tools should be able to handle diverse and large-scale data from multiple sources (e.g., cloud data warehouses like Snowflake and data lakes like Amazon S3), integrate and prepare data for analysis, clean and transform data, and ensure quality and consistency.
The key word here is “effortlessly”: As Gartner puts it, your solution “should allow users to effortlessly combine datasets from approved sources and customize insights based on inputs those users define,” the firm said. No clunky, manual processes—just a modern data prep experience.
Moreover, your platform should automatically track all data prep steps in a visual pipeline so that there’s clear data lineage, reproducibility, and reusability of transformations for future projects. This not only saves time but enables business users to stay in the loop during the stages of data preparation, which can reduce risk and provide users with more trust in the analysis.
(Source: Gartner Insights: What to Look for in an Analytics and Business Intelligence Platform, Colin Reid, 2023)
Governance
This should be a no-brainer point, but proper data security is paramount. Look for AI analytics platforms that offer robust data encryption, access controls, and compliance certifications to ensure your sensitive data is protected.
As we heard at the most recent Gartner Data & Analytics conference, “powering AI with less data and fewer experts will make it cheaper, easier, and riskier,” said Whit Andrews, distinguished VP analyst at Gartner.
Use caution when exploring legacy BI and analytics vendors that aren’t built from the ground up to fully understand how to implement them in the context of their tool. Many offer narrow AI implementations that automate specific parts of the analytics process—bolted onto their existing software, rather than baked in.
While many legacy analytics vendors are playing catch-up when it comes to truly augmenting their software offerings with AI, modern analytics companies have been leveraging AI for years. Thus, it’s important to explore comprehensive AI offerings that are available in the market.
Future-proof your decision
Beyond just LLMs, it’s important to choose an AI-powered analytics platform that has the potential to adapt to—and seamlessly incorporate—other future advancements that could emerge with as big of a boom as generative AI did.
Keep in mind that as your business grows (thanks to all of your AI-driven decision-making), so does your data volume. Ensure the platform you choose is scalable to handle increasing amounts of data without compromising performance—the last thing you want to deal with is another delay in your decision-making.
Additionally, while traditional BI contracts are typically based on the number of users, the limit should not exist when it comes to how many people can use your AI analytics platform. Companies of any size, from smaller start-ups to global enterprises, should enable as many users as they need to discover meaningful insights, rather than be burdened by per-user fees.
The AI analytics potential is enormous for organizations looking to embrace this modern, self-service way of engaging with data: happier users, a streamlined analytics workflow, faster data insights, more productive data teams, and more confident decision-making.
When all of this leads to significantly improved business outcomes, why wait any longer to choose your ideal AI analytics partner?
Learn more
Tellius was recently recognized by customers as a Strong Performer in the 2023 Gartner Peer Insights “Voice of the Customer”: Analytics and BI Platforms.
According to the report, 92% of our customers would recommend Tellius’ AI-powered analytics platform, based on 33 total reviews as of 31 July 2023.
If you’d like to check out Tellius for your organization, request a demo here.
