3 Game-changing ways companies are using AI to transform analytics
AI is the new game-changer in democratizing analytics because it makes data insights and analytics significantly more accessible to a wider range of users. By using AI as part of everyday analytics workflows, users from the C-suite to the front lines, who have a broad and diverse array of expertise, can access, analyze, and interpret data in a user-friendly way.
Better yet, they can now gain contextual and relevant insights that break through the traditional barriers that, in the past, stood between data and action. A recent study by Nvidia found that 81 percent of executives are expecting AI to drive a 25 percent efficiency gain over the next two years, while one-third believe efficiency gains could top 50 percent.
We’ll be honest, it’s long overdue. Analytics and business intelligence promised to put the power of data into the hands of people in all types of roles so that everyone could make smarter, data-driven decisions. Yet, as of January 2024, 80 to 90 percent of knowledge workers lack the technical skills, data literacy, or access to use today’s supposedly easier analytics tooling effectively. The tools are not integrated into the daily experiences of their intended users. As a result, it feels unnatural for those users to use them.
However, the good news is that if you’re delivering analytics to your users—or perhaps considering adding analytics to your application—this shortfall in the state of analytics presents a great opportunity to drive success. AI offers two incredibly powerful ways to drive engagement and insight: Natural language processing (NLP) and AI for predictive and prescriptive insights.
NLP for intuitive data insights
NLP is machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language. For people without a technical background, using NLP makes asking questions of the data and getting useful answers more of an intuitive experience. After all, almost everyone outside of the analytics specialty doesn’t know SQL, or Structured Query Language, which is a domain-specific language used to manage data, especially in a relational database management system. Most people also aren’t familiar with the intricacies of dimensions, fields, or engaging with semantic layers.
NLP technology lets users interact with data analytics tools using natural language queries (NLQ). Instead of needing technical expertise in querying databases or writing complex code, users can simply type or speak their questions and retrieve relevant insights and reports. Sounds attractive, right? But here’s the catch: To deliver NLP in a way that feels natural, you’ve got to weave it into your app’s experience, using techniques like composable development. That way, it flows right into your app as part of the existing user experience.
AI for predictive and prescriptive insights
The second way AI can drive engagement and insight is by providing predictive and prescriptive insights directly within the apps your users use every day. Why? Because displaying the traditional type of historical data (descriptive analytics) that we’re all familiar with is often not immediately actionable for most users. People want instant insight, right there and then, in the apps they are already using, so they can put that insight to work immediately.
For example, if your users are a sales leader, seeing revenue trends in a dashboard is useful. However, it’s more actionable and valuable to understand what your quarter will look like if current trends continue, considering seasonality or potential anomalies. For greater relevance, it’s better to receive an alert that highlights urgent trends as “must act now.” This way, you can easily distinguish the signal from the noise.
What if you could take it a step further and provide prescriptive recommendations on what to do next directly in your sales app? For instance, you could serve up a proactive recommendation, such as launching an incentive for a specific sales region to drive revenue.
This is the power of using AI to drive engagement and relevance, enabling your end-users to rely on it to enhance their performance. Technology has finally advanced to deliver this capability.
Using AI to deliver analytics right at the point of decision
Imagine an airline where every employee uses insights to make better decisions throughout the day. Front-line employees, such as gate agents, receive relevant and highly contextual KPI alerts no matter where they are. Safety managers can proactively predict which airplane parts need attention and act accordingly. Don’t believe it? This airline exists today. It’s Air Canada.
Rather than continuing to centralize insights through traditional dashboards, analytics tools, and data specialists, Air Canada took the opposite approach. They infuse analytics into their teams’ daily experiences and power those insights with AI.
At Air Canada, AI-driven analytics were integrated into various facets of their operations. They leveraged the devices employees were already using, such as mobile devices, smartwatches, and smart speakers, to boost engagement. By providing highly relevant insights through these devices, they enabled better decision-making in the moment. Employees no longer needed to pause their work to find a computer, sign in to a portal, and search through dashboards, emails, and PDFs for data. Instead, the company pushed contextual insights directly to them, personalizing the data and delivery method based on the employee’s role, location, and business event.
Air Canada’s success hinged on two essential elements. First, by using an analytics platform capable of refining raw data into actionable insights and enriching it with AI. Second, composable development APIs were required to feed context from their applications into the analytics stack and deliver insights from the data side back into their native applications.
With Sisense, Air Canada has gone far beyond traditional analytics to drive insight directly to the front lines. As Shaul Shalev, Safety Analytics and Innovation Manager at Air Canada said, “We would not have been able to develop the game-changing analytics innovation we have today without having this platform to build on.”
This is democratized AI-powered analytics in action: Everyone in the company makes smarter decisions throughout the day without ever having to leave their existing workflows or learn new skill sets. Wherever they are, they can use insight to crush their role and outperform the competition.
We would not have been able to develop the game-changing analytics innovation we have today without having this platform to build on.
– Shaul Shalev, Safety Analytics and Innovation Manager, Air Canada
Use data and AI to break free from the analytics status quo
If providing contextual, relevant, and engaging analytics powered by AI has so many benefits at companies like Air Canada, why don’t other businesses follow suit?
The answer is that it requires a fundamental evolution in how we approach analytics. Most companies expect employees to learn separate dashboard tools to make better decisions, but these tools often come in the form of standalone user experiences. For most everyday business users, this requires too much time and effort. Moreover, accessing AI/ML-powered predictive insights often demands data science skills that they lack. Consequently, they end up making decisions without the benefit of data, because the analytics aren’t infused into their daily workflows.
Seizing the AI-infusion opportunity
Like Air Canada, we need to extract insights from data and infuse them into employees’ existing workflows, apps, and devices. Picture a CRM that analyzes data to automatically suggest which accounts to contact, a customer service platform that proactively identifies accounts likely to churn, or a retail app that detects shifts in purchasing trends and recommends inventory changes. Additionally, consider an integration into Google Sheets and Microsoft Excel that allows users to access data in their Snowflake database using natural language querying and import that data as an always-up-to-date spreadsheet.
These examples empower end-users to make smarter decisions without pausing to dig through separate dashboards for vanilla insights.
Empowering educators with intelligent, contextual decision-making
Freckle, an education company, enables schools to gain insights into student progress using data. Upon realizing that traditional dashboards weren’t effectively reaching school administrators, Freckle pivoted to embedding insights on student progress—rather than just presenting aggregate data—directly into the product. This approach allowed school administrators to access real-time analytics and recommendations within the app they were already familiar with, without the need to learn additional technical skills. With data readily available, school administrators could collaborate with teachers and promptly identify the most beneficial exercises, as well as students who might require additional attention.
Get started with AI-infused analytics
If you’re ready to take the next step, we’ve got you covered. Check out our comprehensive AI and Machine Learning guide, “Unlocking end-to-end AI for analytics: From ML to GenAI,” which delves into the technology, from AI data preparation to incorporating natural language chatbots into your applications.
Or, explore the latest in composable development with the Sisense Compose SDK. This tool allows you to seamlessly integrate analytics into your applications, tailored to your exact visualizations, look, and feel for the best user experience.
Feeling inspired? Sisense product experts are here to help. Schedule a meeting with a Sisense expert, and let’s discuss how to infuse analytics into your end-users’ daily experiences.
