Driving Enterprise Insights: Why AI is Critical
Driving Enterprise Insights: Why AI is Critical
By Pete Reilly
Insights are critical components of a data-driven culture. They provide the information decision-makers need to understand their data and take action with the best ROI.
Yet, driving insights at an enterprise level requires understanding the analytics landscape and how frontier technologies like AI are shifting the dynamics.
Investing in AI-driven analytics can yield significant returns. Let’s break down why AI is critical for enterprise insights.
The Current State of Enterprise Analytics
When it comes to enterprise analytics in business intelligence, dashboards are ubiquitous. Dashboards are historically descriptive, meaning that they show the data through visualizations, charts, and pivot tables.
To build dashboards, technical employees like data analysts must access business data that’s relevant to specific questions that need to be answered or KPIs that need to be analyzed.
From there, data analysts typically predefine dashboards for business users based on the user’s department, role, or level of data access. Since these dashboards are descriptive, they show what’s happened in the data but don’t answer the larger question of “why.”
Analysts must still diagnose the data, interpreting dashboards and explaining the data story to business users. If business users have new questions or follow-ups on the data story, they can’t get answers independently.
The process of answering a new question can take days or weeks. Business users must weigh the impact of getting the answers they need against the time it will take to get them when decisions don’t have the luxury of being put on hold. Meanwhile, questions and routine reports pile up for data analysts.
Throughout this process, the question of “why” is often buried because dashboards are designed to demonstrate “what.”
For example, understanding past data trends is important for predicting future trends. But the time it takes to simply recreate and describe past trends in a dashboard can be significant. Once the dashboard is built, the analyst must still interpret the results to determine which metrics influenced past trends the most— finally getting closer to “why” past trends changed and how they’ll change in the future.
During this time, more data flows into the warehouse and more business questions arise. Work piles up. The likelihood of the answering “why” diminishes.
This current state is common for the enterprise.
In practice, businesses with low analytics maturity will likely bear the consequences of “rogue” decision-making instead of the benefits of a truly data-driven, curious culture. When questions are tedious and difficult to answer, employees are less likely to ask them in the first place. Employees instead make decisions on gut feelings or outdated information, a frustrating notion when the answers are there in the data— but inaccessible to the people who need them.
On a fundamental level, the current state of analytics is difficult, time-consuming, inflexible, and insufficient. Business users act on partial, delayed insights. Data analysts struggle to answer “why” questions due to sheer volume of requests.
The challenges of the current state mean most enterprises aren’t delivering meaningful insights at scale. That’s why AI is so critical now. It offers a true competitive advantage for the companies who can implement it successfully.
Why AI Matters Now
The growth of data volume is exponential. As such, the amount of analysis necessary to make data-driven decisions will increase in tandem. Businesses will need to anticipate growing costs of hiring analysts as well as shortages of strong candidates.
Because challenges with analytics will continue to grow, now is the time to capitalize on the rise of AI, which is more accurate and powerful than ever before. Along with the increase in data is an increase in computing power, a juncture that supports the growth of AI (and specifically deep learning).
Early AI adopters have the potential to outpace the competition with fast, comprehensive data insights that solve for the business challenges in the current state of analytics.
How AI Drives Enterprise Insights
AI disrupts the current state of analytics by 1) automating steps in the analytics workflow with machine learning and 2) augmenting the employee experience with natural language technology.
Specifically, a cutting-edge advanced analytics solution can operate as such:
Users ask natural language questions to trigger analysis. This interface takes inspiration from the consumer experience. Natural language processing (NLP) enables users to speak to an analytics solution in the way they’d speak to Siri, like so: “how did our brand perform last quarter?”
The solution automates analysis based on the question asked. The NLP understands the intention behind a user’s question. Questions with “why” or “how” trigger machine learning algorithms that test every relevant data combination to uncover patterns, root causes, and drivers in the data.
The solution returns an answer that makes the most sense in context. For example, the solution may provide a visualization to represent trends. For more complex questions, the solution can provide data narratives that thoroughly explain what’s happening in plain language via natural language generation (NLG). These data narratives contextualize insights, explaining not just that brand performance increased last quarter, but which factors contributed to and detracted from performance the most.
With AI-driven analytics, business users instead of data analysts can access and understand their data. They can ask questions in their own words and receive answers in kind. Meanwhile, the machine performs analysis at a deeper level than a single person could in the same timeframe (seconds instead of days).
The potential of AI analytics to reshape insight generation for the enterprise is astronomical. This technology already exists. Businesses already have the data.
Adopters can leverage AI in analytics to democratize data across their organizations, cut costs, and flip the current state of analytics into a competitive advantage.
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