Decision intelligence: The future of analytics
Decision intelligence is a new, powerful practice of using information to make more efficient decisions at scale. Often touted as "the new business intelligence (BI)," decision intelligence promises to take the insights from dashboards a step further than just pretty charts based on data. Decision intelligence effectively extracts value from data, giving decision-makers easy-to-consume answers -- often based on disparate datasets or multiple machine learning models.
Leading analyst firms, such as Gartner Research, are predicting that decision intelligence will become a major factor in enterprise decision-making in the near future. In fact, Gartner predicts that 33 percent of large enterprises will have analysts that practice decision intelligence by 2023. Let’s explore more about how enterprises can transform their decision-making with decision intelligence.
Challenges with modern decisioning
Modern decisioning is becoming significantly more complex than in the past. The best decisions need to take multiple stakeholders and the situational context into account, while continuously optimizing for a number of KPIs. Even so, many organizations are still using Excel Pivot Tables or visualizations from BI dashboards to extract meaning from mountains of data.
In efforts to up the ante on data-driven decision-making, some organizations invest heavily in predictive machine learning models to project future business outcomes. These investments often fall short because existing BI tools aren’t powerful enough to show business users how predictive data can lead to real, useful answers for their day-to-day jobs.
As a result, business analysts often get frustrated and make decisions based on their gut -- rather than what their data is telling them. In fact, according to Gartner, 58 percent of companies base half of their decisions on gut feel.
To ensure success, decision-makers need a way to predict the outcomes of their decisions to guarantee the best results possible. Decision intelligence applies AI to automatically predict growth opportunities and risks, and recommend the best strategies to address them.
What exactly is decision intelligence?
Decision intelligence was born from a need to improve on the current tools for decision-making. BI systems, as described above, were designed for historical reporting. The dashboards and data visualization reports BI systems produce were created to inform decision makers about what's happening at a given moment by looking backwards at historical data. Unfortunately, these technologies fall short when teams need to use the explosion of predictive insights available to decision-makers.
Decision intelligence is a practice born from the combination of dashboard overload and the availability of valuable AI models designed for future-looking predictions. Unlike data visualization tools, decision intelligence connects the dots between multiple machine learning models – helping to determine how the decision impacts other departments in the organization.
Gathering this level of context is far from easy. Nicole France, an analyst at Constellation Research, said recently, "For people on the front lines (analysts), context matters, and there’s a degree of complexity that’s difficult to get right. The goal is to present things in a clear, easy to understand way, so people can understand some complex analysis, and make a decision quickly."
Decision intelligence strips away this complexity and accelerates the translation from data to decisions, unlocking significant value for enterprises.
Building competitive advantage
Many enterprises have already realized that dashboards aren't enough when making decisions. Analytics teams need to get insights from machine learning models into the hands of decision makers in a way that is intuitive for business users. That’s where decision intelligence comes in.
Let’s take a look at two examples.
In retail, the amount of artificial intelligence (AI)-powered insights available to retail decision makers continues to grow, resulting in cognitive overload. Decision intelligence helps retailers to harness new market opportunities with AI. For example, a home improvement retailer could make inventory purchasing decisions based on predicting a particularly hot summer. A decision intelligence system could pull together inputs from multiple machine learning models to show the impact that purchasing more barbecue equipment might have on margins. The system could make a recommendation on what inventory to purchase based on channel, store format, shopper segment, and more.
In financial services, decision intelligence can provide an "Intelligent Advisor" to help identify risks, and shift credit to safer consumer segments. For example, a mortgage company may need to know which parts of the country are likely to defer on mortgages most often vs. do really well on their payments. How does that impact where the company acquires a new business unit or extends credit to consumer segments? Decision intelligence can answer these questions. The technology works continuously in the background, all while maximizing revenue and minimizing risk.
Why Decision Intelligence Matters
Decision intelligence goes beyond traditional BI dashboards, and translates AI models into valuable recommendations. These solutions can understand and apply business context, quantify the impact of competing business objectives, model different scenarios, and recommend the best strategies to meet enterprise goals.
Using decision intelligence, organizations can derive more value from their investments in data and machine learning models, and stay one step ahead of business opportunities by arriving at decisions significantly faster. As enterprises gain access to more data and increase their investments in machine learning, this technology will only grow in value. In 10 years, it may be hard to envision how we ever made decisions without it!
Krishna Kallukari is CEO of diwo. Prior to founding diwo, Krishna was a founding member and CEO of DataFactZ, one of the fastest growing analytics companies in the Midwest. He brings more than 15 years of analytic technology and senior management experience. Krishna is passionate about applying analytics to solve business problems.