Ajay Khanna is the founder and CEO of Tellius, an AI-powered decision intelligence platform.
Only 20% of organizations say they excel at making decisions, according to a study by McKinsey. But every single company needs to make decisions—there’s no way around it. As organizations amass more and more data—data that’s growing in complexity, too—it’s only becoming more difficult for them to extract the right insights to make data-driven decisions.
Why? Traditional data platforms and processes aren’t cutting it. Information can get caught between artificial barriers in tools, processes and teams, delaying the speed at which results are discovered and executed.
As a result, organizations can do one of two things: one, spend a ton of time preparing and analyzing data and building models, or two, let the quality of their insights suffer by using only a select number of variables within their data.
The main problem is that data analysis typically doesn’t scale as data volumes and complexity grow. Simply put, the demand for analysis outstrips supply. The analytics tools used today mean organizations are siloed into descriptive, diagnostic and predictive analytics buckets, and between data engineering, data science and domain-specific analysts, data consistently gets left behind.
The result is inefficiencies in analytics queues as different teams leverage different tools, leading to missed opportunities caused by delayed decisions.
Artificial intelligence (AI) is a common solution businesses deploy to solve this issue, but in order to truly reap the benefits of AI, users across the organization must be enabled to use AI-powered tools—not just data experts. It’s crucial for organizations to democratize access to these solutions, making data analysis more accessible to more people in the organization, and upskilling everyone to become more data-driven.
This is where decision intelligence can help bridge the gap between data analytics and business decisions.
What Decision Intelligence Is
Augmenting human decision-making, decision intelligence uses AI and automation to enable anyone to answer questions about their data more easily and quickly.
Decision intelligence helps to simplify complex decision-making by leveraging natural language to ask questions, ML-based diagnostic analytics to identify root causes and automated alerts to proactively identify market movements and anomalies. It provides intuitive and actionable answers back to the user.
Unlike typical analytics and business intelligence (BI) tools that often leave teams siloed and waiting around for answers, decision intelligence places predictive analytics in the hands of decision makers. It also unlocks analytics agility (i.e., the ability to ask and answer hundreds of thousands of ad hoc questions) to help businesses move faster, close the insights gap between business users and analysts, and drive efficiencies in leveraging data across an organization.
Decision Intelligence In Action
Deploying decision intelligence can simplify the adoption process of a new data strategy. For organizations of any size, the backlogs and silos associated with working with enormous amounts of data can threaten productivity and new opportunities for employees and customers.
Business users should be able to perform their own ad hoc exploration of data to drive results. For example, teams focused on brand performance and customer retention can apply decision intelligence to support critical business strategies and improve outcomes related to their individual business goals.
Manual analysis of brand performance and customer retention can take many hours with potential delays due to analytical bottlenecks and siloed organizations, resulting in missing critical commercial opportunities. It also requires discovering the best consumer, market and brand insights in a sea of terabytes of both internal and third-party data. Instead, when brand teams are able to unify their data sources, they can answer questions more quickly and better understand the drivers of consumer behavior, thus improving eventual business outcomes.
What’s more, it’s wildly more expensive to acquire a new customer than it is to retain an existing one. This is why having a keen eye on customer churn is crucial for organizations. Decision intelligence platforms leveraging advanced analytical engines can query billions of rows of data to uncover key drivers of customer retention and identify which segment(s) of customers are more likely to stay than others.
AI-augmented insight generation via decision intelligence can help remove bias from analysis, unlike traditional manual, hypothesis-driven approaches. Decision intelligence automatically analyzes every combination of when customers are retained and churned while highlighting each segment’s key change drivers. These new insights help businesses quickly pivot on strategy and prioritize focus areas.
How To Get Started With Decision Intelligence
There is a diverse array of options available to organizations seeking to empower business users with data today. As with every organization, data analysis starts with spreadsheets, which still have a very critical role in informing decision-making. Many organizations also may leverage BI platforms with dashboarding capabilities to solve simple problems.
These tools can still be effective in running analysis and keeping users informed of business changes via widely used and “known” metrics. However, decision intelligence can provide a path to help answer questions outside of the “known” while empowering business teams with key analytical tools.
For organizations looking to implement decision intelligence at their organization, here are some things to consider. First, look at your organization’s existing decision-making approaches, explore use cases and pinpoint the types of decisions that would lend best to decision intelligence.
Second, evaluate vendors in the space. Some platforms, although claiming to be true decision intelligence, are simply a rebranding of core BI or data science offerings. Evaluate vendors against the best-in-class core capabilities, and make a shortlist. Finally, start small if you’re not ready to roll out decision intelligence at scale. Look at just one or two use cases, or just roll it out to a small team. If you find value, then you can expand.
Without a way to accelerate data-driven decision-making, companies could be left behind in 2023. Although tackling the large influx of data organizations are now working with is no easy task, tools like decision intelligence can help simplify the process, giving anyone the power to turn insights into business value.
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