We have a data diversion problem. Organizations have access to more data than ever before, but it’s almost impossible to focus through the noise and unlock the data’s full potential. While the transformation of the data industry over the past decade is undeniable, the way companies make decisions with data hasn’t improved much.
According to Seagate, 68% of corporate data goes unused, and I’ve personally heard larger companies report figures approaching 90%. Even companies that have invested heavily in a modern data stack still perform manual analysis of their cloud data – often getting lost digging through a giant haystack while also making mistakes and losing insights.
This state of affairs is a disservice to analysts, executives, customers and, of course, the company’s bottom line. With the ever-increasing volume and richness of data, business reviews and workflows should be data-driven. But that gap continues to widen, because what worked in the past doesn’t work today and won’t work tomorrow.
So how can we overcome the distraction to harness data for business?
Accurate and comprehensive analysis of all relevant data is now a machine-scale task. There is just too much data for people to analyze effectively and focus on what matters.
This does not mean that people have no place in the decision-making process. On the contrary, decisions are better with people in the loop. Individuals are intuitive, creative, and bring historical business contexts to the table. However, advanced technologies like machine learning are scalable, literal and unbiased. Machine learning can quickly analyze billions of data points to deliver what’s most relevant to help people focus. And together, machines and humans can successfully generate fast, comprehensive, and actionable insights without draining data teams looking for signals or requiring decision-makers to rely solely on gut instinct.
Come in, decision intelligence. While decision intelligence is a new and emerging discipline, it brings together advanced technology and analytical techniques (such as artificial intelligence and machine learning) with decision-making processes to help people make the best decisions. In short, decision intelligence is the sum of technology, process and people.
Decision Intelligence goes beyond business intelligence and augments decision making by recognizing both people and technology as equally critical elements in the equation. By improving existing tools and processes, Decision Intelligence extensively tests millions (or billions) of features and columns in cloud-scale datasets to provide statistically significant, relevant information at machine speed, reducing data distraction.
Over the next five to 10 years, corporate governance standards will continue to evolve, and it’s critical that the modern data stack follows suit. Integrating decision intelligence into your tech stack empowers your analysts, improves your results, and puts your data to work.