Companies today can’t push their customers in a direction of their choosing. The new business landscape requires that enterprises actually know what their customers want and deliver on that. Companies must know when to connect with the customer, what the right message is and who influences that customer. To get this right, businesses have to commit to automation.
Autonomous decisioning, fueled by sophisticated models, is helping businesses understand their customers needs at a new level of granularity. But imagine if 100 customers fit into a model and each model needs to be monitored multiple times per day — the task of keeping up with these models, ensuring they aren’t biased or degraded, is impossible without any automation. Integrating machine learning and deep learning to drill down on customer wants and needs requires businesses to be strong in their analytics fundamentals, so they can create robust, scalable processes. Instead of focusing on one question or query at a time, they must be able to manage mixed workloads of complex analytics. You can’t simply say that everyone in an organization is going to do business intelligence or advanced analytics. It really requires a full-spectrum approach.
To get to this next level of analytics maturity, it’s time companies shift from focusing solely on their data scientist to be the captain at the helm of their analytics and business needs and instead create multidisciplinary teams, where everyone is working together to keep decision-making, and unsilo data to get a 360 degree view.
Unfortunately for many businesses now, their past investments drive their disparate businesses. They understood somewhere around 2014 that it would be a huge risk to ignore big data, so they funneled $100 million into a data platform but didn’t invest in how it would give them results — collecting data alone isn’t enough to create value. So now, when they pivot to analytics to create the value, they find the organization is rife with disjointed goals and lack of understanding of how to work together to reach them.
Business users understand that their company has a massive analytics project, but they don’t seem to understand what it is or how it works or how it can solve their business problems. IT is closeted away creating this massive architecture that can handle and manage the data but they don’t know how analytics is going to use it. Analytics teams spend their time creating really cool models, but then struggle to put them into production to solve business problems. These three silos try to do their piece of the puzzle, but it isn’t effective. To solve this problem, companies invested in data scientists — the unicorns that could perform magic.