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.
And, thus, the data scientist is responsible for doing everything — interacting with executives, putting models in production and so forth. But instead of relying on a sole individual to give a company’s big data and analytics investments merit, perhaps it’s now time to revisit the idea of an integrated team — one that understands their piece of the puzzle and how it can drive return on investment.
The shift to multidisciplinary teams can take an idea from inception to designing a solution, creating an architecture for production and integrating that solution into production using DevOps skills. Instead of imagining that one person could perform these tasks end to end, the team should have different players with skill sets that create a more impactful process to get the desired results. As an added bonus, businesses won’t need to employ a handful of very expensive data scientists to spend all their time babysitting models. Instead of being strapped for time, these teams will have the resources to better focus on the most challenging operational tasks and innovating next-gen solutions.
Taking a multidisciplinary approach to data science and advanced analytics can free up staff to focus on and think about business innovation. A shift in approach, away from a single data scientist and toward a team of specialists can enable better outcomes for businesses keen on getting more out of their analytics investments.