Every business wants to get the most out of their data. Some firms are looking to forecast revenue probability, while others want to reduce churn or calculate winnability. So how do you go about harnessing your data to drive predictions?
For many companies, this journey can feel like a steep hurdle. It can be difficult to figure out the path you need to take to convert your data into actionable insights. But don’t worry — Horizontal is here to navigate. To help you simplify this intimidating process, we’ve broken it down into three digestible steps: analyzing data, operationalizing and piloting AI capabilities and making intelligent predictions.
For today’s conversation, let’s focus on the first step.
The problem with “good enough” data
The first step to achieving predictive analysis is data health. Now you may ask — why data health? Isn’t my data “good enough?” Clean, consistent data is the foundation of any good prediction. Having “good enough” data will give you a “good enough” model. But that won’t result in the outcome you’re seeking.
Once you start digging into your data, it can be hard to know what to focus on. Do you check for duplicates? Which fields are important? What objects should you reference? This is exactly why we recommend you use a tool like Tableau CRM (formerly Einstein Analytics) along with an app we purpose-built to answer these exact questions.
Horizontal Digital’s Data Health app
Our proprietary Data Health app is a Tableau CRM tool you can implement into your instance in minutes. Once installed, it provides a series of dashboards that tackle the key questions you need to answer to understand the quality of data you have in the areas that matter the most.
As you embark on your journey toward predictive analysis, it can be helpful to examine a few real-life examples. Register for our webinar on December 3 to see how we turned data into insight — and insight into action — for two very different companies: Lindus Construction and Boston Scientific.