Analytics is information resulting from the systematic analysis of data or statistics.
How Analytics Works
Every business is an analytics business. Every process is an analytics process ripe for improvement. And every employee could be an analytics user in some way. No matter what you plan to accomplish with analytics, the first requirement for any analytics project is data. Once you have data, you need to analyze that data. And then you need to deploy the results of your analysis to drive decision making. The faster organizations can move through the analytic life cycle, the quicker they can achieve tangible value from their analytics investments.
At Packard Business Consulting, we see these three categories – data, discovery, and deployment – as iterative steps of the analytics life cycle. Regardless of the scope or scale of your project, it should include all three steps. Let’s look at each step more closely.
Data today is fast, big, and complex. Analytics solutions must analyze data of any type, including traditional data and emerging formats. To access, prep, clean and govern that data, you also need a data management strategy. How will you collect, clean, and store your data? Data preparation is estimated to take up to 80% of the time spent on an analytics project. Could that time be better spent building models?
An intelligent analytics platform streamlines data preparation, integrated data quality and data preparation tools that automate time-consuming tasks with AI. Finally, data governance ensures your data can be trusted because you know the source and content and can monitor data quality. Data governance also makes it easy to protect data when appropriate.
Discovery is all about exploration, visualization, and model building. Finding the right algorithm is often a process of trial and error. But when it’s easy to document, save and compare those trials, the process works the best it can. Choosing the right algorithm depends on several factors, including data size, business needs, training time, parameters, data points, and much more. Even the most experienced data scientists can’t tell you which algorithm will perform the best before experimenting with multiple approaches.
If you want your analytics efforts to pay off, you need to deploy the results of your discoveries and put them to use. Machine learning and other models are not meant to sit on the shelf – you must use them to get the business value. Yet the deployment phase is where most organizations struggle the most.
Whether you’re building a single model or thousands, moving from selecting models to deploying models requires model management. Model management provides version control and helps you register, validate, and centrally manage your models. It helps you develop procedures and rules for model deployment and monitoring. And you also get transparency about data and model use.
Your goal should be to build models once and deploy them anywhere – to executive dashboards, right into operational systems, or built into other apps through APIs.