If you were to build a house, it would take more than just supplies. It requires a sturdy blueprint, a capable team and the right resources to bring it to life.
The same process is true for insurers developing an advanced data and predictive analytics solution. It starts with a plan, followed by quality data, a capable team of both architects and data scientists, and a handful of other factors to successfully build and incorporate the practice into the business.
While this may seem like a massive undertaking, it can be tackled in smaller, manageable steps. Here, we will address the necessary must do’s regarding data whether an insurer is building, buying or creating a hybrid data solution.
The must do’s of data collection
Have the right team in place
In an ideal structure, a data engineering team will collect, cleanse and maintain an efficient database that can be leveraged by the data scientists. A data architect is best equipped to lead the data engineering team to design high-level reports for executives, append external data sources, update databases, and work closely with the data scientists to help build predictive models.
Work in an ecosystem
Any data project should be part of a bigger ecosystem, and the more integrated the better. The team must consider everything from the sources of data, to the integration points and even training of the end users. When all the parts play together, you have a cohesive solution that can deliver quality insights. The most common reason for failure is when the implementation hasn’t been thought through. Therefore, when it’s time to implement, the technical staff needs to collaborate with the business to make sure a plan is in place.
Conduct a data inventory
It’s important to take a technical inventory of the data you are using. This provides an initial benchmark of data and relationships, and what you’re working with. Things like summary statistics or data sets with no variability or poor populations are useful to know up front. Not only is this helpful initially, but also for on-going monitoring and performance evaluation of the solution.
Now it’s time to evaluate if all of your data is relevant and meets expectations. This process will help you identify where the data needs to cleansed and what you could potentially remove from the model data set. You can use different types of visual diagrams to illustrate this process including heat maps, trends, aggregation models and more.
As you go through the process of inventory and cleansing, you will ultimately find things that cause you to reexamine the data. Data governance provides visibility to the changes that occur to data profiles. Keeping track of data versions, revision history and security is crucial in understanding the evolution of the data and how it influences your modeling efforts. When data governance is lacking, there are significant risks including a lack of ownership and transparency, to name a few.
These components make up the blueprint that becomes your overall approach to an analytics-based solution. Step by step, we walk through how to begin building each of these pieces in our recorded webinar, The Complete Guide for Implementing Predictive Analytics for Data Scientists and IT Professionals.