Part of the process is balancing the costs and benefits of the models you're considering. With statisticians and other quants in short supply, think about what skills you’ll need for the kinds of models you want to build. However, you need to think about the skills you’ll require for data management, as well as the skills to build your models and deal with your data. The democratization of analytics is moving ahead. Good starting points include geospatial data and text.
Think about incorporating data beyond the traditional types you might have in your data warehouse or on your servers. Utilize disparate data.Īlthough structured data and demographic data are the mainstay of analysts and modelers, disparate data types can enrich a data set and provide lift to models. Start with a proof of concept.Ĭompanies succeeding with predictive analytics often start with a metric they’re already measuring, so they can demonstrate that they can predict that metric - they know it's valuable and will get attention. Vendors are making the tools easier to use, and with the right controls in place, this can be a good place to start. Often a good first step into the world of advanced analytics is predictive analytics. Although this requires skills and training, the upside is clear. Consider more advanced analytics.Ĭompanies measuring value are using more advanced analytics.
Yes, this can include the data warehouse (don’t expect the new stuff to replace the old), but it should also include the right tools for the jobs, including in-memory computing for highly iterative analysis or cloud computing to deal with vast amounts of data that might be generated in the public cloud and on premises. Consider new infrastructure technology.Ĭompanies succeeding with next-generation analytics are putting together an ecosystem that consists of multiple technology types.
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