6 Major Tips of a Successful Predictive Analytics Project This 2016
Over the years we all helped organizations bring predictive analytics modelling in house and have learned a lot along the way. The use of predictive analytics has had a great impact on the company impact, resulting in better business decisions as well as the strategic role in the IT. Software based predictions facilitate the orientation in complex and violate the markets. Thus using predictive analytics could lead you with increasing revenues and profits speeding up the change and optimizing the business processes. Below is the best of list of ten major tips that the employees of our business organization helped putting together in order to make a modelling process.
Start thinking about the entire process early: Before the project kicks off, know what exactly your goal is along with the major pieces of data that it would take to arrive. This would here ensure you that the critical information that has to be tracked has been tracked or atleast has the ability to be synced into another database using a unique identifier.
Design your data collection: Data is generally collected because the decisions were made on where you need to put the sensors, what sensor frequency has to be set, what data has to be aggregated and the last but not the least how to design the app. When it comes to predictive analytics for some of the applications there is a need of longitudinal data or the data that is aggregated with a certain frequency. And in the other cases when it comes to predictive analytics any missing data would here represent the information that you actually need in the model, as imputing them could completely destroy them.
Starting with simple and developing your own scenario: Good results would always require a learning process, and there are many possible uses for organizations when it comes to predictive analytics. Start with a simple, manageable and implementable scenario in order to expand and refine the use cases through incremental improvements.
Choosing the right person for your project: The person who is in charge of predictive modelling would have to collect, analyse, and work with the results gathered from your data. The person here has to be a creative problem solver who is actually willing to learn. Ideally this would be the person who has already been working within the data. Remember a person who understands your data on how it is stored, labelled and used can learn any statistical technique that they need to know and hard to do so while doing so.
Do not get lost in the translation process: The data driven approach is here gathering a lot of transaction and success. And in most of the business organizations today the process is still heavily designed and optimized around people. While introducing predictive analytics within the organization you are changing the adoption process, and the results could be achieved only when everyone are on board and you have a plan in order to change the entire process.
Plug and play: Once you have constructed the entire model plug in the numbers in order to see and know what your final results generally look like. Use an eye test in order to determine if there are any sort of adjustments that has to be made and at which step of the model. Sometimes seeing some of the end results would help you focus on the things around so that you can go ahead and make the adjustments rather than getting stuck in the mud.
These are some of the above mentioned tips we have generally come up with, and I am sure that there are more to come up yet. So if you have tips, advices and cautionary tales we would love to hear them in the comments below.
The post is by Amy Jackson, a freelancer and she has experience of over 7 years in Supply Chain management solutions, Procurement and Analytics industries. She has been writing on Supply chain planning, predictive analytics solutions, Prescriptive Analytics solutions and other solutions. Follow her on Google+ and get the scoop on the latest business trends.