It has been said that “Forecasting is an art, not a science.”. Let us apply this proposition to depict our vision of forecasting.
If the forecast is the artwork, then the practitioner becomes the artist, the users of the forecast are the audience, and evaluators and academicians are equivalent to art, music or literary critics.
Consider a piano concert: The pianist’s performance depends on the quality of the instrument, his/her technical competence, and interpretation of the composer’s musical score, as well as the quality of the composition. The critic then evaluates the performance by commenting on the technical competence and interpretive skills of the soloist as well as the quality of the composition.
Our forecasting critiques are based on analogous criteria. We at SEE-R evaluate and compare the accuracy of models, which may be considered the quality of the forecasting instrument; technical competence is measured by whether the model was estimated properly; judgmental adjustments or inputs are analogous to musical interpretations; and our data are similar to the composer’s composition. Thus the accuracy of a forecast depends on a variety of factors which together represent the forecasting process ( the concert).
We have been investing a huge amount of time to analyse each of these factors, models, data, techniques to understand the weakness of traditional approaches and how forecasters perform on average. However, it is not enough to critique the predictions of others. Unlike the music critic, we are creators - composers if you will. In creation, our mission is “discovering the future in data” developing new methodologies to improve data quality, accuracy, and usability of the forecasting process. Finally in this analogy, we are performers: retesting, recalibrating and readjusting our predictive analytical methodologies into the best Forecasting Software, to satisfy a discerning audience.
High-quality forecasts for anyone. Unprecedented simplicity for non expert-users.
Predictive Analytics software is a powerful instrument for organizations. It helps them to create competitive advantage and make their business processes more effective. Insurance companies and credit card issuers for example use it to detect fraud, cops use it to catch criminals, sometimes even before they commit a crime, and car dealers apply analytics to predict the chance that someone responds to a campaign. We at SEE-R strongly believe that there is huge, most unexplored, added value in predictive analytics. But, prediction is very difficult – especially if it’s about the future (Niels Bohr) – and making automated predictions is even more difficult. There are many pitfalls on the road to success.
Using Predictive Analytics Tools
Predictive analytical tools are not a panacea for every business issue, especially for the ones that are still unknown. Just gathering a lot of data, installing a predictive analytics solution to mine them all and see what pops up, in the ‘rarest of rare’ cases can enable you to find a golden nugget, but often the Predictive Analytics Software comes up with worthless or spurious correlations. Before you start using any predictive analytics solution, you should have a clear view of the actual business application and ask yourself “which business problem do I want to solve and which data do I probably need”.
Forecasting Software & Data Scientists
Forecasting using traditional approaches is a multidisciplinary skill that requires deep understanding and knowledge of statistics, data massaging and applying the right visualisations. There are not many people in the world who are able to do the job properly and make a success of it. If you have a challenging business issue where predictive forecasting software might help, you will be required to hire the best data scientist you can find. We at SEE-R want to move predictive analytics from data scientists (expert-users) to business users (non expert-users).
Forecasting Software & User’s Perspective
Once you have booked success with your Forecasting Software (it works and the outcomes make sense) don’t suppose that everyone is immediately enthusiastic. Especially the business users of the forecasting software. They will need some time to trust and believe the outcomes because they’re not in control of the process to generate them. We at SEE-R don’t forget to manage the user’s perspective and spent all the needed effort to improve the forecasting service as a collaborative effort. Over and above, test and validate the forecasting software on a regular basis.
Forecasting Software & Stand-alone applications
Forecasting Software can’t be successful as a stand-alone application in the long term. It should be based on a solid infrastructure so the data can be properly cleansed and integrated. In fact, you need to embed Predictive Analytics Software in your daily business processes and make sure that the predictions can be easily used in other modules of your business intelligence platform (reporting, dashboarding, analysis and visualisation).
Forecasting Software & Data Quality
If bad data are able to decrease the added value of your regular reporting applications then bad data will certainly nullify the benefits of predictive analytics software. Bad data are a huge risk for your company if they fuel your predictive engine. Decisions based on poor data quality in reporting or dashboarding can have a big impact, both medium and long term (depending on the level). Decisions based on poor data quality in predictions will have a tremendous impact on your financial results, on customer satisfaction and on your company’s reputation. If you are indifferent to the value of prediction, you may ignore the issue of data quality.