5 challenges companies face when building models for predictive analytics

Last few years have witnessed a great development in data science; in particular, in relation to the by the (predictive analytics) Predictive Analytics that has become of the most important tools that help companies enjoy in the market today, helping companies understand their customers and their requirements, which helps the decision makers within companies to take appropriate decisions constantly.

Shown Research McKenzie that the application of the strategies, big data has become a major reason for the growth of businesses, strengthen their competitiveness, thanks to improved efficiency of operations and optimal use of resources.

But unfortunately, most businesses have learned how to collect data on the safety of customers, but few have learned how to exploit it. Currently, only a small proportion of data collected by companies for download, so still need tools to derive useful insights from big data.

The following are the 5 challenges companies face when building models for predictive analytics:

1 – lack of attention to the quality of the data:

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Companies face the problem of data integration, since data that need to be analyzed come from a variety of sources, in a variety of different formats, as there is a much bigger challenge is the data is not reliable, since Big Data is not 100% accurate, not only because it can contain false information, but because they can be frequent, as well as may contain contradictions. Which leads to the results are not reliable and conclusions are incorrect based on the analysis wrong.

Solution: you must start building a business intelligence system, where each stage of data collection, storage, processing automatically, which prevents human errors.

2. use of limited data:

Despite the huge development of models of marketing that says the prediction of the safety of the client, but it is a good school, because the companies are training their models on internal data alone; thus promoting the models of corporate from the outside world.

You can’t be the predictions created by these models is accurate, where works like a wave in the market alone, and do not depend on competitors, and trends in demand, etc., so you must include all these factors when training these models.

3 – reduce the real value of predictive analytics:

Pilot enterprises can only assemble the ideas of their data, but for most companies still the real value of the forecasts is not clear, however you must start relying on marketing-based data, even if you don’t know how to do it perfectly.

Within a year you will have a solid foundation to start model building, or try another way to predictive analytics. Will depend on your data and to take decisions on the budgets for your advertising based on expectations is more effective.

4. do not use the correct techniques:

Grows the market for technologies that rely on data, as well as market the tools and services that help on data collection, storage and processing.

I started most of the analytical services in providing tools for predictive analytics that are based on methods and mechanisms are different, so you should choose the most efficient for your company, also be sure the possibility to use the tool selected for a long time, because the change will cost you a lot of resources.

5. do not choose the right team:

You’ll never be able to create predictive analytics without the presence of a professional team supports you, and understands how to spend remittances on daily operations, and without the right team you can’t ask the right questions, setting goals required.

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