What are the biggest challenges facing predictive analytics today?

Noam Brezis

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Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide the best assessment of what will happen in the future.

Predictive analytics is difficult at the best of times, and yet enterprises are increasingly turning to predictive analytics to predict the likely behavior of customers, the possible success of digital marketing campaigns and even what content is likely to work best in those campaigns. But can it predict the outcome of something like the World Cup that has so many variables in the mix?

While many companies are already using a form of analytics applications, fewer are using predictive analytics. However, it is already gaining traction in the banking and financial sectors, retail, health, insurance, and manufacturing - in fact, any industry where future risk assessment can impact business.

Realizing the full potential of predictive analytics requires overcoming significant problems in various fields, among them:


Every company has developed its method for managing data in their databases. Some of the common data challenges are:

  • Understanding your business so that you understand your data and its usefulness. Data can never be understood without understanding how the business operates. Business and data scientist need to work together to create something meaningful. Learn to love data and not let yourself be satisfied by Enterprise Information Management systems.
  • Knowing when data has outlived its usefulness. Often, people include in their analyses data that is, say, many years old to predict the near future. So, understanding how old is too old is very important.
  • Dealing with the problem of plenty. Integrating datasets across operations, sales, finance, websites, and social media is difficult in itself; and curating and finding customer matches across sources is one of the major challenges in creating an integrated dataset of the customer.
  • Digging deep into your data. You should always use curated and clean data. Make sure that your data scientists dig deep into the data and fully understand it so that they don’t encounter problems in later stages of modeling.
  • Feature engineering. A lot of the effort in data modeling is in identifying and constructing features that could be important. The quality and quantity of the features will have a great influence on whether the model is good or not. Better features can produce simpler and more flexible models, and they often yield better results. But feature engineering demands a lot of work and a deep understanding of your problem. Don’t be satisfied with just using the available metrics without assessing the importance of normalizing, aggregating and formulating a feature.
  • Understanding industry-specific issues. A modeler requires a deep understanding of the domain.  Modelers need to keep on exploring algorithms and ways to find the required solution. For the best model, the modeler needs to comprehend the complete cycle of the overall process fully.
  • Finding experts in R, python, etc. It is extremely important to know how to use query languages effectively. Data scientists devote most of their time to preparing data for the algorithm, and for this, they need to write code.
  • So much to do. Every predictive analytics project requires an extensive list of steps, which are almost always handled by a dedicated data scientist. The challenge is that for every update and release, these steps place more of a burden on your application team. They include:
  1. Data preparation
  2. Data cleansing, data wrangling
  3. Ensuring the data format is correct
  4. Identifying important variables
  5. Recognizing correlations
  6. Dealing with imbalanced data
  7. Understanding how different algorithms work
  8. Choosing the right algorithm for the right problem
  9. Deciding the right configurations the algorithm
  10. Understanding the output of the algorithm
  11. Re-training the algorithm with new data
  12. Deploying/re-deploying the model
  13. Predicting in real time/batch
  14. Integrating with your primary application to build data insights into the application and initiate user action

In conclusion, predictive analytics presents daunting challenges to data scientists. The business benefits are enormous, and failure is not an option.
New, emerging automated AI services can decrease some of the burden and enable a faster and easier time to market.

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