Predictive Analytics can help reduce your Data Decay

Data decay isn’t isolated to just one industry. Its adverse effects span all sectors and can take many different forms.

Issues stemming from data decay can range from inaccurate information on a retailer's website to stale member information that interrupts a healthcare payer's social system. Consumers can also become frustrated by the issues caused by decaying data, leading to a loss of trust in the business.

Many companies spend a lot of time and money manually cleaning and scrubbing their data. Still, sometimes the entire dataset must be scrubbed to locate the bad data attributes and values. Recently scrubbed data is ideal for predictive analytics and forecasting capabilities. With that in mind, wouldn't it be beneficial to anticipate which data will soon be at risk and take corrective action before it is too late?

Using historical data, predictive analytics determines what factors are essential in influencing results. A scrubbed data set, for example, could be leveraged as predictor input variables (i.e., the date of the last contact, the customer age, the previous purchase amount, etc.). The scrubbed result would then be used as the outcome variable to determine if the data is good or bad. Predictive models could then determine which variables influenced the result and their level of impact.

How does predictive analytics reduce data decay?

Data decay occurs when data loses value over time due to storage limitations or other reasons. In other words, the longer data remains unanalyzed, the less valuable it becomes. By using predictive analytics solutions, you can reduce data decay. Predictive analytics allows businesses to identify patterns and trends in their data and predict future events. This enables them to address issues proactively before they become serious problems.

By using predictive analytics to irradicate data decay, companies can better manage their data assets and prevent them from becoming obsolete. In addition to reducing costs associated with data decay, predictive analytics can also increase an organization's productivity.

And while predictive analytics solutions are becoming more mainstream, companies are still exhausting resources on solutions that don't deliver. This leads to wasted time, money, and ultimately less efficient processes.

Choosing the Right Statistical Model

When reducing data decay, statistical models are a crucial factor. Using models designed explicitly for predictive analytics enables businesses to get more informed predictions and take proactive measures to prevent data from becoming antiquated.

There are several different statistical models, each with its strengths and weaknesses. Choosing the right model for your specific needs is essential to winning the battle against data decay.

Some of the most popular types of statistical models include:

  • Linear regression
  • Logistic regression
  • Random forest
  • Support Vector Machines
  • Neural networks

Each of these predictive models has its advantages and disadvantages. For example, linear models are simple models that are easy to understand and interpret. However, it is not as accurate as some more complex models.

On the other hand, support vector machines are more accurate but difficult to interpret. Neural networks are very accurate but require a lot of data to train.

The type of model you choose will depend on your specific enterprise needs. If you require very accurate predictions, you may want to use a more complex model. If you need an easier or more visual interpretation of the results, then a simpler model to correspondence analysis may be better.

Choosing the Right Data

Another critical factor in reducing data decay is choosing the correct data. Not all data is created equal. Some information is more valuable than others.

When choosing data, you should consider its:

  • Accuracy
  • Timeliness
  • Relevance
  • Completeness

If data is inaccurate, it will not be helpful for predictive analytics. Data that is not timely may also be less valuable. And data that is not relevant to your specific needs may also be less valuable.

It is also essential to make sure that data is complete. Data that is missing critical information will be less beneficial for predictive analysis.

You give your predictive analytics model the best chance of reducing your data decay by choosing the correct data.

Other Options for Preventing Data Decay

So far, we've highlighted how predictive analytics can help prevent data decay, allowing companies to keep data fresh and accurate.

However, there are other steps an enterprise can take to help prevent data decay if adopting a predictive analytics model isn't an option. These include:

  • Data cleansing: The process of identifying and correcting errors in data. FYI, this can help to improve the accuracy of your predictive analytics.
  • Data purging: Removing old or outdated data from your system. This will help to keep your data up to date.
  • Data governance: Ensuring that your data is accurate and consistent. This can help prevent data decay and improve the quality of your predictions by regulating data access to eliminate redundancies.


Data decay is a significant problem for businesses that rely on data. By taking proactive measures to address these issues, companies can keep data quality at a high standard. Predictive analytics can help enterprises reduce data decay by identifying patterns and trends in their data. Contact the data decay experts at Data Ideology today to help you set up your predictive analysis and eliminate data decay today and in the future.

Written by Mike Sargo

Chief Data and Analytics Officer and Co-Founder of Data Ideology

Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.


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