February 12, 2020

The Importance of Data Quality in Predictive Analytics

Predictive AnalyticsAre you worried about the quality of your data? If yes, then you’re not alone. Thousands of other businesses have the same concerns as you. Your data is probably your most valuable ‘intangible’ asset. So, it’s natural for you to be concerned about its quality. To ensure that the data available to you is accurate, reliable, complete, and free of errors, you need to manage the data quality. You can do this with quality assurance.

A combination of organization, methodologies, and activities, data quality assurance helps to reach and maintain high levels of data quality. In most organizations, quality assurance is applied to other activities that exist to maintain the activities or products of the company at a high level of excellence. Data quality assurance needs to abide by the same principles.

Reaching high levels of data accuracy within the company’s critical stores and then keeping them, there is the goal of data quality assurance. Data quality assurance needs to include all existing, important stores of data, and above all, it needs to be involved in the creation of new data stores or in the replication, migration, or integration of existing ones.

In addition to ensuring the accuracy of data when it is initially collected, data quality assurance is responsible for accurate access, accuracy decay, the transformation of data, and accurate interpretation of the data for users. Data quality assurance works based on three simple principles: improve, prevent, monitor.

Unfortunately, for many businesses today, QA is an afterthought when it should be the most important step for them. For example, in predictive analytics, organizations often overlook the QA strategy to focus on planning for additional controls on the DevOps side.

This needs to change if organizations want to get more out of the QA process. When it comes to predictive analytics, the quality of data is probably the greatest factor and the biggest driving force in both quality assurance and execution of this advanced analytics process.

Why Data Quality is Critical in Predictive Analytics

The advantages of using predictive analytics, big data, and machine learning are there for everyone to see. However, data quality is paramount to experience the true value and capabilities of these technologies. Unfortunately, most organizations have data that cannot be trusted.

According to a recent annual benchmark report by Experian, most businesses globally and 95% in the U.S use data to meet their business objectives. However, less than half of them trust their data’s quality. The trust deficit needs to be filled if a business wants to get the most out of their big data, machine learning, or predictive analytics.

Of these, predictive analytics deserves special attention. This is because it plays a key role in helping an organization to increase their bottom line and gain a competitive advantage. How does this technology achieve said goal? Let’s find out.

Understanding Predictive Analytics and How It Benefits Organizations

Today, predictive analytics is becoming increasingly popular among businesses in different industries. The reason for this increased adoption is easier-to-use software, faster and cheaper computers, the need for competitive differentiation, and growing volumes and types of data.

A great quality of predictive analytics is that it identifies future outcomes based on historical data. It does this by making use of data, machine learning techniques, and statistical algorithms. The goal is to provide the best assessment of what will happen in the future.

There are several reasons why an organization would want to use predictive analytics. Some of these reasons include increasing productivity, reducing cost, saving on misallocated resources, faster results, improved operations, fraud detection, risk management, and optimized marketing campaigns. With the data that can be easily used for analysis, businesses can be more proactive and make sound decisions based on past data.

Additionally, predictive analytics save on both time and money by allowing businesses to save on misallocated resources. The technology is also used by many industries in the decision-making process to improve operations in terms of both quality and functionality. Another use is recognizing various types of fraud and detecting and preventing any vulnerabilities.

Where does data quality come in all of this? A primary requirement of training a predictive model is historical data that meets exceptionally broad and high-quality standards. The number one enemy to the widespread, profitable use of machine learning is poor data quality.

The Role of Data Quality in Predictive Analytics

Data quality in predictive analytics means that the data must be accurate, reliable, and properly labeled. Moreover, it needs to include lots of unbiased data over the entire range of inputs for which the predictive model is to be developed. Unfortunately, most of the data today fails to meet these standards.

There are several reasons for this, including human error, overly complex process, and not having a clear understanding of what is expected. To compensate for this, data scientists cleanse the data before training the predictive model.

For many data scientists or 80% of them, this is tedious and time-consuming work. It is also a problem that they often complain about. The good news for them or all others that deal with organizational data is that building data quality in predictive analytics can help to overcome this problem. What does it take to build data quality? It takes the following:

  • Dedicating enough time to execute data quality fundamentals into the overall project plan
  • Maintaining an audit trail as training data is being prepared
  • Charging a specific individual with responsibility for data quality
  • Obtaining independent, rigorous quality assurance

By ensuring the above to build data quality, any organizational leader can leverage the power of predictive analytics to increase their bottom line and gain a competitive advantage.

by Bobby J Davidson

We love our company and we love what we do.  Check out the ‘Why Percento‘ page to learn more: Love of Technology and Business!  As the President of Percento Technologies International, I provide day-to-day leadership to the company’s senior management and I am personally involved in the strategy, business development and sales activities of the firm.

The company was founded in 1999 with the purpose of providing a one call source for organizations in need of Enterprise IT Consulting and Management.  We also provide a line of products in the boutique Cloud Server space with a touch of high-end website strategy consulting and design services.   We personalizes the IT Service experience with a team approach, working with clients from diverse sectors of industry, including energy services, financial, legal, entertainment, healthcare, hospitality, retail and general and/or corporate business.  percentotech.com/contact