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Data Science and ML

Mastering Data Quality: Strategies, Solutions, and Impact on Business Success

To stay viable in today’s constantly changing market, businesses need to be able to anticipate what comes next and react quickly. It comes down to the power they have over data. In exchange for personalized services, customers are willing to share their data. 

But businesses cannot afford to assume that the data collected is trustworthy. 

Poor quality data could be the result of honest typographic errors or a fraudster trying to impersonate a customer. Hence, the need to prioritize data quality. 

Understanding Data Quality 

Data quality can be assessed by its relevance to a user’s specific needs. Hence, data that is categorized as good quality for one use may not necessarily meet quality standards for other models. 

For example, when looking at profit margins for a store, you only need sales numbers for that particular store. 

However, if you needed to calculate profit margins for the brand across all outlets in the city, the same data would be considered incomplete. 

That said, there are certain facets of data quality that can be used to distinguish between good and poor data quality. To be considered high-quality, data must be:

  • Accurate – It must reflect real-life entities
  • Complete – All the required information must be present
  • Consistent – Values between datasets must correspond to each other
  • Timely – The data must be up to date
  • Valid – The values must be consistent across domains, formats and types
  • Unique – An entity should exist only once in a data set.

The Growing Relevance of Data Quality

From the retail sector to banking and more, businesses are relying on Artificial Intelligence (AI) and Machine Learning (ML) models to make data-driven decisions. This decides product recommendations, pricing, route optimization and so on. 

77% of businesses surveyed said they were already using AI or exploring its uses. Leveraging this technology has helped companies increase revenue while simultaneously reducing costs. But, the model results are only as good as the data fed into them. 

In a talk on model accuracy, British-American computer scientist, Andrew Ng showed how model accuracy is dependent more on data quality than the algorithm. 

While data has the potential to raise profits, bad-quality data can be an expensive mistake. The annual cost of bad data for business has been approximated at about $15 million. Then there’s the increased exposure to fines and penalties. Citigroup Inc. was fined $400 million in 2020 for deficiencies and operational lapses in their data governance strategies. 

It’s not just the financial losses, decisions based on poor-quality data could be life-threatening too. Take a hospital or example. Mis-managed patient records could result in a wrong diagnosis or the administration of wrong medicines. Such consequences are severe for businesses as well as their clients. 

Acknowledging the importance of maintaining data quality brings us to the next challenge – improving data quality.

Data Quality Solutions

Improving data quality is a multi-step process that must be seen as an ongoing exercise. It includes:

Data Profiling

Businesses collect data from multiple sources. Some information comes directly from customers when they create accounts or complete surveys. Others are generated by systems during transactions. 

Data profiling refers to understanding data collected from each source and reviewing its quality standards. This is the foundation for further data quality improvement processes. Once profiled, data professionals should understand what the data set is about. 

Data profiling summarizes key metadata and identifies inaccuracies, inconsistencies and missing fields within the data set. 

De-siloing databases

With every interaction, customers generate fresh data for a business. Since the data collection channel and intended use differ, it is easy for such data to be siloed in different departmental databases. 

Siloed data increases the risk of duplicates and lowers overall data quality. Hence, a standardized format must be adopted and data from all sources must be pooled in a central database. 

Doing this may involve transforming data from one format to another. For example, in the case where a business has an international audience, the order values may need to be converted from one currency to another to make them comparable. 

Cleansing and enrichment

All incoming data must be verified to meet data quality standards. This becomes easier with automated tools that compare data to reliable third-party databases. These tools should also be able to make corrections to improve data quality. 

For example, when verifying a street address, it should be able to append a missing PIN code to complete the address. Similarly, deduplication can also be automated. 

In other cases, it should highlight the issue so that it can be manually corrected. For example, let’s say a customer entered the house number as ’41’ on a street that has only 30 houses. This is a typographical error. 

Address verification tools may highlight the address as non-deliverable and give the customer a chance to correct it to ’14’. 

Quality maintenance 

Even if the data meets high-quality standards at the time of collection, it may decay with time. The city may change street names, a product variant may be discontinued, etc. Hence, the database must be regularly processed by data verification and validation tools. 

Taking responsibility

The responsibility to improve data quality must be shared by all data users as well as the IT teams. However, to streamline processes certain roles must be designated. This includes data quality managers, data scientists, data analysts and data engineers. This minimizes ambiguity and creates sustainable structures. 

Summing it up

9 out of 10 respondents to a survey testified that improving data quality led to better customer experiences. In turn, this means higher profits for the company, a stronger brand reputation and a loyal customer base. It is no surprise to see businesses realizing the need to prioritize data quality. 

Given the amount of data generated and collected manual verification is next to impossible. To maintain a high-quality database, you need the right tools. Thankfully, there are automated data quality tools available that can be integrated at data collection points. Don’t procrastinate, take a closer look at your data quality today. 

The post Mastering Data Quality: Strategies, Solutions, and Impact on Business Success appeared first on Datafloq.

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