data quality management

How to Overcome 4 Common Pitfalls of Data Quality Management

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Organizations today have access to more data than ever before. However, cross-industry studies show that less than 1% of unstructured data is analyzed or used at all. Moreover, data analysts spend roughly 80% of their time searching for and preparing data. Many data analysts lament this fact. What’s more, they point to it as a reason for poor data quality management.

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The inability of an organization’s data analysis team to access the right data combined with inaccurate data can lead to poor business decisions. According to the IDC, by the year 2020, up to 1.7 megabytes of new information will be created every second for every human being on the planet. This means that sorting and filtering the wheat from the chaff will be essential for data-driven decisions.

However, business intelligence consultants warn that the gathering and filtering of accurate and necessary data remains a substantial challenge for a variety of reasons.




#1. Data Is Diverse

Organizations today have a huge range of sources from which to pull data. These sources include the Internet as well as experiential and observational data. Different types of data originate from unstructured data such as documents, videos, and audio. Unstructured data accounts for more than 80% of online information. Moreover, there are also semi-structured data sources, such as software packages, modules, spreadsheets, and financial reports. But there is also structured data. This includes strings, numbers, and dates.

When data originates from so many different sources and can take on so many forms, the chances of inconsistencies increase. It is possible to manage small volumes of data with manual searches or programming, such as ETL, to ensure higher quality. But these solutions are not scalable.


#2. The Timeliness of Data Is Fleeting

The second challenge beyond the diversity of data is its ephemeral quality. In other words, it tends to be relevant for only a short time. When you combine the fleeting nature of data with its volume, you find that it becomes even more of a challenge to determine its quality. What’s more, data managers must first convert unstructured data into structured data before anyone can determine its quality. This process takes time, during which the data can easily and quickly become irrelevant.


#3. There Are No Universal Standards for Data Management

With data originating from multiple sources, it becomes difficult to standardize. Additionally, data quality is in the eye of the beholder. In this case this means that the definition of data quality depends on the business environment.

Formerly, those who consumed data were usually producers, whether directly or indirectly. This ensured data quality. This is no longer true, however. Since universal standards for data are nonexistent, it is vital for organizations to create their own process for management. This includes a data governance board.

The role of a data governance board is to continuously process data quality at all levels of the organization. Moreover, it needs to separate relevant and necessary data from useless data by defining and enforcing procedures for data quality. For example, the board might decide on standards for data. However, it also needs to know how these standards would be defined in the database. Additionally, it should know how those standards would be monitored and enforced.

A data governance board also creates the opportunity to delegate roles and responsibilities that ensure the data quality is aligned with the organization’s business needs. This is important for growth and profitability. It is also essential for detecting and limiting fraud and other security issues.


#4. Data Needs Validation

As a result of the timeliness, diversity, and non-standardization of data, data is often incomplete, inaccurate, or irrelevant. What’s more, data is often repurposed. This means that the same data sets are shared in different contexts. Repurposing gives the same data different meanings, depending on the context.

Therefore, validating or correcting the data becomes difficult. That’s because it needs to be corrected in a consistent manner, which might include compromising data quality. Data rejuvenation, or extending the lifetime of historical information, is another common culprit. Before extracting new insights from rejuvenated data, the data should be properly integrated into newer data sets.

Data quality programs that alleviate these bad data challenges include data cleansing. This helps enforce data standardization. In addition, data profiling, or monitoring and cleansing data, can validate it and reveal relationships between data sets that allow organizations to find data inconsistencies.


Developing a Data Quality Management Strategy

With these significant challenges, organizations will find that they receive an excess of bad data. This can occur through legacy systems, third-party data providers, external applications, and social media channels. As a consequence, organizations must develop a data quality management strategy.

The first step is a data quality assessment to determine the quality and accuracy of an organization’s data. Then the organization can adopt the right data quality management strategy to fit its needs. Additionally, the organization can adopt a data governance policy that enforces requirements to meet those business needs.


On a Final Note

In the digital age, data is a key component of any organization. Moreover, analysts must properly manage data so that it benefits the entire company.

Successful data quality management can positively influence the entire organization. It is therefore critical that organizations understand the pitfalls that confront them when developing a data quality management strategy. Only then can organizations develop a method to properly confront these hurdles and enable themselves to make better data-driven decisions.