Data Quality and Impact in Management

Today, a critical element for organizations is having data from various sources that help the company move forward and to correctly define strategies.

It is well known that information is power, and its proper management allows you to know the business and prepare to meet and, if possible, exceed customer expectations, but mainly it helps you make the right decisions at the right time. Information is a super-valuable and critical asset and as such, requires care to ensure the  information you have is trustworthy.

It is also very important to understand the useful life of the information and, if it is not static, how it can be utilized  in each process of the organization. All this while also understanding the importance of protecting that information.

In addition to  securing their  information and how it is  handled in each process, companies also need to understand if the information is transformed during a process, and, lastly, when information creates a result and can be used to analyze behavior, make decisions or to predict future scenarios.

There is a very important concept related to information called Data Quality. Not all companies have a strong structure or strategy to manage the data journey into normal operations or the impacts and risks that it can represent due to the management of data.

Data Quality is the discipline that through different processes, techniques, and operations seeks to improve the quality of data in an organization. It refers to the assessment of the information you have. The quality of data is defined by different factors that are necessary to fulfill the needs of an organization in terms of operations, planning, and decision-making.

There are different views about the factors or dimensions related to Data Quality; some practices manage four, six or more factors.  In this article, I will briefly explain the main six that in my opinion could help to set up rules for your Data Quality Management system: 

Data Quality Dimensions or Factors

Completeness: Indicate if there is enough information to draw conclusions

Consistency: Specifies that two data values pulled from separate data sets should not conflict with each other.

Timeliness: Availability and accessibility, the time between when data is expected and the moment when it is readily available for use.

Uniqueness: Data uniqueness is achieved when information in the dataset only appears once.

Accuracy: Refers to business transactions or status changes as they happen in real-time.

Validity: The data values are appropriate for use in format, length, allowed values, etc.

All information has a meaning by itself, but the information set in a process can be used in different ways; nonetheless,management, controls, rules, plans, systems and even strategies are required.

Data Quality Management can be defined as a set of practices undertaken by a data manager or a data organization to maintain high quality information. The person in charge  of the data in a company must ensure the correct execution of Data Quality components.

This kind of process obviously should generate some benefits for companies, but that will depend on the company’s situation, its maturity, the involvement and priorities of upper management  and if Data Quality is part of the company’s strategy to improve its operation. Data Quality is important because companies get accurate and timely information to manage services and accountability, which help in internal operations to manage service effectiveness, to prioritize and to ensure the best use of resources.  Regarding the specific situation of each company, some of the main benefits could be:

• Improved decision-making process: The company will be able to analyze the supply chain management and all company processes to plan accurate strategies, enhancing the use of resources.  

• Save time and money: it is of utmost importance for companies to invest in the right processes, systems, and tools to ensure the quality of their data. As a result, the company will save money and resources.

• Competitive advantage: Since customers are related to marketing, sales, and finances, being able to make informed decisions with your own data is invaluable in today’s fast-paced world. Getting a clear picture of what steps you should follow to be successful will lead to gaining a clear competitive advantage that will set the organization apart from the rest.  

• Better targeting: You can know who your prospects are, which you can only manage by analyzing and understanding data, and anticipate the needs of the new opportunities and target markets.

• Effective marketing: Good data quality will help you identify what’s working and what’s not in marketing campaigns and what could be more successful or what to adjust.

• Better Customer Relations: Some customers just want to do business with brands they can trust, but if companies use information they can better know their clients and understand and meet customer expectations, leading to  loyal  fans and providing better services and products.

“Data” is an important asset for companies. It is critical to know how to manage it as well as how to use technology and innovation to exploit data and gain as many benefits  as possible in different areas. But mainly, data is important for defining strategies to ensure a competitive advantage and a better positioning in the market.