Why Data Quality is important in business?

Data can transform into knowledge and knowledge is power. Business Intelligence tools can help you with useful insights and decision-making processes, but before using BI tools, you have to gather data. Data quality is the most important factor before analyzing and producing results. Data must be clean and accurate. Data has become a currency, a valued commodity. As an entrepreneur, you have to make sure that your gathered data should be on accurate, consistent, reliable, updated, and complete. Data quality in business operations has increased as data become the key for future technology. From Artificial intelligence to the Internet of Things, everything is getting depends on the data.

Characteristics of data quality:

· Accuracy: When data can represent by a real-life object, data accuracy increases. When the data is error-free and can be used as a reliable source of information. Many factors can affect the accuracy of the data. Poor Data Culture can be considered the most common reason for data inaccuracy. Businesses invest in technologies but forget about data awareness training. Data hoarding is the collection of huge data and retention of data. Businesses are spending millions on big data technology. around 2.5 quintillion bytes of data are produced every day. It is an extremely difficult and expensive task to clean, sort, and manage the data. Many businesses are still dependent on outdated technology. Applications like MS Excel or ETL tools are still used in many businesses. These manual applications are incapable of handling modern unstructured data.

· Consistency: Consistent data is another major characteristic of data quality. The data‘s uniformity as it moves across different applications or when it is collected from multiple sources. You need to be consistent in your gathered data. Range, variance, or standard deviation are examples of consistency metrics. Identical datasets should be stored with the same name or same reference if you want to store them in different locations.

· Completeness: Records must be complete for a high data-quality structure. You have to analyze data quality for any missing or incomplete values. The data element must have a value, it is known as an assertive mandatory value assignment. By applying a method for a data element to have or not have a value under specifically described conditions.

· Timelessness: It is the measured time between the data is expected and available for use. You have to keep the data updated. You have to make sure that the gathered data is always available and accessible. Tracking data is the solution for maintaining the data quality, and especially timelessness. Time Variance can be considered a metric of timelessness.

· Uniqueness: Uniqueness means that there should be no duplicity and redundancy in data. No records should be present multiple times in the datasets. Analysts use data-cleaning processes to reduce duplicity and redundancy in datasets. For example, assume that we have two people in our dataset whose names are Michael Anderson and Michele Anderson, in reality, they are the same person but somehow registered as different people. Then the risk of accessing wrong or outdated data will occur.

How do you improve Data Quality:

There are many ways to improve data quality. There is more to data quality rather than just cleaning the data. Some disciplines used to improve data quality are as follows

· Data Governance: Data governance can be defined as data policies & standards that determine who has the right to view and manipulate data. In simple words, the oversight of an organization’s information falls under the dimension of Data Governance. It establishes standards that how the data is collected, stored, and manipulated for specific uses. Data Governance encompasses several components, these components are generally formalized in a data standard. Some ideal Data Governance includes the objectives of the organizations, identifying the scope of data covered, designating an accountable position, bringing clarity on data ownership rights, articulating how data collection is handled, and describing how relevant data sets and data streams should be accessed and shared.

· Data Profiling: Data Profiling is a process of analyzing data. Reviewing the source data, and understanding the structure of the data, content, and its relationships fall under Data Profiling. So you can understand how your data is relevant and useful, and how the data quality can be improved. The structure of the data with its characteristics is where you can start your data profiling process. You should look at large outliers, anomalous clusters, and data types to find whether your data’s structure is correct or not. If your date is showing in numbers then you should correct this or the salary column must be in numerical format.

· Data Matching: Data Matching is a technology or methodology in which match code is used to determine if two or more bits of data described the same real-world objects or not. Data Matching methodologies analyze the duplication in the single data source. It helps you to eliminate those values that are the same in the data. It determines the correct value and reduces the error in the data. It identifies both exact and approximate matches and helps you in removing duplicate values in data. The process of data matching is simple. First, you have to create a matching policy in the specific dataset and then perform a de-duplication process, which will fix the data duplication problems.

· Data Quality Reporting: After Data Profiling and Matching, the gathered information is used to improve data quality. Reporting also involves operating a quality issue log, which documents known data issues and any follow-up data cleansing and prevention efforts. Data quality finds it useful to operate a data quality dashboard highlighting the data quality KPIs, the trend in their measurements, and the trend in issues going through the data quality issue log.

· Master Data Management (MDM): Master Data Management frameworks are one of the best resources for preventing data quality issues. It deals with product master data, location master data, and party master data. Product Master Data Management (PMDM) is a system by which businesses organize and store data about all the key attributes of their suite of products. Location Master Data Management ( MDM) helps you in managing, maintaining, updating, and sharing location and siting master data across multiple channels & applications.

· Customer Data Integration (CDI): CDI is a process of compiling customer master data gathered via a range of applications. This information must be compiled into one source. These applications include self-service registration sites, Customer Relationship Management (CRM), ERP, customer service, and many more.

· Product Information Management (PIM): Businesses need to align their data quality KPIs with each other so that when clients or customers order a product or service, it will be the same in the supply chain. PIM involves creating a standardized way to receive and present product data. This is used for ensuring data completeness and other data quality dimensions within the product data syndication processes.

· Digital Asset Management (DAM): Videos, text documents, images, and all similar products fall under DAM. DAM ensures that all tags are relevant and the product must be a high-quality product.

Data Quality Best Practices

For improving data quality, you need to follow best practices. Here are the ten most used and best practices to follow.

· Lot of data quality issues are only solved by having cross-departmental participation or view. You have to ensure the involvement of top-level management in data quality.

· Managing data quality activities as an integral part of a data governance framework. Data policies, data standards, and provide a business glossary should be set by the framework.

· Each data quality issue must be handled with proper analysis. If issues are not handled properly then problems will appear again. Taking roles as data owners and data stewards from the business side of the organization.

· Issues need proper entry and must be with complete information, the timing of any necessary procedures, and the impacts of issues. You should use a business glossary as the foundation of metadata management.

· Assign a data owner and data steward roles from your organization to operate a data quality issue log with an entry for each issue with information. Assign data custodian roles from business whenever possible.

· Always address the root cause of the issue while analyzing data. You should rely on risk analysis and real-life fact-based impact to address your solutions.

· You should always strive to implement processes, methodologies, and technologies that prevent issues from occurring.

· Data Quality KPIs can be related to data quality dimensions. General KPIs should be linked to data quality KPIs. When dealing with product data, if possible use other-party data from trading partners.

· You should make every effort to implement every possible and relevant process and technology to stop the issues from occurring while resolving the data issues.