What is Data Quality and What Are Its Dimensions and Characteristics, How Can It Be Improved?

 The modern world is awash in data. Because data is information, information is knowledge, and knowledge is power, data has evolved into a type of modern currency, a valuable commodity traded between parties.


People and companies may use data to make better decisions, boosting their chances of success. By many accounts, this suggests that having a lot of data is a positive thing. That isn't always the case, though. Sometimes data is missing, erroneous, duplicated, or irrelevant to the user's requirements.

But, thankfully, we have the concept of data quality to aid us in our efforts. So let's take a look at what data quality is, what its characteristics and best practices are, and how we can utilize it to improve data.


What Is Data Quality and How Is It Defined?


In simple terms, data quality indicates how trustworthy a set of data is and whether or not it is suitable for use in decision-making by a user. This attribute is frequently graded on a scale of one to ten.


But, in practical terms, what is data quality?


Data quality refers to how relevant data is for a certain purpose, as well as its completeness, correctness, timeliness (i.e., is it up to date? ), consistency, validity, and uniqueness.

Data quality analysts are in charge of doing data quality evaluations, which entail evaluating and interpreting each quality data measure. The analyst then calculates an aggregate score for the data's overall quality and assigns a percentage grade to the company based on how accurate the data is.

To put it another way, data quality refers to the quality of the data and how valuable it is for the task at hand. However, the phrase also refers to the activities of planning, implementing, and regulating the necessary quality management procedures and methodologies to ensure that the data is actionable and valuable to the data consumers.


Dimensions of Data Quality

Data quality is divided into six basics, or core, characteristics. These are the parameters that analysts use to assess the data's feasibility and utility to those who require it.


Accuracy

The data must reflect real-world items and occurrences and must correspond to true, real-world scenarios. To check the measure of correctness, analysts should employ verifiable sources, which are defined by how closely the data match the verified correct information sources.


Completeness

Completeness assesses the data's capacity to correctly offer all of the mandatory values.


Consistency

The uniformity of data as it flows between applications and networks, as well as as it comes from numerous sources, is referred to as data consistency. Consistency also implies that the same datasets kept in several locations should be identical and should not contradict. It's important to remember that even if data is consistent, it might still be incorrect.


Timeliness

Data that is easily available whenever it is needed is referred to as timely data. This dimension also includes keeping data current; data should be updated in real-time to ensure that it is always available.


Uniqueness

The term "uniqueness" refers to the absence of duplications or redundant information across all datasets. There are no duplicate records in the dataset.


Validity

Data must be collected in accordance with the business rules and parameters established by the company. All dataset values should be within the proper range, and the data should follow the correct, accepted forms.


How Can Data Quality Be Improved?

Data quality management is a good place to seek ideas on how to improve data quality. Data quality management strives to use a balanced mix of solutions to avoid future data quality concerns and clean (and, ideally, delete) data that does not meet data quality KPIs (Key Performance Indicators). These acts assist firms in achieving their current and future goals.  


Data quality entails more than just data cleaning. With that in mind, here are the eight required disciplines for preventing data quality issues and improving data quality by removing all faulty data:


Data Management

Data governance establishes the data regulations and standards that set the required data quality KPIs and the data items that should be prioritized. The business principles that must be followed to assure data quality are also included in these standards.


Profiling of data

Data profiling is a technique for identifying and understanding all data assets in the context of data quality management. Because many of the assets in question have been populated by data, data profiling is essential.


Data Reconciliation

Match codes are used to assess if two or more bits of data describe the same real-world entity in data matching technologies. Let's say you know a man named Michael Jones. Mike Jones, Mickey Jones, Jonesy, Big Mike Jones, and Michael Jones may all have separate entries in a customer dataset, yet they all refer to the same person.


Reporting on Data Quality

Data quality KPIs can be measured using information acquired via data profiling and data matching. Reporting also includes maintaining a quality issue record, which tracks known data problems as well as any subsequent data cleansing and preventative activities.


Master Data Management (MDM)

Frameworks are excellent resources for preventing data quality problems. Product master data, location master data, and party master data are all dealt with by MDM frameworks.


Integration of Customer Data (CDI)

Customer master data is compiled using CRM applications and self-service registration sites as part of CDI. This data needs to be consolidated into a single source of truth.


Product Information Management (PIM) 

Requires manufacturers and sellers of goods to synchronize their data quality KPIs so that when customers order a product, it is the same thing throughout the supply chain.


Management of Digital Assets (DAM)

Digital assets include videos, text documents, photos, and other files that are utilized in conjunction with product data. This discipline entails ensuring that all tags are relevant and that the digital assets are of good quality.


Best Practices for Data Quality

To reach their goals, data analysts who want to increase data quality must follow best practices. The following are ten important best practices to follow:


  • Make certain that senior management is involved. Through cross-departmental collaboration, data analysts can overcome numerous data quality challenges.

  • Assemble a data governance system that includes data quality activity management. The framework establishes data regulations and standards, as well as the necessary responsibilities and a business vocabulary.

  • A root cause analysis must be conducted for each data quality concern raised. If you don't address the source of a data problem, it will inevitably resurface. You must not only treat the disease's symptoms; you must also treat the sickness itself.

  • Keep a track of data quality issues. Each issue requires its own entry, which includes information about the assigned data owner, the participating data steward, the issue's impact, the ultimate resolution, and the date of any required processes.

  • Fill data owner and data steward jobs from your company's business side as much as possible, and data custodian roles from either business or IT as much as possible.

  • To promote awareness about the importance of data quality, use examples of data quality disasters. While anecdotes are useful for illustration, you should use fact-based effect and risk analyses to explain your solutions and the money they demand.

  • The cornerstone for metadata management must be your company's business lexicon.

  • If at all feasible, avoid typing in data. Instead, look into cost-effective data onboarding solutions that use publicly available data from third-party data sources. Names, general localities, firm addresses and IDs, and, in certain situations, individual people are included in this data. 

  • Rather than relying on downstream data purification to resolve data issues, make every attempt to build applicable processes and technologies to prevent problems from occurring as close to the data onboarding point as possible.

  • Create data quality KPIs that work in conjunction with overall business performance KPIs. KPIs for data quality, also known as Data Quality Indicators (DQIs), are frequently linked to data quality characteristics such as uniqueness, completeness, and consistency.


Are you interested in working as a data analyst?

The greater the demand for data analysts, the more data our world generates. Learnbay’s data science training in delhi or data analyst course in delhi  can turn you become a data analytics specialist. This IBM-sponsored Data Analyst certification course teaches you vital skills like how to deal with SQL databases, construct data visualizations, R and Python programming languages, analytics tools and techniques, and how to apply statistics and predictive analytics in a business setting.

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