Getting to know DATA QUALITY TOOLS

“Data Quality Tools are software solutions that combine processes like profiling, cleaning, enrichment and parsing with an interactive interface, embedded workflow, and knowledge base to help organizations with data management, decision making, and operation improvement.”

Before we explore data quality tools and their features, it is prudent to understand a few aspects of data quality and its effects on business awareness, analytics and strategy creation.

So, why is the quality of data so important?

Data Quality Management is a business strategy that has risen in popularity since organizations had the realization that sub-standard data quality was affecting their overall business performance improvement. The emergence of various data compilation processes, data landscapes and data sources has made data chaotic and erratic. While data preparation aims at transforming data to easily manageable formats, it cannot account for the trustworthiness of quality of the data.

“Data quality” refers to how accurate, consistent, unique and relevant the data is to a particular analytical approach. The more correct and relevant the data is, the higher its quality. It goes without saying that when data lacks consistency, coherence and correctness, the results of analysis based on it are likely to be unactionable and even incorrect.
For example, if the data collected is from an untrusted source, there are high chances that the data is incorrect or falsified. If an organization utilizes this data for analysis without verifying the source, their analysis will be based on false notions and thus, be rendered useless. A data quality tool, on the other hand, will verify sources and eliminate any data that does not qualify pre-set parameters, saving the organization time and effort spent on analyzing incorrect data.

A major influencer of analytical outcomes, data quality improvement has been a top priority for enterprises. Data quality tools help teams create effective quality management approaches, and optimize data collected from external sources.

How is data quality decided?

Generally speaking, the quality of data can be determined by evaluating it against numerous dimensions. Some of these dimensions are listed here –

  • Believability
  • Credibility
  • Accuracy
  • Completeness
  • Timeliness
  • Consistency
  • Accessibility
  • Value addition
  • Relevancy

Data Quality Tools – How do they work?

Data quality tools function by automating certain processes that make data match the standards required by analytics tools. Some of these processes are –

  • Data Cleansing and Validation – Eliminating errors, verifying the source, and identifying relevancy and consistency.
  • Data Profiling – Identifying relationships between values and the frequency of their appearance.
  • Data Parsing – Breaking a data entry into its components and identifying whether entries refer to the same or similar data concept. Parsing is helpful in identifying the same data in different formats.
    To explain with a remarkably simple example, the quantity 12 may be written in digits or as a word – twelve. Both these entries mean the same but appear different. Parsing would help identify both of these entries as the same.
  • Data Standardization – Once different forms of the same content are identified; a single format can be applied to all the entries.
    To continue with the same example, if the system preferred the use of digits (all other entries could be numerical), all entries in the alphabetical format could be changed to the numerical format.
  • Data Integration – While standardizing data, it is also important to ensure that the format chosen is in accordance with the formats used by other analytical tools. Data quality tools ensure that data can be easily integrated with data from disparate sources.

Other features of data quality management tools include data quality assessment, exception management, data quality rule builder, pre-build / customizable rating systems, metadata derivation and metadata driven machine learning.

Use cases and scope of data quality tools

Dealing with massive amounts of data, analysts and researchers have concluded that data quality tools should aim towards improving the quality of data that is generated. Merely correcting faulty data is a time-consuming and repetitive process. Hence, improving data quality at the source seems like a more reasonable approach.

Data quality tools are evolving to incorporate artificial intelligence driven optimization suggestions. This will ensure that employees can be trained to produce high-quality data and eliminate the need to assess data quality. Defining institutional regulations surrounding data can eliminate the pre-processing and qualification stages in data management. Data quality tools are also being used to identify reference points of low-quality indicators. These indicators are then used to determine what not to do while gathering and analyzing data.

Be that as it may, data quality tools will always be essential to ensure that external data measures up to the standards of quality expected by an organization. Their use cases are increasing and spreading to the realms of data governance, analytics, master data management, and business operations management