Data Quality Management - What Is It?

What is Data Quality?

The extent to which a set of data is precise and useful can be determined through its data quality. The precise meaning of data excellence will shift depending on the data forms being utilized and for what purpose they are being employed. Any business that collects data needs to make sure it is accurate, reliable, and beneficial.

So, what does data quality measure? Ensuring that your data is high quality means you can be more certain it meets the following requirements:

Accuracy – is the data correct in its entirety and is each entry unique? Are there mistakes and/or duplicate data?
Completeness – is all of the necessary data available or is some of it missing?
Fitness for Use – does the data provide the information needed for its purpose?
Reliability – is the data trustworthy and consistent? How does it compare to other reliable data?
Timeliness – is the data up-to-date?

The quality of a dataset is guided by five interconnected factors. If the information is not precise, it will not be suitable for utilization. If the information is not complete, it may not be dependable.

High-quality data is vital for businesses. Data is instrumental to making business judgments of all sorts- ranging from identifying the products to release each week to considering revamping your e-commerce site. The Experian study states that more than half of company owners are emphasizing on upgrading the accuracy of their data which is more than emphasizing any other matter relating to data management.

Types of Data Quality

Let’s explore further the varied types of data quality. The five components noted earlier (which are accuracy, completeness, fitness for use, reliability, and timeliness) that influence the quality of data can be categorized into three groups, facilitating a more in-depth comprehension of how they each factor into a data set’s quality. These are intrinsic, contextual, and representational data.

1. Intrinsic data quality.

A paper authored by Richard Wang and Diane M. was the focus of the study. The authors, with Strong as the director of MIT’s data program and professor of data science, maintain that intrinsic data quality means that the data is of high quality in its own right. Wang and Strong emphasize the importance of data being precise, believable, trustworthy, and impartial.

In summary, intrinsic data quality is information that is excellent in and of itself, independent from considerations such as when it was acquired or how practical it is.

An ecommerce company like Bliss World is getting ready for their upcoming marketing initiative. Bliss World utilizes their client information to acquire the email locations of previous purchasers. This information is stowed away properly and for the ad to be sent to buyers’ inboxes adequately, there must not be any fundamental issues with the quality of the data.

2. Contextual data quality.

It is vital to have a strong set of data, however, it is also necessary to keep in mind the circumstances surrounding the data. It is essential that the accuracy of customer email address data is prioritized over the context, however, if the data will be used to evaluate sales from the past quarter, ecommerce companies must take into consideration its context.

For what purpose is the data being gathered and how thorough is it? The importance of these aspects- thoroughness, promptness, and usefulness for a certain purpose- must be evaluated.

In order for the data to be of a high standard, it must be applicable to the individual utilizing it. Even if the information is precise, if it was collected too far back or is not full, it will not be of much help to, say, a company hoping to get the scoop on recent customer habits.

Bliss World has just acquired a Voice-over-Internet Protocol (VoIP) headset for all personnel in their customer service center. They are interested in determining whether customers are more content following their shopping. For this to happen, Bliss World requires fresh information for it to stay applicable. The customer satisfaction data will be useless if it is from before the purchase of the headsets, even if the data in itself is excellent.

3. Representational data quality.

The quality of the representation of the data indicates the structure of the data and how simple it is to comprehend. It is absolutely necessary to furnish data to those who use it, whether they are clients, workers, allies, or shareholders.

Bliss World has gathered data from their call center to find out if customer contentment has increased since they bought VoIP headsets. Once the data has been gathered, it should be simple to detect the disparity in customer contentment earlier and later in the acquisition. If individuals who are analyzing the data do not understand it, the data does not accurately represent anything.

Data Quality Dimensions

The measures used to assess data quality are outlined in the data quality dimensions. If you wish to determine the caliber of your data, it is imperative to have measurable metrics established.

1. Data integrity.

It is assessing if the data has been acquired, retained, and distributed in an unbiased manner. This indicates that those managing the data have done so without bias, in a responsible manner, and that their procedures are obvious to those using the data.

Skullcandy, a business in the technology ecommerce industry, is considering whether or not it would be a good idea to invest in improved business communication systems. In order to achieve this, they need to evaluate employees’ information, like an employee satisfaction study, to make the best selection. For Skullcandy to make a wise decision, it is essential that the gathered information is accurate and has been gathered in an unbiased way.

2. Methodological soundness.

The quality of the data is assessed by evaluating if the methodology used to gather, save, and circulate it was correctly implemented. This is about whether the strategies employed abide by the customary practices and standards of data accuracy for gathering this data.

Skullcandy wants to identify the age group they should target, and they may utilize customer information to ascertain the average age of their consumers.

A dataset that has been well-thought-out and organized could be created by having online customers input their birthdates when they complete their purchases. It would not be appropriate to use the place of residence of a customer as a basis for determining their age.

3. Precision and consistency.

The accuracy and uniformity of a set of data points refer to how reliable and consistent the data is throughout all datasets.

Skullcandy has both an electronic payment system and a Customer Relationship Management platform that gathers customer data. You can determine the accuracy and consistency of data for these systems by analyzing if the customer information is precise (eg. names typed correctly) and consistent (eg. names written the same way in both systems).

4. Serviceability and relevance.

It is important to consider whether the data elements are meaningful and practical in order to assess data quality. This relates to qualities like timeliness and usefulness.

Skullcandy is looking to find out the average length of their customer service calls. If the information that was accumulated ten years ago or is limited to a solitary communication channel, it won’t be beneficial or practicable for Skullcandy to employ this information to reduce the handling time.

5. Accessibility of data.

What is the level of ease associated with accessing, reading, and understanding the available data? This is what data accessibility measures.

Skullcandy is looking to find out the amount of money the typical shopper spends when they shop on their website. They send products to customers worldwide, thus they manage different forms of currencies. The compilation of data, ranging from the Euro to the Yen in relation to customer transactions, will make it complicated to comprehend. Converting the information into US dollars will allow users to figure out how much money an average customer spends.

Data quality team: roles and responsibilities

Data governance encompasses the practice of overseeing data in order to maximize its potential benefit. Data quality is a key element of this, with the aim of managing data in a way that can most effectively be utilized. The role of a Chief Data Officer (CDO) is to oversee the data usage and governance of the entire company. The Chief Data Officer must assemble a group devoted to ensuring the quality of data.

The amount of people needed in a data quality team is contingent upon the size of the company and the amount of data they oversee. Typically, a data quality team consists of individuals who have both technical expertise and business knowledge. Possible roles include:

The individual responsible for the data is responsible for ensuring the accuracy of a dataset or multiple datasets, as well as specifying the standards for data quality. Senior members of the team from the business side usually take on the responsibility of data owners.

Someone who regularly uses data and sets out the parameters for how it should be used, along with identifying and reporting any issues to their team, is known as a Data Consumer.

Someone who collects data and ensures it meets the quality standards desired by those who will be using it.

A data steward generally has the responsibility of managing data content, understanding the meaning of the data, and ensuring that pertinent business regulations are being followed. The specialist guarantees that staff members adhere to the predetermined principles and regulations regarding the production, access, and utilization of info and data. A data steward can offer advice on ways to enhance existing data governance systems, as well as assist a data custodian in their duties.

The data custodian oversees the technical aspects of maintaining and storing data. The data custodian guarantees that data remains of high quality, trustworthy, and secure throughout the process of extracting, modifying, and loading it. Job positions for data custodians may include data modeler, database administrator, and ETL (Extract-Transform-Load) developer as outlined in our article.

An individual in the role of a data analyst examines, evaluates, and compiles information and then provides a summary for those with a vested interest in the results.

As a data expert is a big part of the data quality teams, let’s discuss what responsibilities they have.

Data quality analyst: a multitasker

The data quality analyst’s duties may vary. A specialist could carry out the tasks of a data consumer, such as creating standards and documentation for data and making sure the data is of good quality prior to loading it into a data warehouse, which is typically done by a data custodian. According to the analysis of job postings by an associate professor at the University of Arkansas at Little Rock Elizabeth Pierce and job descriptions, we found online, the data quality analyst responsibilities may include:

Monitoring and reviewing the quality (accuracy, integrity) of data that users enter into company systems, data that are extracted, transformed, and loaded into a data warehouse
Identifying the root cause of data issues and solving them
Measuring and reporting to management on data quality assessment results and ongoing data quality improvement
Establishing and oversight of service level agreements, communication protocols with data suppliers, and data quality assurance policies and procedures
Documenting the ROI of data quality activities.

Firms may involve the data quality analyst in organizing and administering education on data quality, and advise on measures that can be taken to increase data accuracy. The specialist may also be responsible for making sure that the company follows data privacy regulations.

It is your responsibility to delegate tasks to the data quality group. An essential component for any team is to have an individual who oversees the whole operation, making sure that quality control is up to standard, manages the data standard regulations, creates data designs, and a tech guru who makes sure data is successfully sent and stored throughout the firm.

IBM InfoSphere Information Server for Data Quality: end-to-end for ongoing data monitoring and cleansing

IBM InfoSphere Information Server for Data Quality is one of the four top-quality data products that the vendor provides. This system allows for the automatic tracking of data and enables users to customize the cleaning of the data either in batches or in real-time. The program pinpoints data accuracy issues and devises a plan to make corrective corrections depending on criteria that are fitted to the user’s professional targets. So, companies can define their own data quality rules.

IBM InfoSphere Information Server for High-Quality overview

The tool’s core features include:

Data profiling
DQ transformations: cleansing, matching, validation (i.e., flexible output table configuration for data validation rules, sequencing and impact analysis)
Customizable data standardization (i.e., data enrichment and data cleansing)
Data lineage maintenance — users can see what changes were made to data during its lifecycle
Data integration
Data classification (i.e., identifies the type of data contained in a column using three dozen predefined customized data classes)
Data quality assessment and cleansing activities within a Hadoop cluster

FlexPoint licensing allows customers to gain access to IBM’s Unified Governance and Integration Platform in a more manageable and adjustable manner.

The answer can be executed either in an on-site environment or through an online platform. Pricing is available on demand. IBM provides instructional information (i.e. videos, interactive demonstrations, and e-books) to inform people about what their solution is capable of.