#dataqualityplatform

Data quality is an assessment or a perception of data’s fitness to fulfill its purpose. Simply put, data is said to be high quality if it satisfies the requirements of its intended purpose.

The quality of data can be measured by six dimensions: Accuracy: Data accuracy is defined as the degree with which data correctly reflects the event in question or the ‘real world’ object.

Consistency: Data is said to be consistent if all the systems across the enterprise reflect the same information.

Completeness: Data completeness is the expected comprehensiveness. Data is considered complete if it meets the expectations.

Uniqueness: Each data record should be unique, otherwise the risk of accessing outdated information increases. 

Timeliness: It references whether the data is available up to date at the time someone is attempting to use.

Validity: Data is valid if it conforms to type, format and range of its definition.

Bad data is inaccurate, unreliable, unsecured, static, uncontrolled, noncompliant, and dormant. Certainly, no one wants to go to the wall in such fashion, and businesses will work their heads off to improve the quality of their data as they seek to make good in a rather competitive digital age.

Importance of a digital solution to manage data quality

A Good Data Quality tool has to be implemented to ensure the quality of the data. DQ tools remove the errors, redundancies and other issues effectively. They tackle numerous tasks such as data mapping, ingestion, integration and metadata discovery. So identifying the right data quality management solution is vital for every business.

DQLabs, an AI augmented data quality platform that helps you to collect accurate and relevant data from various sources. It empowers organisations to detect and solve data quality issues without much human efforts.

In a word, data quality is the whole multi-faceted process of styling data to align it with the needs of business users. The quality of the data you have will be reflected in the business decisions you make both in the short run and in the long run.

Data quality will make or break your business, as the insights you get from it dictate the business moves you make. The higher the quality of data a company has in its hands, the better the results its campaign strategies are going to produce.

Better Decision Making

High-Quality data leads to better decision making across organizations. When the quality of data is high the outputs are reliable which mitigates risks and guesswork in decision making.

Productivity

Good quality data boost productivity. Workers spend more time working towards their primary mission instead of spending time validating and fixing data errors.

Effective Marketing

High-Quality data enables more accurate targeting and communications because it allows organizations to make decisions on targeting an audience.

Customer Relationships

Better data can help you to build a strong relationship with customers. More accurate data about the customer helps you to know better about them.

How do you implement data quality?

The success of an organization depends upon the quality of the data. Data quality tools do data cleansing, data integration, data ingestion, metadata management and master the data. Proper tools can significantly reduce the effort required to perform each step. Also, it enhances the accuracy of the data and processes the procedure to generate high-quality data at a source.

DQLabs.ai is the best data management platform that functions and has all the built-in features to make autonomous execution without any manual configuration.

2020 has proved to be nothing like anything the world has ever experienced before. The onset of the ongoing COVID-19 pandemic was fast spreading and taking lives, saw businesses quickly close down & others readjust their operations to cope with the new circumstances. 

Research by Experian shows that half of the organizations consider their CRM data to be unclean. Data inaccuracy has affected the financial sectors in  operational risks,  poor strategic decisions and high data costs.  Quality data is cleaned, assured, and guaranteed by data quality tools. Data quality tools have proved to be very helpful for the financial sector during the ongoing pandemic in these ways: 1. Digital Transformation:  The COVID-19 pandemic has accelerated the digital transformation of the corporate world, including the financial sector. Employees have been forced to work from home or work remotely in an attempt to contain the quickly spreading virus. The digital transformation journey cannot be achieved if controls are not in place to ensure high data accuracy and reliability. These are well assured by good data quality tools.

2. Better understanding of customers:

The financial sector has heavily relied on effectively segmenting their customer bases. This has always been fueled by the need to understand each customer segment’s value. The financial sector is always actively seeking to stay relevant to their customers and stay responsive to their ever-evolving needs. Accurate, complete, holistic, and quality data can help the sector connect more and better with their customers.

3. Enhance effective communication:

The ongoing pandemic exposed businesses, including the financial sector to managing communication during a crisis. Businesses were monitoring the situation and being forced to make quick strategic decisions. These decisions had to be communicated with their customers. This is still the case to date. Complete, clean, and up to date customer data was and continue to be crucial to the financial sector in managing communication with their customers and other stakeholders.

4. Ease compliance:

The financial sector is heavily regulated owing to the crucial customer data and finances they handle. There are many regulations affecting the sector and the players have to comply with the regulations including the Protection of Personal Information Act, among similar regulations. For compliance purposes, customer’s data needs to be of adequate quality to enhance effective decision making.