In the Information Age, the industrial world is rapidly creating massive amounts of data each and every day. With data becoming the lifeblood of businesses, simply managing these enormous amounts of data is no longer enough for making decisions to run a profit-making business. Using inaccurate data is as dangerous as sloppy data management in any organization’s environment. Thereby, ensuring that the data used by organizations is not erroneous is emerging as an important aspect of data management for the Information Age.
Organizations, not only in the IT industry but also in non-IT areas, are becoming aware of the fact that incomplete and low-quality data can seriously impede their growth. However, verifying the accuracy, reliability, and high quality of data is becoming more and more challenging with the burgeoning amounts of data flowing through various business processes. Businesses across industries will have to understand the importance of and discover new models of implementing data quality management protocols in order to ensure efficiency in data management practices.
Why Data Quality Management Cannot be Divorced from Data Analytics
In today’s world, most industries are increasingly giving immense importance to decisions and strategies that are derived from data analytics and data-driven insights. While a lot of important organizational decisions are made with the processing of data, the accuracy, completeness, and reliability of that data has become of paramount importance to companies. There are four prominent reasons why data quality management is emerging as one of the most crucial factors involved in data analytics and business intelligence.
Reduction in Wastage of Company’s Resources
If data used by any company is inaccurate and erroneous, then it would certainly translate into various resources including money, manpower, and time, being spent inefficiently. Incorrect data can mislead organizations in wrong directions, impeding from achieving significant growth. Thereby, businesses must ensure that they are making decisions based on high-quality data along with business intelligence through maintenance as well as data management, in order to get higher quality leads for the company.
Good Quality Data = High Quality Leads
Almost all the business activities today involve a lot of data-driven decisions, and with the low-quality data being used, the decisions are bound to reflect in a significant loss. For instance, any marketing campaign is highly likely to collapse if even 1% of the data about target audience is flawed. If the consumer data is correct and flawless, then that would indicate a better chance of achieving a higher conversion rate. Thereby, good quality data can fetch a company much higher quality of leads, which makes data quality management of utmost importance.
Raw Data is Converted into Meaningful Data
In the business world that is driven by data analytics, a massive collection of raw data is as good as being futile. Businesses are increasingly expressing their interest in data that offers them specific and useful insights that indicate future growth prospects of the bizz world and the market. Through data quality management protocol, businesses can enable implementing data analytics to extract insights that can facilitate them to tap on profitable opportunities.
Gaining an Edge over Competition in the Market
In order to maintain an image of a world-class business, having just a high-quality product is not enough; businesses must confirm the maintenance of high-quality data. Low-quality data is most likely to result in consumer dissatisfaction, while it can damage the reliability of various insights and reflect in consumers’ distrust in the company. Thereby, in order to gain a competitive advantage in today’s highly competitive market scenarios, it is becoming a priority for enterprises and manufacturers to manage data quality efficiently.
How to Efficiently Implement Data Quality Management Practices
Data Quality Management is a critical process involved in data analytics and a variety of tools, methods, processes, as well as technologies can be employed to make sure that the data quality is up to the mark, syncing with specific business requirements. Despite being an extremely extensive function, data quality management can have far-reaching results when implemented continually, reliably, and consistently.
Poor data model design is one of the leading reasons why most companies face various data quality issues. Taking into consideration the importance of maintaining high data quality, many companies are implementing data quality management protocols that can either be reactive or proactive. Most mature companies usually possess frameworks that can effectively predict and anticipate data issues that may arise in the future, and this enables them to implement data quality management models proactively.
The most important aspect of data quality management is commencing the process with a solid data design and a competent team of data architects to implement the design. A majority of businesses today prefer to make system architects and data modelers through business intelligence companies a part of their teams in order to integrate their applications, while most fail to understand the importance of data architects. They can easily understand the consequences of poorly designed data models, which leads them to ensure the fitness of data for its efficient use.
Don’t Misconstrue the Value of High-quality Data and Cost of Low-quality Data
In the Information Age, companies need to understand the rule of thumb about data – the cost of maintaining the data quality is much lower than the cost of using poor-quality data. Companies must ensure implementing tasks for reactive data quality maintenance so that adequate high-quality data is available for specific business requirements.
Data quality management is a broad subject that covers various business activities including data integration, data synchronization, data architecture, data stewardship, entity resolution, data governance and security, as well as reference data management. Data quality management is not only a broad type, but it is also very diverse. A variety of techniques can be used for designing high-quality data models in order to ensure efficient management of data quality in any organization. Right from defining the impact of poor data quality, rules, metrics, and data standards, to implementing and monitoring those data management standards, all the processes are included in the tasks that are performed for maintaining data quality management.
Ultimately, data quality management is a combination of tools, processes, and people. When people working on these tasks understand the right processes and correct tools that are used for maintaining high data quality, a flawless process of data quality management can be implemented efficiently.