How to Improve Enterprise Data Quality? Here’s the Answer
Written by Imran Abdul Rauf
Technical Content Writer
May 28, 2022
Sales and marketing teams worldwide lose approximately $32,000 per salesperson and 550 hours through poor data usage. Plenty of other statistics highlight the importance of high quality data as demonstrated through different verticals and technology sectors worldwide.
This blog will educate you on what data quality is and give tips on how to improve it to acquire the best investment out of your data-driven decisions.
What Is Data Quality?
Data quality determines the usefulness of your data, whether it provides relevant and valuable insights, and if its utility proves beneficial for the business. Typically, focusing on accuracy, completeness, timelessness, consistency, and accessibility forms the benchmark for determining if the data is worth using. Let it be your key accounts for the sales teams, content topics for the marketing team, and email list for the lead generation team; the above aspects are necessary to consider before using the data for making informed decisions and gauging if your data governance programs are performing up to the mark.
Significance Of Driving Business Through Data Quality
Any enterprise can use the insights gathered from high quality data to:
- Streamline operations
- Optimize internal business processes
- Create and deliver excellent customer experiences
- Generate better ROI
The better the data insights, the better decisions you will make for today and future endeavors. Maintaining data integrity also translates into efficient enterprise data management and governance practices, making the business secure, reliable, and credible.
Critical Practices to Improve Data Quality
Define Business Needs and Evaluate the Impact
Business needs often determine the urgency of data quality improvement initiatives. Therefore, data professionals should understand and prioritize the data quality issues that need to be addressed according to specific business needs. The focus should be on determining if they are in alignment with the company’s objectives and how they can impact the business in the long run.
Analyzing the business impact and predefined KPIs puts the data quality management strategy on track and creates a goal for future validation. In short, the impact needs to act as a benchmark and define the context to coincide with data quality measures.
Perform Data Profiling
Data profiling refers to the process of evaluating the available data gleaned from different resources to spot deficiencies and identify suitable measures to improve its quality. For instance, data profiling can help you identify the missing information about your existing and prospective clients or customers in the customer data. Similarly, it enables you to do demographic and geographic segmentation and thus define the market potential for your business.
Acquire Data Intelligence
Not all kinds of data are equal and suited for all businesses. IT teams and decision-makers need not only the enterprise data which is correct but also the data which meets their demands for the intended use. The key to understanding your data is answering where your data comes from, what material and value it provides, and how business owners plan to use it.
Data intelligence is the proper term here, which shows how to adequately explain and connect the valuable data throughout the journey strategically. It provides an effective way to master data management.
Locate The Data Quality Issues at the Core
Professionals often acknowledge and fix the data quality issues at the surface level without investing time in understanding the source. For example, if a data science expert comes across empty records in a specific data set, he’ll probably fix the error in the records and proceed with the routine analysis.
However, if the correction isn’t implied at the source level, the original data source will remain unchanged and keep on affecting the records in the future. The wisdom we acquire from this tip is that preventing the mixing of poor data in the first place is how you improve data quality management.
Utilize Options Sets and Normalize Data
Users typically make mistakes when providing data in different formats, particularly content-related errors. For example, even if you write “insure” instead of “ensure,” it might seem negligible but can considerably affect the data set quality. Try to use defined option sets and values for such fields, preventing the system and users from committing any mistakes. Moreover, data normalization techniques and tools can assist with data inconsistencies for data quality improvement.
Create A Data-Driven Culture
Data governance teams are responsible for managing the availability, usability, integrity, and security of enterprise data in the systems. But are also equally responsible for promoting a data-driven culture that follows a particular set of values and behavior. The overall outlook and perception of the data allow teams to use the data effectively.
Governance experts should clearly define what they mean by quality data in their enterprise’s particular context, identify quality metrics and data cleansing approaches, ensure the defined metrics are consistently met, and create a roadmap for fixing irregularities and other errors. Additionally, empower users like data scientists, data analysts, and end-users with the ability to identify and address data quality issues as a self-service data quality initiative.
Hire A Data Steward
Recommend a data steward for data quality management who will be responsible for comprehending the current dimensions of data quality, optimizing review processes, and using the essential tools for fixes. You can even nominate a person from your data governance as some governance activities and managing metadata will also constitute a significant part of their job. The data steward will be entitled to complete accountability and supervision over the activities to improve data quality.
Siloed data is never preferred as it rarely provides value to the user due to its inability to obtain a comprehensive overview of the business and provide a single source of truth. When data is isolated, users from different departments perform data publication rather than sharing, which generates confusion, inconsistencies, and a lack of agility in data-driven tasks. By eliminating silos through data integration, all the relevant stakeholders can see all the data assets at once and from a single source of truth. This also helps enterprises attain consistent data that is free from irregularities.
Consider Data Quality as A Process
Maintaining data quality isn’t a one-time process where teams implement tools and acquire value through one-off insights. A well-running data management system and processes aren’t enough; businesses need to continuously cleanse the data and create a consistently running system around it. And for this, they need to define some data quality dimensions beforehand that can be followed in the future during analyses.
Continuously analyzing your enterprise data operationalizes your data assets and oversees your data analytics. Try to streamline all the activities from data storage to analysis and application to data management.
Improve your data quality with Royal Cyber
Whether it is incomplete, inaccessible, or outdated, poor data quality will compromise security and credibility and also obstruct your business from thriving in the digital transformation sector. Data quality is always at the forefront, either for cloud migrations or laying the playground for enabling data-based results. Hence, improving your data quality will reciprocate obtaining similar insights for the business.
Royal Cyber is a digital transformation firm offering data analytics services and consultancy for enterprise-level clients. Our data governance and analytics experts are well-equipped with the above tips and educate businesses on how to improve their enterprise data quality to make intelligent decisions. Contact us to discuss your enterprise needs with our experts.