Improve Your Enterprise Data Quality with These Proactive Steps

Written by Imran Abdul Rauf

Technical Content Writer

Sales and marketing teams worldwide lose approximately $32,000 per salesperson and 550 hours through poor data usage. Plenty of other statistics that 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 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, valuable insights, and if its utility proves beneficial for the business. Typically, focusing on accuracy, completeness, timelessness, consistency, and accessibility is the practice of 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 that data for making informed decisions and if your data governance programs are performing up to the mark.

  • Driving business through data quality: Any enterprise can use the insights to streamline its operations, optimize its internal processes, create and deliver excellent customer experiences, and generate better ROI through quality data. The better the data insights, the better decisions you will make for today and future endeavors. And quality data translates into similar 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 data quality improvement activities. Therefore, data professionals should understand and prioritize the data quality issues that need to be addressed for specific business needs, if they’re in alignment with the company’s IT objectives, and how they impact the business in the long run.

Analyzing the business impact and predefined KPIs puts the data quality improvement initiatives 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 quality improvement measures.

Comprehend your data

Not all kinds of data are equal and suited for all businesses. IT teams and decision-makers need not only the data which is correct, but also the correct 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 educates how to adequately explain and connect the data throughout the journey strategically.

Locate the data quality issues at core

Often data quality issues are acknowledged and fixed at the surface level without spending time to understand 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 affect the records for future use. The wisdom we acquire from this tip is that preventing poor data in the first place is how you improve the data quality.

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 data in the business 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, 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 to manage the data quality who’ll be responsible for comprehending the current data quality scenario, 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.

Eliminate silos

Siloed data is never preferred as it rarely provides value to the user due to its inability to obtain a comprehensive of your business and 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 data silos, all the relevant stakeholders can see all the data assets at once and from a single source of truth.

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, but the business should need to continuously cleanse the data and create a consistently running system around it.

Continuously analyzing your data proportionally operationalize your data assets and oversee your data analytics. Try to streamline all the activities associated with data storage to analysis and application to data management.

Improve your data quality with Royal Cyber

Whether it is incomplete, inaccessible, or outdated, insufficient data will compromise security and credibility and obstruct your business from proceeding 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.

Related content: Maintaining Data Quality During Cloud Migrations

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 data quality to make intelligent decisions.