BLUF: Achieving high quality data comes with many challenges. Here we share some strategic ways to counter the associated growing risks.
Note: This is Part 2 of the series Exploring Data Quality – read part 1.
With the Three Dimensions of Data Quality in mind, next, let’s look at how to use strategies, processes, and tools to achieve high quality data at the enterprise level. First, we will identify and examine common challenges organizations face reducing their data quality and then discuss tactical strategies for combating them.
Common Challenges
By identifying and addressing your organization’s unique challenges, you can lay a solid foundation for mitigating the risks associated with bad data.
Data Entry Errors: Human errors during data entry can introduce inaccuracies and inconsistencies into the data. Typos, missing values, and inconsistent formatting are common examples compromising data quality.
Data Integration Issues: When organizations collect data from multiple sources or systems, integrating and reconciling the data can be complex. Incompatible data structures, duplicate records, and unannounced API changes can all contribute to data integration challenges.
Lack of Policies and Structure: Commonly known as Data Governance, the policies, processes, and roles that ensure the effective management and control of data within an organization ensure its validity. Standardized rules, procedures, and accountability for data management are imperative components to achieving high quality data.
Tactical Strategies for Improvement
Establish a Data Governance Framework: Implement a robust data governance framework that outlines the responsibilities, processes, and policies for managing data. This framework should include data stewardship roles, data quality monitoring mechanisms, and improvement initiatives.
Establish Data Quality Standards: Define a clear set of standards and guidelines that align with the organization’s objectives and requirements. These standards should cover the three dimensions of Accuracy, Reliability, and Relevancy.
Implement Data Validation and Verification Processes: Employ data validation and verification techniques to identify and eliminate errors in the data. This can involve automated checks, validation rules, and cross-referencing data against trusted sources. By validating and verifying data, organizations can ensure its accuracy, completeness, and consistency.
Invest in Data Cleansing: Data cleansing involves identifying and rectifying errors, inconsistencies, and inaccuracies in the data. Data cleansing processes should be regularly performed to maintain data quality and integrity. This can include removing duplicate records, correcting typos, and filling in missing values.
SOI Solutions + Cribl = High Quality Data
Having strong strategies in place, sets a solid foundation for companies to trust their decisions are informed by accurate and precise data. We are your partner in your journey towards achieving impeccable data quality. We believe in collaborating with teams, fostering innovative environments, and maintaining successful data governance with stringent standards.
Once the strategy is in place, the next step is to implement technical controls for enforcement and validation. Cribl solutions are designed to perform a multitude of ETL tasks, including collecting, routing, filtering, and transforming data. These tasks, executed meticulously, align your data with your governance standards, ensuring consistency and reliability.
In our age of digital transformation, data is the backbone of every organization. A slight discrepancy can lead to faulty decisions, impacting the overall growth of your company. With the support of SOI Solutions, you will have confidence in your data again. Our comprehensive services not only improve the quality of your data but also enhance its usability, paving the way for informed decision-making and successful business strategies.
