Data in today's world is a currency. Organizations managing their big data well can afford a stable currency worthy of significant business value. Key characteristics you'll find in such organizations include data accuracy and consistency, among other data quality attributes. In contrast, some organizations are characterized by inconsistent and inaccurate data, and they face numerous challenges leveraging large volumes of data for business decisions and daily operations.
A huge part of the differences between these two organizations hinges on their data quality improvement efforts. As a business rule, the higher your data quality, the more precise your data insights and chances of satisfying business users. Here are a few ways to ensure data quality in your organization.
There are many different ways to ensure data quality in your organization, and making the data quality process an organization-wide priority can be a good first step. Often, businesses treat data quality as a company policy reserved for business intelligence experts and IT team members, but that has proven to churn minimal benefits compared to an all-inclusive approach.
Data quality management involves the sum of all efforts to transform inconsistent data, making it error-free and aligned to the organization's purpose. It includes adopting best practices to rid an organization's data of duplicates and other inconsistencies, and this can apply to all other professionals as much as your IT department. The fewer people involved in your data quality control efforts, the more room you create for data entry errors at the source, which increases the inaccurate data load for your organization to bear.
High-quality data doesn't just happen. It takes consistent monitoring and quality assessment to improve the quality of your data. Different departments and data managers may have different quality standards and business requirements for data quality checking. Quite often, that's where the challenges arise, as data quality experts need to be on the same page. Establishing data governance guidelines based on the uniqueness of your business needs can be a good idea.
Data governance comprises processes, roles, policies, standards, and key performance indicators (KPIs) that manage and measure efficiency in using information across your business. Data science experts recommend organizations establish their guidelines based on their business processes, use cases, and operational structures.
Data governance and quality require different people to work together for the same data quality cause, but you'll need enforcers for your data quality policies on an ongoing basis. Data stewards focus on data integrity and quality. They play an oversight role in your organization, supervising your data governance strategy's implementation. Data stewards report on your data's functionality levels, ensuring professionals across your organization are in good standing with established data quality policies.
It's tempting to make data quality a typical IT role. However, businesses increasingly realize the need to narrow the steward and the data owner gap. Assigning a data steward to multiple areas where they can get closer to your user community can be a great option.
Managing data from disparate sources and different systems can be daunting and expensive and often leads to several data quality issues. It pays to have an integration strategy that reduces data redundancy and supports your business goals. Over the years, the traditional extract-transform-load method has given way to more efficient cloud-based options. This paradigm shift continues to fuel a rapid migration to cloud-based integration solutions like integration-platform-as-a-service (iPaaS) technology.
The cloud can be a great way to bring your premise applications and legacy systems into a single source of truth. That way, you can access your data sets from a single verifiable source regardless of their encryption formats.
All in all, managing your data sets via a single version of truth can help upgrade your data quality levels. There's often more room for automation, limiting the risk of data challenges due to human error.