Why Data Quality Driven by Technology Is Key to Success
- Data Quality is Critical for Success: In an era of exponential data growth, high-quality, reliable data is paramount for generating accurate insights, making informed decisions, and avoiding strategic errors.
- Technology’s Pivotal Role: Advanced technology plays a central role in ensuring, managing, and improving data quality across its lifecycle.
The following statement is more relevant than ever in these unprecedented Corona times; without high-quality data, you are at a loss. If you turn on the news, data is being presented in order to inform us on the current status of the pandemic. On a daily basis, we see proof of the criticality of data in making informed decisions and how inaccurate data can lead to disastrous consequences.
Good management of data quality builds a foundation for all the initiatives of a business (and beyond). In order to achieve high-quality data, it is necessary to make this a holistic, continuous improvement process.
Research shows that generally, six core dimensions of data quality can be distinguished:
- Completeness: The proportion of stored data against the potential of “100% complete.”
- Uniqueness: No thing will be recorded more than once based upon how that thing is identified.
- Timeliness: The degree to which data represent reality from the required point in time.
- Validity: Data are valid if they conform to the syntax (format, type, range) of their definition.
- Accuracy: The degree to which data correctly describes the “real world” object or event being described.
- Consistency: The absence of difference, when comparing two or more representations of a thing against a definition.
Other data quality considerations might be useful to make, as there are additional factors that can have an impact on the effective use of data.
The following questions might help:
- Usability of the data? This refers to whether the data is understandable, simple, relevant, accessible, maintainable, and at the right level of precision.
- Timing issues with the data? This questions whether the data is stable yet responsive to legitimate change requests.
- Flexibility of data? This explores if the data is comparable and compatible with other data, if it has useful groupings and classifications, if it can be repurposed, and if it is easy to manipulate.
- Confidence in the data? This involves considering if data governance, data protection, and data security are in place, and what the reputation of the data is, including whether it is verified or verifiable.
- Value of the data? This assesses if there is a good cost-benefit case for the data, if it is being optimally used, and whether it endangers people’s safety, privacy, or the legal responsibilities of the enterprise, or if it supports or contradicts the corporate image or the corporate message.
Zooming in on the life science industry, the basic building blocks of good GXP data are to follow Good Documentation Practices and then to manage risks to the accuracy, completeness, consistency, and reliability of the data throughout their entire period of usefulness; that is, throughout the entire data life cycle.
The FDA expects data to be meaningful, taking into consideration the design, operation, and monitoring of systems and controls based on risk to patient, process, and product. In addition, FDA expects the data to be reliable, including a demonstration of integrity, validation, safety, identity, strength, quality, purity, reproducibility, and so on.
Useful questions to ask yourself in order to meet regulatory requirements:
- Are controls in place to ensure that the data is complete?
- Are activities documented at the time of performance?
- Are activities attributable to a specific individual?
- Can only authorised individuals make change records?
- Is there a record of changes to the data?
- Are records reviewed for accuracy, completeness, and compliance with established standards?
- Are data maintained securely from data creation through disposition after the record’s retention period?
With advances in technology, there are many tools that organizations can use to improve data quality. These tools often perform three main functions:
- Data cleansing.
- Data auditing.
- Data migration.
Depending on the needs and preferences of the organizations, the choice of technology is being made; cloud-based versus on-premise, compatibility with different sources, integrations with other platforms, complexity of data sets, etc.
Implementing data management technologies can help companies to:
- Reduce organizational costs.
- Increase workforce efficiency and productivity.
- Make faster and more knowledgeable business decisions.
- Recalibrate business strategies.
- Improve organizational consistency.
- Support greater collaboration and communication.
Popular technology services for ensuring data quality
In the age of digital transformation, ensuring data quality has emerged as a critical success factor for businesses. Popular technology services play a pivotal role in driving data quality through automation, analytics, and data governance. Here are some key services that exemplify how technology can enhance data quality:
Informatica
Informatica is one of the most popular data management software options. It comes with a set of prebuilt data rules, a rule builder for customisation, and artificial intelligence (AI).
AI can also improve data quality by automating data capture, identifying anomalies, and eliminating duplicates more quickly. This will save human time and allow for more efficient processing of huge data sets.
SAP
SAP HANA is an in-memory platform and database that retrieves and stores date for applications.
Talend
Talend has a metadata management solution and a popular tool for the ETL (extract, transform, and load) function. The basic package is free and open source. Providing a graphical depiction of performance on compliance matters.
Oracle
Oracle offers a collection of data quality programs, including Oracle Big Data Cloud, Oracle Big Data SQL Cloud Service, and Oracle NoSQL Database.
SAS
The SAS Data Management Tool handles large data volumes. Data quality technology is all integrated within the same architecture and can connect to other SAS tools for data visualisation and business analytics.
IBM
IBM has a few different products, such as the InfoSphere Information Server for Data Quality, to monitor and cleanse data, analyse information for consistency, and create a holistic view of entities and relationships.
Defining a data management strategy is central to the success of any enterprise. It is the ‘secret ingredient’ behind how we use and secure information, ensure compliance, operationalise transparency, and reduce expenses. Using technology at our disposal today can make this task much easier.
Stay tuned as Pinnaql will soon launch one of its innovative technologies, using the entire spectrum of data science that guided us to add valuable support to human functions and automate processes.
Data quality driven by technology is not merely a technical necessity but a fundamental pillar for business success in the modern era. By understanding the core dimensions of quality and strategically leveraging popular technology services, organizations can ensure the reliability of their data, empower better decision-making, and secure a crucial competitive advantage in an increasingly data-driven world.
- ResearchGate. 2014. Continuous Data Quality Assessment in Information Systems
- ELSEVIER. 2012. Business Intelligence
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