Data integrity is the quality, reliability, trustworthiness, and completeness of a data set – providing accuracy, consistency and context.
Data integrity is built on four key pillars: enterprise-wide integration, accuracy and quality, location intelligence, and data enrichment.
Why is data integrity important?
Overwhelmingly, we rely on information to do our jobs, and to inform our decisions. Our business is dependent on our ability to maintain and deliver reliable, trusted data, when needed, to the knowledge workers and decision-makers that depend on it. Data integrity means that we deliver this trusted data, consistently, to every knowledge worker and decision-maker that needs it.
Driving digital transformation
A key goal of most digitalisation programs is the automation of inefficient, manual processes. IDC research shows that 47% of newly captured data records have at least one critical error. Reducing errors at the point of capture, and ensuring integrity through the data lifecycle is key to enabling efficient automated processes.
Data integrity is therefore a stepping stone to digital transformation.
It doesn’t stop there. Digital adoption drives an increase in both the volumes and complexities of data coming into the enterprise. Companies need to manage this complexity in order to listen to the new, digital voice of the customer. As businesses shift to new digital channels, it becomes easier to lose critical customer interactions in the digital noise.
Data integrity is at the foundation of digital transformation making it easier to understand and support customers.
Next-generation Customer 360
Next-generation customer 360 approaches move beyond entity resolution to identify and track customer interactions and relationships – critical to understanding the customer buying journey and driving smarter omnichannel marketing approaches.
Building high-quality, consistent and reliable datasets starts with consolidating what you have internally. A complete view of our enterprise, and our relationships with our markets, comes when we enrich our internal data with additional information– such as location and demographics data – that allow us to build a picture of our customers, our channels, and our products positioned in the world in which they live and interact.
Data integrity combines internal data with curated third-party datasets o build an unparalleled understanding of the customer, their relationships, and their preferences.
Empowering data science, machine learning and advanced analytics
Advanced analytics capabilities – from data science to machine learning and AI – are the new frontier in data. Businesses are looking for insights from more data and more data sources than ever before. Consumer transaction data, social media, and data in motion (from IoT sensors, mobile devices, vehicles etc) provide many opportunities for insights, assuming that the data is trustworthy.
The old adage, “garbage in, garbage out”, is more relevant than ever before. Flawed decisions, based on poor quality or biased data, have immediate consequences and affect both individual customers and staff directly. Reputation is at stake.
Data integrity enables unbiased, ethical use of machine learning and artificial intelligence for optimised business.
The 4 Pillars of Data Integrity
Making it easy to connect, validate and enhance your data
How do we help you to deliver trusted data?
For more than 15 years, Master Data Management has helped large organisations implement data management best practices to achieve data integrity.
- Understanding the importance of data integrity capabilities within the data strategy
- Building data management skills and data literacy through our courses and certifications
- Implementing appropriate decision making structures to govern data to ensure integrity.
- Enabling collaboration between data stakeholders across the enterprise
- Providing the capabilities to build a common understanding of data and data management practices across the organisation
- Delivering automated capabilities to clean and enhance existing data, and to stop bad data from entering systems in the future
- Identifying and managing duplicate data
- Identifying and managing access to sensitive data