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.

The 4 Pillars of Data Integrity

Making it easy to connect, validate and enhance your data

Data Integration

Data Integration

Easily connecting your data



Accuracy and consistency

Accuracy and consistency

Delivering trusted data


Location intelligence

Location intelligence

Analyze your customer's movements

Location Intelligence

Master Data Management

Master Data Management

Creates a consistent, quality set of reusable data

Get started

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.

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 or 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.

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.

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 affecting both individual customers and staff directly. Reputation is at stake.