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What is Data Integrity?

Authors Photo Eric Yau | July 23, 2020

Data has become the single most important corporate asset for driving strategic advantage. Modern analytics platforms are capable of consuming enormous volumes of data, then analyzing and making sense of that data to drive efficiency, innovation, and insight. The leading companies in virtually every industry are the ones that have successfully leveraged digital transformation.  Success in digital transformation is, in turn, built on a clear data strategy.

Accurate, consistent, contextual data plays a key role in an overall data strategy. Analytics and artificial intelligence (AI) can drive strategic advantage, but only if the inputs to those tools are clean and reliable. Data integrity demands that corporate data be consistent and accurate across all of the information systems within the organization’s control.

Data integrity is built on four key pillars: enterprise-wide integration, accuracy and quality, location intelligence, and data enrichment.

1. Enterprise-wide data integration

If an organization was running a single, monolithic application for all of its business processes, then most data integrity checks would presumably be performed at the application level. Several decades ago, that might have been the case for many businesses, but the agile, innovative companies of today must manage an array of different software systems for core ERP, CRM, marketing automation, human resources, analytics, and more.

This leads to one of the key challenges in maintaining data integrity: silos. When departments are running distinct software systems to meet their individual needs, they align those systems to the unique requirements of their department. That can result in variations in the way those different departments treat a customer record, for example. If they are using different software to manage that information, and if those systems model data differently, it could lead to discrepancies or inaccuracies across the two systems. This becomes a problem when data needs to be shared or analyzed.

In some respects, companies themselves have become silos. We live in a highly interconnected world in which supply chain partners share information on inventory item catalogs, serial numbers, vendor information, customers, and much more. Sharing information removes friction from business processes, but it magnifies the challenges of data integrity, as the number of data models, business processes, and software systems grows even larger.

True data integrity, then, results when data is consistently integrated according to a set standard that is applied consistently over disparate systems within the organization.

2. Accuracy and consistency

This raises yet another concern. As the volume of data increases, and as the number of systems in a network grows, data integrity problems present an ever greater challenge. Inaccurate or incomplete data diminishes the value of business analytics, even in a best-case scenario. In the worst case, it renders the results invalid.

In the era of big data and AI, data integrity is a key ingredient that can be the difference between the success or failure of your digital transformation initiative. With the advent of IoT devices, mobile apps, and cloud connectivity, the volume of data that companies have to work with is greater than ever before. Tendü Yoğurtçu, CTO of Precisely, points out in Forbes: “Poor data quality is especially problematic at scale, magnifying initially benign data issues and creating poor business insights.”

A sound data integrity strategy must be capable of managing and validating data across multiple systems, identifying gaps or discrepancies, and triggering workflows and processes to correct those errors.

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3. Location intelligence

Location intelligence involves the use of geospatial data to reduce risk, better understand customer behavior, and increase efficiencies. Virtually every data point in the world can be associated with location in one way or another. Companies embarking on digital transformation initiatives, in particular, should consider location intelligence as a key component of their overall data strategy.

This may be as simple as standardizing address information across a customer database so that data can be understood and analyzed within a common context. For example, if our data tells us that a customer is located at “123 Amherst Street”, and another customer is at “125 Route 101A”, we might not understand that they are located on the same road, and are simply using different names for that road. A sound data integrity strategy would ensure that we are able to see both of those locations in their proper context.

On another level, location intelligence can add context to existing data. When we’re able to better understand boundaries, movement, and the environment surrounding the customer, vendor, store location, or other entity; we can gain richer insights to drive better business decisions.

4. Data enrichment

This leads us to the final pillar of a good data integrity strategy: enrichment. Most leaders understand that their data is a valuable asset. When we add trusted third-party data, that asset increases in value. It is a classic case of “one plus one equals three”.

Let’s return to our previous example, in which we validate and standardize addresses in a database of customers and prospects. That process gives us confidence that the insights we derive from that data can be trusted. Now let’s consider an enrichment scenario, in which we add mobility data to the equation. If our customers are retail businesses and we want to better understand them, mobility data can help us to analyze traffic flows into and around those locations. Moreover, we can develop a clear picture of where that traffic is coming from. Suddenly, the data starts to paint a far more detailed picture for us.

The data integrity imperative

Understanding that data is a strategic corporate asset, smart business leaders are establishing clear frameworks for implementing these four pillars of data integrity. They understand that reliable, secure interconnectivity (data integration) is a clear starting point. They also understand that meaningful insights cannot be derived from data that is inaccurate or incomplete (data quality). They understand the power of location (location intelligence) and the potential to create additional value by integrating data from third-party sources (data enrichment).

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