Why Data without Context Lacks Integrity
You’re likely familiar with the oath that witnesses must take to tell “the truth, the whole truth, and nothing but the truth.” It’s also pretty clear why “the whole truth” is included as part of that promise. When someone gives you only part of the story, it can distort your understanding of the facts. At best, you get an incomplete picture. At worst, you draw the wrong conclusions altogether. That, in a nutshell, is why data integrity is so important.
You should view enterprise data integrity through the same lens. In other words, when you only have partial view to relevant information, you could very well be missing something important, or you could draw conclusions that lead you in the wrong direction. At the enterprise level, that means making bad business decisions, and that can have serious negative consequences.
Precisely believes that “the whole truth” is an important part of data integrity. Context is an essential element in the quest to discover meaning and uncover business value in your data.
What is data integrity?
Ask ten people to define data integrity, and you’re likely to get 10 different answers. Many people use the term to describe a data quality metric. Technical users, including database administrators, might tell you that data integrity is determined by whether or not the data conforms to a pre-defined data model. Still others will provide a mix of answers that touch upon accuracy, consistency, and conformance to standards. While those answers might reflect some element of the truth from a technical perspective, they fall short of explaining business data integrity in its entirety.
At Precisely, we see data integrity from a broader perspective. For us, it’s about your data having the accuracy, consistency, and context for you to confidently trust it for business decision-making. To be sure, data quality is a critically important part of that picture. But if you step back and look at the business value that enterprises expect to derive from their data, it’s clear that you need to expand those traditional definitions of data integrity.
If decision makers and others throughout an enterprise are to fully trust the data, they need “the truth, the whole truth, and nothing but the truth.” They need a complete picture. That requires reliable, scalable data integration that eliminates siloed data and allows organizations to view it holistically. It requires the powerful element of location intelligence. And it requires data enrichment from third-party sources to create even deeper context and meaning. That gives decision makers visibility to the “whole truth” of their enterprise data.
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That’s why these five elements make up Precisely’s definition of data integrity: data integration, data quality, data governance, location intelligence, and data enrichment. Those components provide accuracy, consistency and context, making it possible for enterprises to act with confidence on insights driven by trusted data.
Location matters (more than ever)
Traditional GIS analytics have an important role to play in understanding location, but the value of location extends much further than that. Virtually every data element you can think of has some relationship to a physical location in the real world. When you can match that location to other geospatial attributes, you open up a whole new world of possibilities for data analysis.
Let’s take a simple (and very obvious) example from the real estate industry. The value of a home is determined to a great extent by its size, the quality of workmanship, and the amenities it includes. But none of us would imagine pricing a home based solely on those factors. Location matters.
The quality of local schools makes an enormous difference–so much so, in fact, that two identical houses situated very close together but in different school districts might be valued very differently. Other location-based factors have a significant effect on price as well. That includes crime rates, median income levels in the region, tax rates, proximity to public transportation, and more. Without the context of location, it is virtually impossible to develop an accurate estimate of a home’s value.
Now let’s consider a somewhat less obvious example. Banks and credit unions develop a range of criteria for evaluating the performance of branch managers, but the characteristics of each branch vary widely. It’s not fair to compare a branch location in a busy downtown area to one located in a rural community. The nuances of location extend further than that, though. What are the surrounding traffic patterns? How much parking is there? Are there numerous businesses in the area, and are they thriving? What does the competitive environment surrounding a branch look like?
Once you begin to consider all the factors that can impact business at a particular location, assessing the relative performance of branch managers can appear increasingly complex. Location intelligence adds context to the data about branch performance, providing a deeper and more meaningful analysis of which managers are doing well and which are not. Context makes all the difference.
Data enrichment, likewise, adds depth and context to your existing corporate data. According to IDC, 86% of senior leaders in organizations they surveyed believe that the ability to integrate and derive value from external data will be a critical competency over the next three years.
With data enrichment, enterprises can develop a more complete picture of their customers, competitors, and supply chains. They add depth and nuance to the information they already have. Insurers who offer a mix of commercial, auto, and homeowners policies, for example, can better understand which of their customers are also business owners and therefore might represent new sales opportunities for commercial policies. They can understand family relationships and identify life events that could impact automobile policies. When a teenager reaches the age at which they may begin learning to drive, the insurer can take that opportunity to reach out and engage with their customer. The additional context of family relationships, life events, and business ownership provides rich opportunities to upsell customers, increase loyalty, and win more share of wallet.
To learn how 300+ C-Level data executives in the Americas, EMEA and Asia Pacific are managing enterprise data assets to fuel reliable data-driven business transformations, read the research report from Corinium Intelligence, Data Integrity Trends: Chief Data Officer Perspectives in 2021.