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Innovations in data-driven intelligence continue to power transformation initiatives for modern businesses. While these innovations improve how we collect, store, manipulate, and analyze data, that is not enough to fully leverage data. Being accurate, consistent, and contextual is crucial for unleashing the data’s full potential. It is the combination of three components that defines data integrity and ensures all of the necessary information is captured to make confident business decisions.
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Data integrity is crucial if businesses are to treat data as a strategic asset because, without it, organizations are making decisions that will result in flawed strategies. But how can businesses achieve integrity? It’s important to first understand the four core pillars it’s built on: data integration, quality, location-intelligence, and enrichment.
Pillar #1: Data Integration
Businesses are constantly managing data on customers, prospects, vendors, inventory, employees, and more. Oftentimes, these businesses are using multiple applications that operate individually rather than cohesively. When applications are running on an individual basis, it becomes difficult to maintain efficiency across the organization, and data becomes siloed, resulting in an unclear view of the business.
It only becomes more difficult when the languages spoken within each software system vary. For example, an enterprise resource planning (ERP) system considers a customer a customer, focusing on their warehouse locations, billing addresses, etc. However, a customer relationship management (CRM) system views data in terms of accounts, prospects, and leads, focusing on individual purchasing decision-makers. The different ways these systems view and store information can lead to challenges when attempting to make informed decisions because each system lacks the full insights available to the business.
This is where data integration comes in, providing a holistic view of customers and potential customers. However, it’s not often an easy feat. The integration of organizational data requires extensive planning and strategic approaches to make it happen. But the return on investing in wide-scale integration is well worth the effort as it allows decision-makers to see how segmented parts of the business interact with certain audiences.
Pillar #2: Data Quality
Enhancing the clarity and flow of information is a great first step, but Data Quality plays a major role in guaranteeing complete, accurate, and consistent data. Whether it’s integrated from multiple sources or even just from a single system, Data Quality can often be poor and inconsistent due to the many formats used to collect and record information. This compromises the analytics that businesses are relying on to operate effectively.
A common example of where this challenge arises is when customers are listed in multiple ways, such as abbreviated companies versus full business names, which leads to inconsistencies that impair the decision-making process by skewing the size of certain datasets and contributing to flawed analyses. This example can be seen across different systems, such as ERP and CRM, and is exacerbated by the M&A process many businesses face today. These challenges reduce efficiency and result in avoidable costs since time and resources are spent engaging with and planning for duplicate entries.
When it comes to analyzing and understanding customers, seemingly minor mistakes compound into larger issues within an organization and affect how it gauges and addresses its target audiences’ needs. It’s critical for businesses to invest in improving Data Quality from the onset to ensure accurate insights drive every business decision.
Pillar #3: Location-Intelligence
Location-intelligence collects data on a territorial scale and ensures that businesses are recognizing location-based information, which enhances data integrity by delivering greater accuracy and consistency. For example, when an address or location is widely known under two legal names, such as Route 66 or Highway 66, these systems need to understand that they are the same regardless of the difference.
Discerning between a customer’s billing address and a shipping address seems obvious, yet businesses often overlook this critical difference. Being more precise with location information is vital for business data, and location-intelligence brings that possibility to life by distinguishing the exact locations where customers are located versus where deliveries should be occurring.
Location-intelligence is vital for businesses that deliver physical services — whether retail, healthcare, telephony, or safety. When it comes to delivering services to a person or group of people, it is essential that the location information is of high integrity.
Pillar #4: Data Enrichment
Data enrichment is critical for businesses seeking to build a competitive advantage because it provides a more contextualized view for decision-making. By using external sources, such as third-party data, organizations can act upon a greater degree of analysis alongside their existing data. This data can include location, business, climate, and consumer demographics, among other factors, to add dimensions to existing datasets that will help set a stronger foundation for planning initiatives. Enriching existing data enables businesses to build better customer profiles and target geographic regions to guide them toward accomplishing their goals. Enriching internal business data with third-party data can open up whole new areas of innovation for businesses.
The Journey of Data Integrity
Business leaders must create a solid foundation for all of their digital transformation initiatives. By prioritizing integration, quality, location-intelligence, and enrichment, organizations can gain a competitive advantage through greater accuracy, consistency, and context. Data integrity is powering how businesses make decisions and approach the market. Data integrity is imperative for success in today’s increasingly data-driven world.