Most companies would acknowledge that quality customer data is important.

In most cases, however, customer data quality is not good enough.

Why?

Changing business requirements

Most businesses have a history.

Customer data may have been accumulated over years, or decades.

Data that was captured in the past may not have complied with the requirements of modern business.

Two simple examples:

Enhanced customer analytics

Modern, data-driven marketing and product development is driving interest in our customers that goes beyond the basic need-to-know data points of years ago.

Our marketing department may want to know about our customer’s interests, employment history or education (for example) at a level of detail that may not have been catered for in the past.

New channels of communication, and social media outlets, also generate data points that earlier data sets could not possibly have catered for in the past, as they did not exist, or were not relevant.

A focus on customer segmentation or experience may find that existing data does not cater for these new requirements. Data may be adequate for operations but is of poor quality for analysis.

The race toward artificial intelligence and machine learning applications to drive better decision-making, and even to advise clients, is particularly impacted – with more and more acknowledgement that data quality is essential for AI

Regulatory changes

Highly regulated industries – think financial services – are used to navigating a minefield of data-related compliance.

Over the last few years regulations and standards such as BCBS 239, AML and FATCA have forced banks and insurance companies to learn more about their clients. Existing data may not have been sufficient.

Regulations drive new data standards that may not have been catered or in the past. Historically accurate data may fail data quality checks when measured against these new requirements.

The growing focus on customer privacy and the ethical use of data means that every industry is becoming highly regulated. Do your systems allow you to track and measure customers’ needs with respect to regulations such as GDPR and PoPIA?

Data degradation

Customer data degrades at a rate estimated at +- 2% per month.

People change jobs, move house, get married, have children, etc.

Over 1 year this means that +- 30% of your contacts and leads have lost quality – making them harder to contact, harder to support, and harder to understand.

What is the impact on your business if you recognize that one in three customers, leads or accounts data are no longer relevant?

The Critical Role of Data Capture Standards

In the age of digital transformation, the rush towards digitization is often hailed as a harbinger of improved data capture efficiency and accuracy. While the promise of seamless data integration and precision is tantalizing, the reality is more nuanced.

One of the central challenges lies in ensuring that data is captured accurately in the first place. Far too often, data capture standards fall short, leading to inaccuracies that reverberate throughout the data lifecycle. The digital journey, while transformative, must be underpinned by a resolute commitment to data quality. Without this dedication, digital endeavours risk becoming hollow victories, with efficiency gains overshadowed by data quality compromises.

Paving the Path Forward: Embracing Data Quality as a Cornerstone

As the facets of modern business continue to evolve, the importance of data quality becomes increasingly indisputable. High-quality customer data is not a mere luxury; it is an essential ingredient for sustainable growth, strategic decision-making, and personalized customer experiences.

Recognizing the role that changing business requirements, regulatory dynamics, and data decay play in influencing data quality is the first step towards addressing this challenge. By proactively embracing robust data capture standards and cultivating a culture of data quality, businesses can bridge the gap between historical data and the demands of the present.

In the relentless pursuit of AI-driven insights and data-powered strategies, the significance of data quality emerges as a lynchpin. Every byte of data captured, analyzed, and utilized contributes to the foundation upon which these advanced technologies operate. Thus, the journey towards comprehensive AI integration and informed decision-making is contingent upon the bedrock of high-quality customer data.

Conclusion: The Imperative of Data Quality

In the intricate tapestry of modern businesses, data quality is the missing piece that completes the customer puzzle. From compliance to customer experiences, from operational efficiency to strategic foresight, every facet of an organization’s journey is underpinned by the quality of its data.

Embracing data quality is not a mere option; it is a mandate for businesses that aspire to thrive in an increasingly competitive landscape. As industries traverse the terrain of changing regulations, evolving customer expectations, and transformative technologies, the role of data quality assumes paramount importance.

Dive into the significance of data integrity in business engagement and its impact on organizational success

In the quest for sustainable growth and enduring success, businesses must recognize data quality as a strategic imperative. It is the key to unlocking the value embedded within customer data and, by extension, unlocking the potential for innovation, differentiation, and a brighter future.

Lay the foundation for a customer-centric business with accurate customer data