Customer Data Quality: The Weak Link in Your Digital Transformation?

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jeanmichelfranco_talendIn this special guest feature, Jean-Michel Franco, Director of Product Marketing for Data Governance at Talend, discusses how big data can affect customer relationship and how data quality can be the weak link in an organization’s digital transformation. Jean-Michel has dedicated his career to developing and broadening the adoption of innovative technologies in companies. Prior to joining Talend, he started out at EDS (now HP) by creating and developing a business intelligence (BI) practice, joined SAP EMEA as Director of Marketing Solutions in France and North Africa, and then lately Business & Decision as Innovation Director. He authored 4 books and regularly publishes articles, presents at events and trade shows.

What if you were able to manage your own data to boost your customer relationships?

The digital transformation is underway now that all businesses, particularly those associated with customer relationships, have become aware of the value data can add to their operating activities. However, the divide between top performers and others continues to grow, with the latter only experiencing a fraction of the potential the digital economy has to offer. The most frequently mentioned causes for this shortfall range from a lack of management support, to the difficulty in recruiting experts in data science, and the inability to create a culture of experimentation. What is often overlooked as a reason for not taking full advantage of data is issues related to data quality. Yet, in this regard, the figures speak for themselves.

Is Unreliable Data Inevitable?

Several studies show that one-fourth of the contact details contained in corporate customer databases is incorrect. This is explained in part by the fact that they are rather volatile: 71 percent of this data has been changed at least once over the course of the last 12 months (i.e. based on a move, change of phone number, change of position, role, or situation, etc.). The result: 54 percent of companies admit they do not have the data required for personalizing their content or services to meet customer needs.

The cost of bad data has been estimated at $14 million dollars a year for each large company. Add to this the problem of poor customer consent management and opt-in, which erodes consumer trust, the difficulty of dealing with privacy regulations, and data loss – 534 million personal data records have been compromised since 2005.

Should we consider handing responsibility over to a central governance service, possibly outsourcing the necessary tasks for qualifying and securing customer data, as we do with janitorial services? Or, rather, is it time to regain control of the key company asset that fuels marketing campaigns, sales and customer loyalty activities: customer data?

And what if we cleaned up our data?

It is high time for those handling customer data on a daily basis to participate in the maintenance of its quality. Particularly now, with the emergence and increased use of self-service tools to make this task a whole lot easier and less time consuming. The better solutions are equipped with familiar man-machine interfaces like Excel that make them accessible to non-experts; they are capable of automatically recognizing the most common customer data such as email addresses, phone numbers or postal addresses, and guiding users towards the necessary actions to correct the data that appear suspect.

These tools bring with them collaborative features that allow marketing to enhance data quality by assigning tasks to those best equipped to perform them. For example, they can automate the verification of mailing addresses, telephone numbers and email addresses, or enable the merging of duplicate customer records. Additionally, automation tools can learn based on human expertise by taking advantage of technologies, such as machine learning, to understand the rules of reconciliation as performed manually by experts, then reusing them on a large scale on much bigger data volumes.

Ultimately, these types of tools allow marketing teams to roll up their sleeves and organize campaigns to enhance their data. They are also the primary beneficiaries since the objective of these campaigns is also to improve the conversion rate and the effectiveness of marketing campaigns.

All for One, and One for All

Giving more power to information consumers: sound like a good idea? Years back, Web 2.0 introduced new collaborative models that revolutionized the way information on the Internet was not only used, but also produced. Sites like Wikipedia and TripAdvisor revolutionized the model for orchestrating the collection, curation, and dissemination of information on a potentially unlimited scale. That was when traditional, centralized information collection and quality models became irrelevant.

Today, Big data, cloud, and self-service, pit companies against the same challenges—how to scale data collection and governance models in this new era. It is time to establish a more collaborative governance model that makes everyone responsible for managing their personal, corporate information capital. After all, those who use the data on a regular basis are best position to guarantee its quality This doesn’t mean the centralized governance model has to disappear, but in today’s day and age, it is becoming more the exception than the rule. The entire challenge, as we see it, lies in the establishment of a governance system which makes it possible to approach the problem and find solutions collectively. That is exactly what the digital world offers us today: the ability to create an “all for one and one for all” approach to data governance.


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