The data quality cycle as a “closed loop”

But even a consolidation of the data silos does not automatically lead to a higher data quality. Rather, complementary measures are needed that need to be integrated into a continuous cycle, the data quality cycle.

This data quality cycle consists of a “closed loop” of actions that must seamlessly intermesh to allow continuous control and optimization of data quality. The four phases of the data quality cycle are:

1. Analyze

2. Clean up

3. Protect

4. Monitor

The first step in the analysis phase is to determine the current state of the customer master data available in the enterprise, identifying deficiencies in the nature and scope of the data, inconsistencies in data attributes, and possible violations of pre-determined data collection rules.

The cleanup phase then defines the actions that need to be taken to address the deficiencies identified in the analysis and to optimize the quality of the data.

After extracting the data from the source systems (CRM, ERP, service helpdesk, for example) and careful validation (for example, by postal address check and cleanup of duplicates), the data is enriched by additional information, such as geo-data. The result is the so-called “Golden Record”, the “mother of all customer master records”.

The four phases of the quality process

The long-term and sustainable preservation of the desired data quality level is the focus of the “shooter” phase. This is achieved by setting up “DQ checks” at the points in the company where data gets into the company. This ensures that a high data quality is achieved the first time the data is collected. A “creeping pollution” of the data is thus prevented. Companies set up so-called DQ firewalls according to the “first time right” principle.

The “monitor” phase ultimately serves to define processes that can be used to ensure continuous monitoring and documentation of data quality in the company. An important tool in this phase is the “DQ Scorecard”, with which the quality of individual data stocks can be recorded and assessed at a glance. This measure also helps to prevent a “gradual deterioration” of the given or required data quality level.

Based on the “closed loop” approach described in the beginning, the results recorded in the “monitor” phase then form the basis for a new analysis of the data quality.

The only thing that is constant is the change

The importance of the most up-to-date, precise, complete and correct 360-degree view of the customer for operational and strategic decisions in the most diverse business areas (sales, marketing, corporate governance, strategic business development) has already been described above.

However, in practice, more and more companies are finding that the customer base data collected in-house alone is not enough to achieve this 360-degree view. The more the customer becomes a “digital customer”, the more this digital customer leaves his “mark”: at the company’s ever-increasing number of touch points (website, online shop, newsletter and more), but also in online communities , Rating portals and social media. Also, these “tracks” must be taken into account to allow a true 360-degree view of the customer.

Uniserv gets to the bottom of the truth

“Ground Truth” is a solution and process methodology developed by Uniserv that helps companies to create the Golden Profile of each customer beyond the customer’s Golden Record. In it, its address data, its buying behavior, its interests and preferences, but also its communication and interaction with the company are aggregated into a central data record.

These data are supplemented by the above-mentioned “traces” left by the customer on the Internet and social networks. Finally, the golden profile contains the master data of each customer (golden record) and his transaction data (transaction and interaction data). Ground Truth also follows the concept of the data quality cycle, ensuring continuous updating of this data as well as synchronizing the data across the different data sources.

So, a one-time action is not enough today if companies strive to increase the quality of their customer data and keep it on a high level. Instead, a continuous data quality cycle of seamless interoperable data quality optimization measures is required, ideally implemented company-wide.

The ground truth method enables companies to create each customer’s golden profile. Only then do they achieve a true, true 360-degree focus on the customer: the foundation for a basic confidence in their own data. If there is still a continuous review and optimization of the data quality, the way is clear for a successful customer journey.

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