One of the results of our Drivers for Growth in Service research has shown that companies are struggling to design and implement an integrated digital platform to support their services business.
In this blog I discuss why data quality is a key success factor for digital services transformation. I also give some recommendations on how to overcome the challenges related to service data.
The importance of data management and quality for digital service transformation
To successfully sell and deliver services requires a lot of data. This includes data on products and parts, installed base, customers, service contracts, service requests, work orders, maintenance plans, and service resources (together with their skills and availability).
Reasons that data is essential in delivering services:
Having data that you can trust and rely on is critical for achieving the business objectives of your service organization and making the right decisions to improve and streamline the business.
One of the key challenges for many service organisations is control over data quality and the ability to provide reliable data that can be trusted to support the service processes. Unfortunately, we sense that data quality is not well understood or taken seriously enough.
Typical data quality issues are:
With the introduction of connected products, the volume of data collected, distributed, stored and processed increases dramatically. Being able to handle and manage all this data is another key challenge.
The way personal data is collected, processed and use has been impacted significantly by regulations like GDPR. It is very important to understand what type of data you have and its level of sensitivity. Adapting systems, data and procedures for compliance purposes is also critical.
Service organisations are part of a value chain and as such need to exchange information with other parties in that chain. B2B integration can be quite difficult, for e.g. defining, agreeing and/or using industry standards for safe data exchange, as well as monetizing this information exchange in a way that is mutually beneficial.
How could you address these challenges?
The most common root causes of data quality and management difficulties are:
To start addressing any data challenges requires the creation of a Master Data model. The Master Data model defines how data is structured and used, and which roles or applications are responsible to create, edit and/or delete the data.
In addition, it is important to define how the Master Data will be managed and who is responsible. For e.g., if a new product or service is introduced, what data needs to be created and which systems need to be updated? What marketing and sales content needs to be created and who will create it?
Once the Master Data model is in place, data quality can be improved. The Master Data model should be used to assess the quality of the data for each object in the model by assessing whether the data is accurate, complete, up to date, consistent and whether there are duplicates.
To address security and privacy challenges, the Master Data model should show which user roles have access and if they can create, edit or delete the data. In regards to data objects in the Master Data model, the classification, data privacy and security policies thereof should be defined. This includes the measures to be applied.
Before you begin data cleansing existing data, you must avoid new data quality issues emerging. Therefore, it is important that the data integrity rules are applied to ensure that the input data makes sense, is complete and accurate when it is created or updated.
Integrity rules include:
To improve existing data, you could consider the follow tactics:
Once the Master Data model is defined and the applications that need to be created or used with data identified, the integration architecture must be designed and implemented.
Several integration patterns and technologies can be considered. Which are best suited depends on the number of applications and data sources, the timeliness and frequency of data flows, the amount of data and the integration capabilities of the applications involved. The integration architecture must be robust, flexible and scalable. order to avoid that changes the data model has a huge impact on the interfaces.
The most important conclusion is that data is critical to the success of digital service transformation and this shouldn’t be underestimated.
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