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Mastering data quality in service management

Mastering data quality in service management

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:

  • To provide the customer a state-of-the art customer experience. This necessitates your service staff having a 360 degree overview of the customer.
  • To optimise the scheduling and planning of work orders, to improve efficiency and to reduce travel time, you need data on maintenance plans, work orders, availability and skills. In addition, data on location of resources, route and expected travel time, required parts and the availability of parts, is also required.
  • To find inefficiencies and bottlenecks, for e.g. to detect if a certain part has failed more often than others or that certain types of work orders take longer than expected. This can then be used to make adjustments to the process, product or master data.
  • Get insights on how your service business is performing and to use these insights to identify business improvement opportunities, such as improving first time fix or improving customer satisfaction.
  • Provide data driven services, such as remote monitoring and predictive maintenance.
  • Introduce new business models, such as pay-per-use.

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.

Key challenges

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:

  • Data completeness - whether all relevant data is available. For e.g. you need to ensure that all Installed Base objects in the system are serviced or that related information needed for the servicing is available.
  • Data accuracy. The accuracy of data is poor if it is incorrect and does not represent reality. For instance, the serial number of an Installed Base object may be registered incorrectly.
  • Duplication of data - for e.g. the same customer account is registered in the systems multiple times. This must be avoided otherwise how will you know which version is correct and up to date? Duplicate data can also mess up your statistics.
  • Data needs to stay current. If, for e.g., the contact person for an installed base object at a customer location no longer work for that company, then their information is of no use. This also implies that details of the new contact person must be obtained and registered.
  • Data should be consistent across different systems and data sources. If the configuration of an installed base object in the ERP system is different from the configuration in the FSM application, which configuration is correct and should be used?
  • Data as a whole should make sense. This is called Data Integrity. For example, if the installation date of an installed base object is inputted as a future or past date, this is likely incorrect. It also doesn’t make sense to have an installed base object that isn’t related to a customer or is related to multiple customers.

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:

  • No up-to-date master data model
  • No data management processes in place
  • A robust and flexible data integration architecture has not been implemented

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:

  • When planning an appointment, it must be in the future; dates in the past are not allowed
  • When creating a work order, some fields will always be mandatory. For e.g. the description of the problem, the affected installed base object, the severity and the customer contact person.

To improve existing data, you could consider the follow tactics:

  • Deduplicate data - this can be done by creating rules that define when two or more records can be considered a duplicate. Automate the merging of the two.
  • Validate data accuracy - this is rather challenging to do in an automated manner. But for public data such as addresses, it is possible.
  • Do a completeness check by re-defining the mandatory fields for each object that need to have a value; and also check for records that have missing data. Completeness of data sets can be done by counting the number of records and comparing the number to known statistics.

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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