• Measuring and resolving data quality issues
• Applying the existing enterprise security framework to the new MDM system and assigning data-level access based on authentication rules and policies
• Creating business processes for composite transactions which include the organisation and perhaps its clients, vendors, suppliers etc
• Creating and maintaining a metadata repository
• Scalability with respect to data volumes, entity types/subject areas (customer, product, vendor, supplier etc) and data types (structured and unstructured data)
• Data synchronisation amongst the MDM data hub with the consuming and producing data sources and targets, respectively

To achieve this organisations start small with a small, but specific high-priority requirement such as to create customer profiles or resolve data quality issues. To do this in-house development is used and existing resources such as legacy systems are leveraged. For instance, BI tools may be fed more complete and accurate data so that a better and close effectiveness of the effort can be measured.

As the small project proves the PoC, may data sources and consuming systems are added. This helps to build more accurate, complete customer information by which further benefits are realised such as new customer segments are identified, revenue is increased etc. Eventually, it will allow organisations to transition from an account-centric to customer-centric business model as part of a larger enterprise transformation program.

From a technology perspective, the small project will move on to the stage where commercial vendor tools are evaluated and implemented to develop the CDI project further and gain further benefits. Many enterprises want a smooth transition therefore take a SOA-based approach and even apply SOA to their small project. The small project eventually gets replaced by a vendor MDM tool with SOA capabilities and will be integrated with other SOA infrastructures, common messaging framework, common process management and even the enterprise security framework. Ultimately, the CDI data hub will become part of the enterprise architecture and infrastructure.

Reducing administrative process costs and inefficiencies

Another reason why organisations implement CDI solutions is to help reduce the administrative time spent on managing customer accounts. It should be possible to e.g. use existing customer information rather than having to re-enter it as part of the various interactions a customer has with the business. The reason why this cannot be done is maybe because it is too difficult to find the right customer profile details.

CDI integrates customer information and relationships into a single repository and thereby overcomes the issue.

In addition, CDI solves data quality issues and human data entry errors, but more importantly allows to comply to governance and compliance requirements.

Reducing IT maintenance costs

CDI also enables the reduction in IT maintenance costs such as the infrastructure. A system such as CDI which is flexible and agile such that it can adopt quickly to changing business requirements and processes and regulatory compliance requirements is a major business driver for acquiring a CDI solution. Previous systems such as CRM and EDW failed because these systems did not deliver timely, accurate or complete views of data which CDI – and more broadly MDM – solutions deliver.

This ends the explanation of all the major benefits of CDI and how it can benefit organisations independent of their market sector.

Improving customer service time through just-in-time information availability

CDI allows enables the delivery of complete and accurate information in or near real-time. If information is unavailable in a timely manner then this could affect customer service and therefore customer retention. If a Customer Service desk says that there “systems are slow” or “we don’t have access to the marketing systems” then what they are really saying is that they don’t have the information with them now or can’t get it quickly enough, because their systems and CDI information sits across disparate systems and applications.

Nowadays businesses want cross and up-sell to happen in real-time which CDI allows to do. It is not good enough anymore to run a batch job over night. If things like customer analytics and fraud detection can be done in real-time then cross and up-selling can be done, too.

CDI integrates information from several data sources which allows to build a customer behaviour model. Using this it can provide accurate recommendations to customers while they are e.g. still in store or browsing the website. For instance, a family who just moved to a new area are now in a store. A sales person should be able to provide appropriate recommendations to the family by having an integrated solution of different data sources such as customer profile, household information and buying pattern. Therefore CDI allows cross and up-sell to happen in real-time.

Improving marketing services

Marketing companies use CDI to improve the effectiveness of marketing campaigns and therefore increase the chances of cross and up-sells. CDI can deliver some very impressive results in achieving this which also reduces the costs in running marketing campaigns.

Marketing campaigns tend to be similar across industries but there are also industry specific campaigns such as the “Communities of Practise” in the pharmaceutical industry where thought leaders can be targeted as part of campaigns rather than literally millions of people having some kind of relation to the industry.

This ends the explanation of another two benefits CDI delivers. The next blog post will be the last one as part of this series of posts and will be published early next week. So watch this space!

Growing revenue by leveraging relationships

Customer loyalty is now more difficult to achieve given the increase in competition in any product or service. Financial services organisation understand this and therefore instead of growing their success by acquiring new customers instead they are focusing on understanding the relationships a customer has with their organisation. Therefore companies have to acquire new customers, understand them and then manage the relationships they have with the enterprise.

Understanding and leveraging relationships is something that CDI can help with and then this can be used to increase revenue by offering additional services customers may be interested in.

The strategy is to use business intelligence to perform customer analytics and predictive analytics on customer data in order to identify the “best” customers and then find out what should be offered to them to increase the share of wallet.

Relationships analysis can help to understand the relationships a customer has with the organisation as well as other customers associated with the organisation. For this data must be pulled from various sources which is what CDI can assist with.

Improving customer retention and reducing customer attrition rates

For a company to grow it needs to identify the customer segments which offer the highest growth potential. This should be done by focusing on high loyalty from customers to maximise revenue derived per customer. In order to grow customers segments should identify new high revenue customers and existing customers where maximum revenue potential has not been realised yet.

New customers should be acquired with minimum acquisition costs and existing customers should be retained also with minimal retention costs. Acquiring customers is obviously more expensive can be difficult e.g. due to customers perceiving the website as difficult to use, limited opening hours, awkward locations to reach etc. Hence it’s easier and cheaper to grow if the focus is more on existing customers.

Business intelligence used to perform analytics and customer data mining can be very useful in achieving this benefit.

So this ends the explanation on these two additional benefits CDI has to offer. In the next few days another blog post will explain two more benefits of CDI.

This blog post will explain the first two benefits of CDI; gaining competitive advantage and increasing customer experience.

Gaining competitive advantage

Traditionally CDI projects started off as small pilot projects at departmental level by building custom-built solutions. These then gained some exposure to other departments within the organisation which then led to an increase of appreciation of CDI. Eventually, this led to the department being given the clear to go for a bigger and better commercial product.

CDI projects should not be started unless there is a strong business case. It should come with measurable benefits and the ROI should be achievable within a reasonable amount of time. CDI projects require strong organisational commitment, involve high costs (software licence and implementation), take a long time, come with risks and have exposure at enterprise level. Hence it is important to have a solid business case for it which will give the organisation a competitive advantage.

One thing organisations can do is look at their largest customers and then group them based on relationships. Through this approach organisations can manage the customer lifetime value, make marketing campaigns for targeted and effective, increase customer service and reduce customer attrition.

Increasing customer experience

It is good for an organisation to have as many loyal customers as possible. In other words, people who have a strong relationship with the organisation who are not going to take their business to a competitor very quickly. By having a system which gives a full picture of customers would allow to achieve this by facilitating to offer a personalised service and good level of customer service.

If you have kind of investment funds you might be allocated an account manager. If you call up the company and request some information and the company has to e.g. get the information from different people while you are waiting on the phone or ask you to contact them the next day then this shows the company lacks an integrated system of customer information.

Similarly, a customer having several investment accounts may request a certain chargeable service. Most companies would be willing to waive the fee if they know the importance of the customer. However, if the organisation does not have a CDI system showing relationships of customer to accounts then it may not be able to see that the customer may have 5 different accounts all above a certain limit where the company can offer some “treats”.

These examples show how CDI can benefit organisations by offering increased customer experience and reducing customer attrition. The next blog post will be published in the next few days and will explain some further benefits CDI has to offer.

The challenge with this type of data is that it is natural for information to change over time. For instance, people move to new places, peoples’ name changes typically after getting married etc. The other reason is that due to typo errors the data must be corrected/changed.

Even small information can be very complex. For instance, names consist of a title, forename, surname, middle name, professional titles, nicknames etc. The address details of a person can be very different from country to country. Addresses consist of flat or house numbers, floor number, house names, street name, county, state/region, country, postcode/zip code etc.

Imagine a company with millions of customers. There is a statistic which says that every month a customer’s information becomes redundant due to marriage, moving place, dieing etc. So a company with even just a million customers deals with about 20,000 customer data changes every month. That is a pretty big number and clearly has a need for CDI.

Overall, there are five conceptual factors which make customer data management particularly challenging:

1. Accuracy
2. Latency: Customer data changes are delivered too late
3. Completeness: Not having complete customer information available in (near) real-time means that the right business decisions and analysis cannot be done
4. Management: Improper data integration, data governance, data stewardship and data synchronisation will decrease data quality levels
5. Ownership: The more data owners, the more difficult it is to fix data quality issues in the customer data

An important point, which is often neglected after a MDM implementation has been completed or is in operation, is to keep profiling the data on a regular basis to ensure that data quality issues are not reintroduced and kept at the lowest level possible.

Generate and test the master data

In this step the master data is taken from the source systems, various ETL routines are performed on the data and then loaded into the MDM data hub. This is often an iterative process, because the transformation, linking and merging rules may need to be refined and the results analysed to ensure it meets the requirements. Based on the result, further tweaking may be needed.

For the linking and merging, MDM tools can be configured to perform e.g. auto-linking using a weighting algorithm. For instance, if the computation results in a confidence factor of 90% or more then the MDM system can auto-link the two records it is comparing. If the confidence factor is below 75% then the MDM system should not link the two records, but treat them as separate. In case the confidence factor is between 75% and 90% then it should be inspected manually by a data steward.

Modify the producing and consuming systems

Depending on how the MDM system has been implemented, it may be necessary to change producing, consuming and maintenance systems of master data. If the master data is used by a system other than the source system then such system may not need updating. Either the source systems need access to the master data or the MDM system must be configured such that it sends master data to the source systems. Source systems which generate new master data must include a check which checks whether the master data is perhaps already in the MDM data hub to avoid creating duplicates.

Implement a maintenance process

The last step is to implement a maintenance process consisting of tools and processes which ensure that the master data is kept clean and maintained post-implementation. This will include e.g. data stewards who will keep an eye on the quality of data and proactively resolve any data issues. A good MDM tool would have the capability to identify and highlight potential data quality issues to data stewards.

This is also where versioning capabilities within the MDM tool come in handy. In case of data issues, the data stewardship should be able to see how master data evolved over time and if necessary revert a master data value to at least the previous state.

This is the end of the process. Overall, MDM is a long journey and should be implemented incrementally. This way benefits can be shown to the business and short-term benefits can be derived from the efforts and financial investments.

Implement a data governance program and data governance council

The data governance council should be assembled from people who know the data and have the authority to decide to decide what should be stored in the master data, for how long, authorise changes to be made to any of the master data etc.

Develop the master data model

In this step, you decide what attributes should form part of the master data, what data types, restricted values lists, default values, data types etc. Also create a mapping sheet from the source systems to the MDM system which will explain how the attributes can be later on translated during the implementation phase. It is worth noting note to aim to please every stakeholder in this process otherwise you will end up with a large set of master data attributes making the master data very complex.

Choose a MDM tool

Select a MDM tool which will help to collect, transform, standardise, match, link and merge data and then store it into the MDM data hub. You may also need to purchase additional tools for maintaining and using the master data.

You can either select a single tool which is capable of handling multiple domains or choose separate tools i.e. best-of-breed. Even though there are multi-domain MDM tools available on the market, it is probably best to go for the best-of-breed MDM tools especially if the requirements are complex.

When selecting a tool, ensure that it has the capability to identify and resolve data quality issues. In addition, it should have hierarchy management and versioning/history features.

Design the infrastructure

Once you have configured the MDM tool, it is time to set up processes which will use and maintain the master data after the MDM solution has gone live. This may involve changing existing systems and IT infrastructure. The infrastructure will have a dependency on the MDM system and therefore reliability and scalability are additional factors to take into account when deciding on which MDM tool to purchase.

So these were a few more steps involved in the process. The remaining steps will be explained in the next blog post which will be published in the next few days.