All Things Data

Master Data Management and the Top 10 Business Priorities in 2009 (Part 1)

I was recently referred to the Gartner EXP Worldwide Survey of more than 1500 CIO’s by a newsletter from DMReview.

According to the survey, IT spending in 2009 is expected to be flat as companies are investing in some areas and pulling back in others.  Further, according to the survey, the Top 10 Business Priorities in 2009 are:

1. Business process improvement
2. Reducing enterprise costs
3. Improving enterprise workforce effectiveness
4. Attracting and retaining new customers
5. Increasing the use of information/analytics
6. Creating new products or services (innovation)
7. Targeting customers and markets more effectively
8. Managing change initiatives
9. Expanding current customer relationships
10. Expanding into new markets and geographies

The overall theme of the survey is that CIO technology priorities will concentrate on gaining more value out of existing assets.

As the purpose of Master Data Management and Customer Data Integration is to help organizations realize more value out of its existing data assets, I thought it would be a good idea to discuss how CDI/MDM fits in with The Top 10 Business Priorities in 2009.

Today, I will focus on a couple of the Top Business Priorities for 2009 and how they relate to CDI/MDM and tomorrow I will finish commenting on the remaining Priorities.

Priority #1 Business process improvement

The other Priorities in the Gartner Survey are specific areas of Business Process Improvements.  My comments on these specific areas address how CDI/MDM software’s ability to properly count and to utilize information from multiple data sources can play an important role in determining what business processes to improve and how to go about doing so. 

Priority #2 Reducing enterprise costs

I recently posted a blog entry, Master Data Management and Rightsizing the Business, on this very subject.  In summary, matching and linking using a CDI/MDM solution’s numbering schema is the key to generating proper counts, such as number of customers and prospect, and querying information from separate data sources.  This information can help determine the maximum expense consolidation to manage profitability. (How many sales reps, service personnel, service locations, etc. do I need?)

Priority #3:  Improving enterprise workforce effectiveness

In a previous post, I had written, Increase Effectiveness with a Common ID.  In summary, CDI/MDM systems enable reports using information from separate data sources that are more accurate with proper counting.  This enables better planning and decision making.

Priority #4:  Attracting and retaining new customers

CDI/MDM can help attract and retain new customer in several ways.  Here are just a couple of examples:

1. If the prospect is somehow related (subsidiary, division, other location, etc.) to an existing customer, the system can link the prospect with the current customer enabling sales and marketing to use the existing relationship to help with the new sale. 

2. If the prospect has had contact with customer service, the solution’s Single Customer View will enable the sales and marketing team to see the information that is correlated with the prospect and use it in order to help close the sale. 

3. Analysis across multiple data sources can help understand your customer profiles and trends to develop programs and campaigns to obtain and retain customers.

Master Data Management, Corporate Hierarchies, and Wikipedia

No matter how good a Master Data Management matching process that you have, there needs to be some commonality in the data for records to match.  If two records have different company names, addresses, phone numbers, website addresses, or other information that may be used for matching purposes, then there is not any way that your Master Data Management match project can bring these two records together based on an automated match. 

For example, DePuy Orthopaedics, located in Warsaw, IN, is a subsidiary of Johnson & Johnson.  Ethicon, located in Somerville, NJ, is also subsidiary of Johnson & Johnson.  However, if you do not have any information in your databases indicating that these organizations are related, it is going to be impossible to put them together with your Master Data Management match.  Consequently, if someone wants to know how much business the company is doing with Johnson & Johnson, they are not going to get a complete answer.

If you want to put corporate hierarchies together with subsidiaries that do not share any common information, then you are going to need an outside source.  An external marketing file that contains corporate hierarchy information can be a great resource.  However, sometimes, you only need to create hierarchies for a relatively small sample set.  Such as, if you want to answer the question, “Who are our top 20 customers?”, you do not necessarily want to purchase an external list just for this purpose.  Instead, you may want to research the top 20 – 50 companies from your Master Data Management automatic match and then augment the results with manual research.

Of course, a company’s website is a great place to start.  However, sometimes a company website does not layout the corporate hierarchies in a manner that is easy to research.  Therefore, we have found that one of the best places to research corporate hierarchies is Wikipedia.  Many times Wikipedia can lay out corporate hierarchies in a fashion that is quicker and easier than a company website.  Sometimes, it is more complete.  Also, sometimes a Wikipedia entry will provide history.  So if your data includes a record for a company whose name has been changed after an acquisition, sometimes Wikipedia may have this information where the company website may no longer include this.  Of course, Wikipedia is not verified, so it’s important that you double check any findings on Wikipedia before incorporating it into your Master Data Management match. 

Master Data Management, Precision Matching, and the Mid-Market

Another point of discussion during the MDM Summit was how precise does the matching have to be in order to have a successful Master Data Management solution.  And the answer, of course, is it depends.

First, there are some differences in the practicality in matching between consumer and business to business Master Data Management systems.  With consumers, you may be able to get a higher match results if you have social security numbers.  With B to B companies, in addition to not having a social security number, you are also dealing with the complex nature of corporate hierarchies, multiple locations, mergers, and other factors making the matching more complex. 

Second, the purpose of the Master Data Management solution is also going to play a big factor in the level of preciseness needed.  If your Master Data Management system is for something like HIPAA compliance, then you are going to need to be very precise since you are dealing with health and privacy issues.  On the other hand, if your Master Data Management application is primarily used for marketing and analysis, it may be acceptable to have a somewhat lower match rate.

When we are working with B-to-B Mid-Market organizations, the primary drivers of Master Data Management solutions are developing a singe customer view for up-selling and cross-selling, a single version of the truth for marketing and other analytical purposes, and data quality.  With these goals in mind, Mid-Market firms need to see value right away.  Therefore, we try to match and link the separate silos of data in a relatively short-time in order to provide immediate impact.  A 70-80% match result is still better than the 0% match result that existed before the Master Data Management project.  Then, manual resolutions can be invoked if greater precision is needed.  Further, the Master Data Management system can be used to tackle the Data Quality issues both during the MDM implementation and continuing on an on-going basis after the “go live”.   However, most Mid-Market B-to-B firms looking to develop a single version of the truth for sales and marketing analysis and action do not have to wait until their data is “clean” before gaining the benefits of a Master Data Management system.

Master Data Management, the Single Version of the Truth, and the Mid-Market

Another big topic at the MDM Summit was the concept of using Master Data Management in order to build a Single Version of the Truth.  There seemed to be a consensus with large enterprises at the Master Data Management conference that there should be a separate database to act as the central data repository in order to view and analyze the Single Version of the Truth.  With large organizations having so many data sources and not necessarily a single system of record, it makes sense that larger enterprises use a separate database as their Master Data Management central repository for analytical purposes. 

In the Mid-Market, many organizations also use a separate central repository to create the “Golden Rule” record just like larger enterprises.  This is a record that the Master Data Management solution creates based on the best information in separate data silos and is only for analytical purposes.  When using a separate repository for analyzing the Single Version of the Truth, the “Golden Rule” record is not disseminated into any operation systems as there may be good reasons to keep the information in the operational systems exactly how it was originally entered.  Instead, this “Golden Rule” record is used to help provide crisp and clean reports. 

However, we also have Mid-Market clients who use a Master Data Management application to match and link separate data silos to help populate an operational system that becomes their Single Version of Truth.  For example, a Mid-Market organization may choose to use their CRM system as their central hub in order to view and analyze their Single Version of the Truth regarding their clients, prospects, and centers of influence.  Since a Mid-Market firm’s Master Data Management scope may be more limited than larger MDM initiatives, (less data, data sources, and data types) this approach may be reasonable for some Mid-Market firms. 

Master Data Management and People, Processes, and Technology in the Mid-Market

As I continue my conversations with Bob Bloom about his experience at the MDM Summit, one of the concepts that Bob heard over and over again at the conference is that a successful Master Data Management initiative involves the confluence of people, processes, and technology.  While the MDM Summit was mainly focused on large enterprise organizations, it has been our experience that this is equally true for the Mid-Market Master Data Management systems as well.

People play a vital role in any successful Master Data Management application.  The client needs someone who has a vision as to what they want to accomplish, has an understanding of their internal data and data systems, and has the time to dedicate to the MDM project.  This could be one person or this could be a team of people.  It has been our experience that the client needs one person who “owns” the Master Data Management system during the implementation as well as on an on-going basis.  Then, there are others who can assist the company’s appointed Master Data Management specialist.

A good process is also fundamental to developing a Master Data Management solution.  Here, this is one area where the client will typically need to look to an outside Master Data Management partner.  Typically, an organization does not have experience in developing a Master Data Management system.  Therefore, it is best to work with an outside organization that has been through the MDM territory and can provide guidance on the Discovery, Design, Prototype, and Implementation of a Master Data Management application.

Finally, the last piece in the puzzle is the tools.  Master Data Management tools can vary greatly in data types that they are best suited to handle, time needed to configure systems, and the specific features that each system has developed to handle Master Data Management.  Of course, there can also be a great difference in price as well.  Regarding the Mid-Market, it’s important to use a tool that can meet your objectives without being overkill so that you can get a return on your investment in a reasonable amount of time.

Master Data Management and the Mid-Market Process

In addition to speaking with Bob Bloom about the Mid-Market need as stated in the last post, I also spoke with Bob about the process to implement a Master Data Management Solution.  Basically, the MDM Summit in New York confirmed that the process to implement a Master Data Management initiative is generally the same regardless of whether the organization is a Mid-Market company or a larger enterprise.

The steps for implementing a Mid-Market Master Data Management solution are:

1. Discovery – Understand the project objectives, data, systems, timeframes, personnel, and other items related to a successful Master Data Management project
2. Design – Spec out the system and process for the Master Data Management system
3. Prototype – Develop a working Master Data Management model for testing and adjustments
4. Implementation – Install the adjusted Master Data Management prototype in the live environment. 
5. Maintenance – Support the live Master Data Management system from both a technical perspective and a user perspective
6. Repeat – Expand the functionality of the original Master Data Management solution by using a phased approached to take the MDM application to the next level

The main differences in process between a Mid-Market Master Data Management system and larger enterprise solutions are scale, scope, and timeframes.

Large enterprise Master Data Management solutions may be dealing with many more data records and many more data sources than a Mid-Market MDM solution.  Consequently, the Discovery for a large enterprise MDM Project may be longer than the entire process, Discovery through Implementation, for a Mid-Market MDM system.

Further, a large enterprise may want to include many different types of data, such as customer, employee, product, facility, vendor, and other data types in their Master Data Management project.  Mid-Market firms may be more focused on a specific data type for their Master Data Management solution.  For example, a Mid-Market company may have thousands of customers, prospects, and contacts and organizations related to these customers and prospects.  Therefore, a Mid-Market firm may be focused on a Customer Data Integration application to manage their customer and prospect data.  Customer Data Integration, also known as CDI, is a sub-set of Master Data Management since it is only managing one type of data.  However, larger enterprises many have thousands of employees, products, and other data sets in addition to customers and prospects that need to be managed as part of a MDM solution while a Mid-Market firm may not have enough employees, vendors, or other types of data that need to be included in a Master Data Management system.

Commercial Insurance Brokerage Customer Data Integration Case Study Published

I wanted to let you know that our company, Business Development Solutions, Inc. ("BDS"), recently announced the publication of a customer data integration case study outlining its implementation of SingleVision® software for Conner Strong.

Conner Strong, the 18th largest privately held brokerage and 31st largest insurance brokerage in the country, is a leading provider of property and casualty insurance and employee benefits products and services.

To view the press release, please click here: http://www.prweb.com/releases/customerdataintegration/singlecustomerview/prweb1512644.htm.

To read the case study, please go to our website bdsdatabase.com.

Customer Data Integration and Contact ID, Customer ID, Account ID, and Client ID

In speaking with Steve LoCastro from BDS, Steve pointed out that many companies have “Native Intelligence” in their data sources.  Native Intelligence is information in the data that indicates that separate records have some relationship with each other.  The native intelligence could indicate that the records are part of the same corporate family, indicate the hierarchy relationship of the records, or some other relationship.  Many times these relationships can be indicated in the data’s Contact ID, Customer ID, Account ID, and/or Client ID.  An external marketing list also might have data that indicates the relationships of separate records.

In developing a single customer view, Steve suggests that the data should be examined in order to find any information that could be used as native intelligence.  Further, the use of native intelligence is not something that should be looked as independent of a customer data integration solution.  Instead, a company’s native intelligence is maximized if it is incorporated into the customer data integration match. 

Combining CDI matching algorithms with native intelligence will enable records to be matched, linked, and coordinated by their corporate hierarchy relationships to develop a single view of the customer that could not be otherwise presented if these two processes were completely independent of each other.

Top 8 Steps to a Successful Customer Data Integration Solution

Here are the top 8 steps to a successful customer data integration solution:

1.  Top Management Commitment – Without top management being on-board, the project will go nowhere.

2.  Dedicated Project Leader – Someone who sets aside the time and has the skill to manage both internal resources and work with your external partner.

3.  Clear Goals – Define the objectives of the implementation.

4.  Phased Approach – Do not try to tackle everything one time.  Set priorities and work toward reaching your goals and then add additional goals once the initial ones are met.

5.  Set Time Line – Put stakes in the ground regarding expected deliveries.

6.  Project Flexibility – No matter how much you plan, there will always be things that come up that cannot be anticipated.  Be flexible enough to work through these issues.

7.  Experienced Partner – Work with someone who has been through the territory.

8.  Appropriate Tools – Use a Customer Data Integration software solution that is suitable for your goals.

Customer Data Integration Matching

I recently spoke with one of the members of our team, Steve LoCastro, about the different types of matching for a customer data integration application.  Steve is one of the SingleVision developers and also is a key member in working with our clients in developing their single customer view.

In speaking with Steve, he stated that in order to develop a complete single view of the customer, you need the ability to modify matching criteria based on the specifics of the data. 

For example, if you are matching 2 internal files, such as a CRM system and an accounting system, the match rules are going to be different than if one of the files included external information such as a marketing list.  When the files are fairly homogeneous, such as only internal files, you may be able to use fairly loose rules.  When external files are included in the mix, then you mostly likely will have to tighten your rules.  Otherwise, you will get false positives. 

The key to a successful customer data integration match is to be able to review the data, understand the objectives, and have the ability to craft the rules based on the data and objectives.