All Things Data

Master Data Management and the Multiple Match Project Uses for the Mid-Market

I was speaking with Steve LoCastro from BDS and one of the things that he wanted to point out was how many different uses a Mid-Market organization can get in a single Master Data Management match project.
 
Mid-Market Master Data Management solutions are usually driven by one specific issue.  We can’t get certain reports, we want ours reps to have a single customer view to up-sell and cross-sell, we have data quality problems, or other issues.  Typically, however, once the Master Data Management system is starting to be put in place, the Mid-Market firm starts to realize that they can gain additional benefits that they never foresaw when the MDM project was first authorized.  Sometimes, the “secondary” benefits become as important as the initial objective.

For example, a Mid-Market organization may have two databases that they want to synchronize such as a Communication System (like Outlook or Lotus Notes) and a CRM system.  The challenge with synchronizing these separate data sources is that there are multiple representations of the same or related organizations and contacts within these two systems.  The different ways that these multiple representations have been entered in to these separate data silos, such as abbreviations, incomplete information, spelling mistakes, etc., makes the synchronization even more difficult.  Therefore, in order to sync these two systems up, a Master Data Management match focusing on Customer Data Integration needs to take place.

Once complete, the organization is now able to tell from the Master Data Management match project which records are in both systems and which records are in one system and not the other.  With this information, the synchronization process can then be put in motion. 

Additionally, by changing the way that the same Master Data Management match project is queried, information such as duplicate records, missing data elements, and other data quality issues can also be addressed on the same records at the same time the company is benefitting from the syncing of the two systems which was the initial goal.  If there is other valuable reference data, data that is part of the data sets that are being matched but are not used in the match itself, then additional analytics can be performed, such as marketing analytics, all from the same Master Data Management match project.

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.

Master Data Management and the Mid-Market Need

Bob Bloom, President of Business Development Solutions, recently got back from the MDM Summit Fall 2008 in New York.  MDM stands for Master Data Management. 

While our company primarily focuses on working with mid-market companies, the conference was mostly focused on larger organizations.  Still, Bob wanted to attend in order to see what was going on in the MDM world for larger enterprises and see how these practices can be applied to mid-market organizations.

One of the initial impressions that I got from Bob regarding the MDM Summit is that mid-market companies have the same basic drivers for master data management applications as larger enterprises:

1. Analytics – The ability to query across multiple databases and properly count the multiple representations of the same organization and contacts that are in separate data silos.  Large and mid-market companies have the same issues getting a handle on questions such as  “How much business are we doing with XYZ?”.
2. Single Customer View – The need for representatives to see all the authorized information about an account across multiple systems in order to market, sell, and service customers better.
3. Corporate Hierarchies – The need to gain further “share of wallet” with an account by working the corporate family tree in order to up-sell and cross-sell.
4. Data Quality – Identify and resolve various data quality issues such as incomplete data and duplicate records.
5. Mergers and Acquisitions – Understanding the unique and overlap after acquiring a new business and properly incorporating the legacy data into the exiting or brand new system, including organizing corporate hierarchies. 
6. Data Consolidation and Building New Systems  - Similar to mergers and acquisitions, when an organization adopts a brand new database system, such as a CRM system, many times the data is coming from multiple files and needs to be matched, linked, and organized for the new system.
7. Government Regulations – Many organizations have certain data management issues in order to comply with government regulations.

The big differences between the mid-market and larger enterprises are the scale of the project, the time frame to implement, and the cost of the implementation.

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.

The “Right” Count and Master Data Management

As the economic situation remains uncertain, many organizations have begun to downsize their staff or are thinking about downsizing their staff. 

In many organizations, sales expenses can be the most costly part of an organization making it a tempting area to cut. Still, at the same time, the lifeblood of any organization is its sales.  How does an organization reduce its sales expenses without actually hurting the organization more by not having the appropriate staff to find and follow up on the opportunities?

The key to making any decision regarding the number of sales employees is having a clear understanding of the market opportunity.  Now, more than ever, Master Data Management, also referred to as MDM, is needed in order to make these critical executive decisions.

Master Data Management can help you match and link the multiple representations of the same organization, same contact, and related organizations and contacts that are located within each separate data silo and across disparate data systems.  Once matched and linked, you can see how many customers and prospects that you have within your database systems.  You can further analyze this information by geography, product line, or other criteria or combinations of meaningful criteria to further define the market opportunity. 

To gain even further insight into the market opportunity, you can use your MDM solution to match and link external marketing files against your internal databases so that you can identify locations and contacts that match your customers and prospects that you do not already have within your database.  External files can also provide organizations and contacts that fit the profile of your best customers even if you do not have matching records in your data silos.

Once you understand the market opportunity, you can start to plan for the “right” count of employees based on the market opportunity and not some random reduction of head count which may hurt sales.  Since your best customer is your current customer, a master data management application can also help organize your databases based on corporate hierarchies so that your sales and marketing staff can work the family tree where your organization is already a trusted source.  

While for many large enterprises implementing a master data management solution may take some time to implement, many smaller and medium size organizations can implement a MDM system in a reasonable amount of time and gain more immediate insight in order to determine the “right” count of sales employees sooner rather than later.

Excel Pivot Tables Revisited

Many of our clients have sophisticated tools and expertise in-house in order to analyze and report on data.  Therefore, these clients engage us so that we can help integrate their data from separate data sources in order to develop a single customer view.  Once they have a customer data integration application in place, they can analyze and report on their separate data sources using their single view of the customer.

However, we are also seeing more and more clients who are not only asking to implement a customer data integration system and develop a single customer view, but are also looking to us to help analyze and report on their data integration.  Sometimes, these clients do not have the internal personnel and/or tools to analyze and report on data.  Other times, they may have the tools, but lack the expertise on how to use those tools.  Further, there are times when the client does have the internal expertise and the tools, but those resources are not available.  Therefore, I thought it would be a good time to revisit the subject of Excel Pivot Tables.  We have been using Excel Pivot Tables to assist clients with their analysis and reporting when they do not have internal expertise available.

I spoke with Steve LoCastro from BDS to get a sense of what he likes about Excel Pivot Tables and what are some of the limitations.

Steve stated that Excel Pivot Tables are basically a front-end interface that enables you to build a query in a similar fashion as Access in order to develop a report.

The things that Steve likes about Excel Pivot Tables are that they are: 

1. Simple to use
2. Easy to set-up
3. Easy to change

However, Steve also pointed out some of the limitations of Excel Pivots Tables.

First, if you want to report on any information that needs to be derived, this needs to be developed outside of the Pivot Tables.  For example, if you want to report on how old someone is and your data provides the date of birth, but not the actual age, this would have to be derived outside of the Pivot Tables.  Therefore, we typically need to write an application in order to build a data framework in order to derive certain pieces of data that need to be analyzed before using the Excel Pivot Tables.

Another major limitation is that the Excel Pivot Tables are best suited for looking at one query or question at a time.  If you want to know how much revenue that you have, that may be one query and one table.  Then, if you want to look at how many sales by representative, by region, by product line, or other variables, the Excel Pivot Tables are best suited to look at each one of these variables separately.  Other more sophisticated tools may give your end-user the option to take a look at the general question regarding your revenue and then keep drilling down further to look at different slices of revenue.  Of course, these other sophisticated tools are more expensive, require more expertise to set-up, and require more time to develop before the end-user can start drilling down on these variables.

Utilizing Customer Data Integration To Do More With Less

Here are 3 ways that utilizing customer data integration in order to build a single customer view can help your organization do more with less:

1.  Your best prospect is your current customer.  Customer Data Integration can help you correlate records in your databases that are part of the same organization.  This can enable you to enhance marketing to your existing customer base in order to gain a greater share of wallet with your current customers.  The built in referral from already having done business with another part of the organization shall give you a greater chance of success and be less costly than other outreach efforts.

2.  Allocation of resources – How many unique customers do I have?  How many unique prospects do I have?  Where are my customers and prospects located?  Which industries or other marketing segments are my customers and prospects in?  With so many representations of the same organization and so many different locations for these organizations in your databases many times the answers to these questions are not so simple.  However, customer data integration can help create the organizational hierarchies and enable you to properly count your customers and prospects based on the segmentations that are meaningful to you.  With this information, you can properly allocate the correct number of employees and other resources that are needed to market and service these segmentations.

3.  Market Consolidation – Maybe you are in a position to acquire a competitor or to take on a complimentary organization that adds to your offerings.  In order to get the true benefit of the merger, you need to consolidate the other organization’s data into your systems so that you can capitalize on the opportunity that is created by the merger.  Customer data integration can be used to help you identify the unique and the overlap among the organizations.  As mentioned above, a CDI match can help you create the corporate hierarchies of the customer and prospects of the two combined organizations so that you can maximize the opportunity created by the consolidated organizations as well properly allocate your resources so that you get maximum return on your investment.