MDM – A Geeks Point Of View http://www.mdmgeek.com A Techie Tech Blog on Master Data Management And Every Buzz Surrounding It Tue, 09 May 2017 03:23:46 +0000 en-US hourly 1 https://wordpress.org/?v=4.7.5 31004367 Party Data Model in Master Data Management http://www.mdmgeek.com/2017/05/08/party-data-model-in-master-data-management/ http://www.mdmgeek.com/2017/05/08/party-data-model-in-master-data-management/#comments Tue, 09 May 2017 03:23:46 +0000 http://www.mdmgeek.com/?p=1252 Many MDM initiatives center around customer data. A standard definition used in the industry is “Party” and “Party Domain” is a shared phrase used amongst MDM practitioners. Data model design around party domain is a critical area to address during MDM. In this blog, I share my observations and suggest best practices. Party usually has […]

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Many MDM initiatives center around customer data. A standard definition used in the industry is “Party” and “Party Domain” is a shared phrase used amongst MDM practitioners. Data model design around party domain is a critical area to address during MDM. In this blog, I share my observations and suggest best practices.

Party usually has two subtypes – person and organization. Person and organization parties have many characteristics in common:

  • They both have one or more names.
    • A person has an official name, nickname, alias and/or pseudonym.
    • An organization has a legal name, trade names and/or trademarks.
  • They have one or more internal or external identifiers.
    • Social Security Number (SSN) is a 10-digit identifier in the US for individuals.
    • Employer Identification Number (EIN) or Federal Tax Identification Number is used to identify a business entity. It can also be DUNS number supplied by Dun & Bradstreet
  • They usually have an associated location (shared among parties).
  • They have a communication method which can be a phone number, email address; Twitter handles and more.

One of the important factor to consider during an MDM tool selection process is the flexibility offered by the tool to support different ways in which we identify a Party. I have seen various names used to refer a Party in the data model. The usage depends on the industry and the use case. Ex: Customer versus Prospect in a marketing analytics use case.

I would like to list few variations I have seen while working with customers

  • Person: Customer, Patient, Doctor, Member, Citizen, Client, Person, Signer, Authorizer, Insured, Agent, Character, Prospect, Student, Contact, Guest, Staff, Licensee, Tenant, Lessee, Player, Counter Party, Employee, Employer, Job Candidate, Judge, Owner, Death Master
  • Organization: Wholesaler, Distributor, Dealer, Organization, Trading Partner, Law Firm, Vendor, Supplier

Data Model

MDMGeek’s Point of View:

MDM adoption is a huge challenge because it brings change to the way you manage your most critical data assets. It is important that we take these variations into consideration while implementing an MDM solution. You should look for a vendor tool that provides you the data model flexibility to define YOUR master data the way YOU call it – not how vendor suggests you to do.

You may have come across more ways Party is known. I love to hear your comments. Send me any feedback you have via comments. You can also reach me at @mdmgeek.

Also check out insights from @axeltroike, @hlsdk, @murnane and others in my Twitter community on the ideas related to data modeling.

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MDM Matching – Are You Asking the Right Question? http://www.mdmgeek.com/2017/04/17/mdm-matching-are-you-asking-the-right-question/ http://www.mdmgeek.com/2017/04/17/mdm-matching-are-you-asking-the-right-question/#comments Mon, 17 Apr 2017 22:36:28 +0000 http://www.mdmgeek.com/?p=1245 An odd request came in last week when a prospective customer asked us about a benchmark on the percentage of duplicates we can find for them using MDM. In this blog, I wanted to touch base on few key reasons why this is odd in many ways. I would also like to take this chance […]

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An odd request came in last week when a prospective customer asked us about a benchmark on the percentage of duplicates we can find for them using MDM.

In this blog, I wanted to touch base on few key reasons why this is odd in many ways. I would also like to take this chance to explain what are the right questions you should be asking to your vendor when it comes to MDM matching.

MDM Matching QuestionI have worked with dozens of customers directly in last 12 years. In my current role, I talk to companies implementing master data management, the practitioners and the thought leaders on a day-to-day basis. I can confidently say, every customer requirements around mastering are unique.

When it comes to identifying duplication of data in your organization, the discussion quickly changes to a customer’s specific requirements. The usage of the data (ex: analytics for marketing segmentation, real-time access to trusted data across the company, etc.), the number of sources and target systems, the quality of data in those sources are all different even for organizations within the same industry. Often the business requirements dictate what you need to do with data and there are instances such as legal and compliance when the requirements suggest certain duplicates must survive.

On top of this, there are project timelines, certain trade-offs the customer makes to achieve the level of accuracy, performance, and quality of the data. Think of adjusting several knobs on your stereo to get the best sound which YOU like.

Result? The percentage of duplication we can find using an MDM tool varies from customer to customer. It depends on several parameters, and you need to find what is right for you. Your tolerance level for false positives and false negatives dictates your configuration.

The real question you should be asking your vendor is –

  • How sophisticated is your matching engine?
  • Can it support probabilistic (fuzzy) and deterministic (exact) matching styles?
  • Is it easy to configure the matching engine? Is it easy to understand for my data stewards, IT and business users, so they are all on the same page?
  • How easy or hard it is for us to change tolerance level for missed and false matches?
  • Does the matching engine consider phonetic spellings, partial fields, the statistical distribution of records and more?
  • Does the vendor tool allow fine-grained tuning of the ranges to search, tightness of match and other parameters for balancing the degree of matches amount of processing (performance)?
  • How is a data set with international names and addresses handled?
  • Can the matching engine learn from past behavior from stewards and self-correct?
  • What about data survivorship? Does the vendor provide easy ways for us to configure survivorship rules?
  • Does the vendor take a configuration over coding approach for both matching and survivorship?
  • Can you provide attribute level survivorship?
  • Does the vendor offer scalability for matching large data sets and performing multiple matches with different criteria?
  • Can you do fast searches that leverage matching in real-time? Is the matching engine designed to handle bulk, near real-time and real-time modes?

These are only a few of the questions that come to my mind. A thorough analysis of this can help you use the best solution in the market. A correct decision here can save you months of person hours in the form of manual stewardship.

Back to the original question, the answer depends on what you are trying to achieve.

I would love to hear your views. Please leave your response in the comments section or reach out to me at @mdmgeek on Twitter.

Image courtesy of freedigitalphotos.net

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My New Home: Sugars. Trees. Caring. http://www.mdmgeek.com/2016/07/11/new-home-sugars-trees-caring/ http://www.mdmgeek.com/2016/07/11/new-home-sugars-trees-caring/#comments Mon, 11 Jul 2016 18:38:37 +0000 http://www.mdmgeek.com/?p=1220 Sugars. Trees. Caring. That’s the new name for San Francisco—a place I call home. But it could also be: Peanut. Butter. Brownie. It just depends on which 9 square meter(sqm) of land I’m staying at! Three little words. That’s the solution London-based startup What3Words came up with to standardize the long and complicated address formats […]

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Sugars. Trees. Caring. That’s the new name for San Francisco—a place I call home.

But it could also be: Peanut. Butter. Brownie. It just depends on which 9 square meter(sqm) of land I’m staying at!

Three little words. That’s the solution London-based startup What3Words came up with to standardize the long and complicated address formats from around the world.

What this British startup did is very simple:

  1. They divided the globe into 57 trillion squares. Each of these square measures 9sqm
  2. They then used an algorithm to pick from a list of 40,000 or so dictionary words.
  3. They randomly assigned three words to each square.

Voila! My address is now: Sugars. Trees. Caring.

geocodingOf course, they ensured their word collection did not include offensive terms or homophones, so we don’t end up with an address like “Stupid. Die. Dye.”

However, I did discover Toxic. Manhole. Drivers as a three-word code for an area in Frankfurt.

(I must say, it’s fun navigating over that map to find out what combination of words pops up!)

At first, this idea may seem somewhat crazy. Can you imagine using an address made up of random words?

However, if you think about the challenges we have with our global addresses today, you might be the first to say, “This isn’t a bad idea, after all!”

While looking into this, various questions shuffled through my mind:

  1. How does this system handle addresses within a multistory building with many offices in it?
  2. What about the similar challenge for large residential apartment buildings in cities with multiple flats?
  3. How would you handle scenarios with two friends sharing an apartment or house? How do you ensure mail delivery to the right person?
  4. As Rob Karel says, how do you zone a neighborhood? If this is a random assignment, how do we group certain areas (people living in high-value zip codes, employees of a company who share the same address, and more)?
  5. What are the privacy implications? Today, for privacy purposes, postal services offer post boxes, making it possible to receive mail without revealing your physical address.
  6. What about countries like China, Japan, and Korea, where addresses are written in their native scripts?
  7. What happens when we misspell a word? Will the mail end up in a completely different part of the world?

Location domain is an important aspect of MDM (and one of my favorite topics that I’ve covered in a previous blog on geocoding). In my view, we’ve created a hugely inefficient way to locate places on Earth with our borders, local rules, and political systems.

As my old colleague and Twitter friend James Taylor pointed out, this, in fact, is a crowded space with many companies, such as Open Location Code, geohash.org, and MAPCODE, trying to solve this problem.

It’s nice to see startups trying to address this challenge! And only time will tell if the What3Words system will work.

In the meantime, I’ll be dreaming of a day when I can tell Amazon where I live in three words and expect one of their drones to deliver my package accurately, in the shortest time possible!

What do you think about the three-word address idea? What works? What doesn’t? I’d love to hear your thoughts! Leave a comment below or connect with me on Twitter at @MDMGeek to continue the conversation.

Special thanks to Prashant Kondi, who told me about What3Words.

Image courtesy of gubgib/freedigitalphotos.net

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Review of 2015 & My Top 4 MDM Predictions for 2016 http://www.mdmgeek.com/2016/01/04/review-of-2015-my-top-4-mdm-predictions-for-2016/ http://www.mdmgeek.com/2016/01/04/review-of-2015-my-top-4-mdm-predictions-for-2016/#comments Mon, 04 Jan 2016 18:11:03 +0000 http://www.mdmgeek.com/?p=1203 Happy New Year everyone! I started last year with a blog on the trends we would see in 2015 and beyond. I highlighted eight key predictions on Master Data Management, Data Quality, Data Governance and Data as a Service that we collectively call information quality solutions. Looking back to what I offered (after spending a […]

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Happy New Year everyone! I started last year with a blog on the trends we would see in 2015 and beyond. I highlighted eight key predictions on Master Data Management, Data Quality, Data Governance and Data as a Service that we collectively call information quality solutions.

Looking back to what I offered (after spending a lot of time with other leaders in MDM space and my experience working with customers in the different spectrum of MDM adoption), I am happy to tell you that we did a good job in forecasting what was ahead of us.

PredictionsTo name a few things that stood out –

  • Many customers are demanding multidomain support in the solution so they can have one system for their current and future master data domain requirements.
  • MDM is becoming an integral part of analytics efforts. Many customers I talk to are using MDM as a backbone to big data analytics where the success of the project heavily depends on quality business-critical data about customers, products, locations, and suppliers.
  • I see massive adoption of solutions purpose-built for a particular industry or use case. Customers are looking for vendors to build solutions for them on top of an extensible MDM platform.
  • MDM on Steroid? Yes, it is. Many customers are looking to supercharge their existing MDM system or use some of the MDM capabilities such as matching on Hadoop to address both volume and velocity of data acquisition from internal and external data sources.
  • The market is still solidifying for cloud-based MDM adoption, but as I said in the blog, floodgates are yet to open. The enterprise MDM is very much on premise, but customers are asking for MDM solution to integrate well with the data that resides on the cloud such as Salesforce & Marketo.

2015 was an excellent year for MDM and me personally. As a product marketer, I got to spend much of my time with customers, learning more about the type of business problems they are solving using MDM.

As I analyze the past trends and look into what customer are demanding, here are few speculations about what may be in store for master data management in 2016.

MDM will become critical for driving customer experience: According to Gartner, the majority (89%) of the companies believe that customer experience will be their primary basis for competition by 2016. I predict

  • MDM of customer data will continue to be the biggest criteria for MDM adoption in 2016
  • More executives will realize MDM is at the heart of providing exceptional customer experience (As observed in Gartner Customer 360 Summit in San Diego last year).
  • Organizations will use MDM as a critical tool in their preparation for the competitive battlefield of tomorrow.

MDM will help achieve Mergers & Acquisitions (M&A) synergies faster: According to SpendMatters article, 74% of respondents report M&A plans in the next year. MDM is exquisite requirement for the organizations to ensure the faster realization of M&A synergies. Many companies are finding it hard to integrate a variety of systems resulting from such activities. There is only a handful of organizations that planned ahead their MDM initiatives with M&A in mind. These organizations are seeing their efforts bearing fruits. Although I do not see companies invest solely in MDM to speed up M&A, I believe that it will be a priority for CIO’s & executives planning, carrying out, and integrating data as a result of M&A.

MDM & Big Data use cases will see a substantial rise: 2015 was pivotal year where I saw few organizations leveraging the full potential of MDM by fueling accurate data for their Big Data initiatives. The combination of MDM and Big Data gives rise to a variety of use cases. I would like to highlight three particular types of usage we will see more in 2016.

  1. Matching, grouping/clustering (ex: Households) will move to Hadoop so organizations can apply MDM techniques to large scale data
  2. Linking of master data to interactions, transactions, social media data, to drive specific use cases will demand purpose build apps that leverage big data technologies
  3. Organizations will look for ways in which they can offload some of their existing MDM processing to Hadoop to ensure timely delivery of data.

Customers will demand an integrated solution: Over the years, we have seen that success of MDM depends on other factors such as data profiling, data quality, data integration, data enrichment, Business Process Management and data security. I predict that customers are going to demand a solution that provides all these capabilities as a combined offering.

It is an exciting time to be here for sure. Let me know what you are seeing. Also, check out, predictions from my blogger friend Henrik Sorensen. Henrik has spelled out three ‘gut feelings’ as he puts them, but I have a hint they make accurate predictions.

Please comment.

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Holistic Data Quality More Relevant For Big Data http://www.mdmgeek.com/2015/05/01/holistic-data-quality-more-relevant-for-big-data/ http://www.mdmgeek.com/2015/05/01/holistic-data-quality-more-relevant-for-big-data/#comments Fri, 01 May 2015 18:57:20 +0000 http://www.mdmgeek.com/?p=1194 In recently concluded Gartner MDM, Andrew White in his keynote said – “Only 40-50% companies are methodical in their approach to success with their information management”.  Andrew emphasized on importance of a systematic approach to MDM initiative for it to be effective. Similar to Analysts, we in master data management profession have campaigned for a […]

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In recently concluded Gartner MDM, Andrew White in his keynote said – “Only 40-50% companies are methodical in their approach to success with their information management”.  Andrew emphasized on importance of a systematic approach to MDM initiative for it to be effective.

Big Data Data QualitySimilar to Analysts, we in master data management profession have campaigned for a disciplined approach to MDM for a really long time. Our community continuously talks about importance of taking holistic approach to mastering data in the form of blogs, webinars and tweets. I believe, our efforts have not gone wasted. While we have seen MDM projects getting initiated without much consideration other than, “It seems like a great idea”, that notion has changed for the better.

Some of the customers I work with are taking an agile approach with clearly defined goals for their MDM program. They have realized that being nimble is an important aspect of this challenging journey. Taking an iterative approach with small implementation cycles is helping them build an incremental solution that provides success in every step of the way. These companies realize that master data, even though “small”, is a backbone that ensures efficient execution of every business process and functions in the organization.

So what is next for these organizations?

MDM delivers clean, consistent and quality data to your organization. The next step is to use it as a foundation on which you can build analytical capabilities that leverage big data. Big data analytics has a lot of potential and helps organizations in their digital transformation. However, this once in a generation shift we are seeing is hindered by same data quality issues that have bothered us over the years, now in a larger scale.

To gain most value out of big data initiatives, let’s ensure data quality is at the center

Poor quality and lack of trust are two main reasons that will hamper our ability to deal with big data effectively. The good news however is, we can address these two big data challenge effectively with same holistic approach we took to master data. To make sure big data projects offer us the right insights, be methodical and make sure reliable and trustworthy information fuels analytics.

Data quality is more important in big data world than it was ever before. The quantity is meaningless if it doesn’t drive actionable value and the phrase “garbage in, garbage out” remains valid in this new era. Winning with big data on our side requires data quality at the center.

Do you monitor data quality proactively across your MDM and big data projects? Please drop your comments below.

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6 Ways to Bring Data Visualization to Master Data Management http://www.mdmgeek.com/2014/12/19/6-ways-bring-data-visualization-master-data-management/ http://www.mdmgeek.com/2014/12/19/6-ways-bring-data-visualization-master-data-management/#comments Fri, 19 Dec 2014 23:21:45 +0000 http://www.mdmgeek.com/?p=1183 We humans are big aficionados of information, especially when it is presented in a way which is visual and striking. Catchy and colorful information get processed by our brain faster and hence visualization is a key aspect of today’s data rich world. For a long time we have had lousy focus on user interfaces to […]

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We humans are big aficionados of information, especially when it is presented in a way which is visual and striking. Catchy and colorful information get processed by our brain faster and hence visualization is a key aspect of today’s data rich world.

For a long time we have had lousy focus on user interfaces to MDM system. I think MDM being perceived as a “headless” application led everyone to emphasis more on creation of quality data and less on providing access to it in visual ways.Data Visualization

Fortunately, this notion is changing. We can see effect of data visualization in many different forms in today’s MDM solutions. Here are few ways new interfaces are getting created around MDM –

1. Stewardship Applications

Targeting data stewards and creating ways in which these users can access master data can make MDM extremely popular. Some of the key capabilities you can provide here are – side by side views to compare duplicated customer records, sophisticated hierarchy views that allow drag and drop features, complete picture of master data in innovative, visual ways.

2. Creating rich user interfaces

Ensure the master data is visually appealing to the users. Create a rich user interface which allows meaningful and well-articulated viewing of data. For example, instead of showing the address of the customer record in text form, you can use an embedded map which enhances the visual experience and at the same time is more engaging.

3. Providing striking visual access anywhere

We live in a mobile era. Allowing access to master data from a browser and mobile devices is a huge plus. This will immensely help the users who are on the go (As discussed here in this blog post). Check what Salesforce did with Wave.

4. Allowing flexibility and Configurability

Provide ways in which customers can easily customize user interface applications based on kind of data they manage in MDM. On top of it, allow users to be able to personalize the screens. A widget based adaptation allows easier drag and drop so the right data is at the right place depending on specific user’s preference.

5. Allowing integrated searching, filtering and faceting features

Searching is an important aspect of a master data UI. Allow users to search master data using free form text. Fuzzy matching is key here and once a set of results are returned, allow filtering and faceting techniques. This enables users to classify and view data in multiple ways rather than in a single, pre-determined order.

6. Provide visual data quality scorecards

Presenting key information to grab attention of users of the system is a huge plus. One of the primary objectives of MDM is to address data quality issues. Providing dynamic screens which can display quality of data in MDM can be a great feature and helps go long way.

These are only few things which come to mind but data visualization creates limitless opportunities. A great UI can help you put the missing face for MDM. It will also help you bridge the gap between IT and business by eliminating “headless” approach and enabling easier adoption of MDM within your enterprise.

What do you think? How data visualization and MDM are coming together? Share your thoughts via comments. And don’t forget to jazz up your MDM with some cool visualization.

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5 Ways Data Matching Is Used In MDM Implementation http://www.mdmgeek.com/2014/11/13/5-ways-data-matching-used-mdm-implementation/ http://www.mdmgeek.com/2014/11/13/5-ways-data-matching-used-mdm-implementation/#comments Fri, 14 Nov 2014 07:08:28 +0000 http://www.mdmgeek.com/?p=1175 In his recent blog post, Henrik Liliendahl Sørensen touched on the topic of data matching. He highlighted the considerations around where data matching should be done. I am a big proponent of avoiding duplicates by taking advantage of matching at the point of entry. But in reality, master data records get captured in different applications that […]

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In his recent blog post, Henrik Liliendahl Sørensen touched on the topic of data matching. He highlighted the considerations around where data matching should be done.

Matching-ScenariosI am a big proponent of avoiding duplicates by taking advantage of matching at the point of entry. But in reality, master data records get captured in different applications that are not equipped with matching or any other duplicate prevention mechanism. Not having a centralized master data management system which can address this problem is one of the key challenges organizations face today.

Once you embark on the master data journey, data matching becomes a crucial aspect and can help you at different stages of the implementation. Below, I discuss 5 stages of MDM project where data matching is used.

Initial load

As you start your MDM initiative, approach the solution by identify the sources of master data, bring at least 2 sources of data into master data hub and run data matching process. This is an important step and provides you a framework for future source system integration. It also allows you to pick right matching technique for your scenario.

Batch data loads

The ongoing integration from internal and external data sources usually requires a batch data load option. One of the approaches you can take here is to load party data into the hub and then trigger a periodic (nightly) matching process to identify duplicates. This requirement usually arrives during a consolidation and co-existence phases of MDM where you will create a golden profile for reporting and other analytical purposes.

Real time interactions

As you start moving towards transactional hub architecture, you need to turn on the real time matching and linking of records. In this phase, applications directly interact with master data hub via API’s and services. A party record flowing into MDM is matched in real time; duplicates are identified and merged into a surviving record. This is an ideal state where your party data is centralized, well maintained and acts as a foundation for things such as real time analytics.

Mergers and Acquisitions

Mergers and acquisitions bring unique challenges and matching comes in handy here again by helping you mash up newly acquired customer data with your MDM hub. Usual approach is to take a production like environment (Or M&A environment) and do multiple passes of data matching exercise to find the right approach to integration.

Searching

A new feature some of the early pioneers are doing when it comes to data matching is to provide you a Google like search capability within MDM. Here, data is matched as you type your search criteria to help you avoid duplication at point of entry. I will explore more about this in a future post.

What are the other situations you have come across where data matching is used? I would love to hear your opinions and thoughts via comments.

Image Courtesy of phanlop88/freedigitalphotos.net

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3 Under Looked Aspects of MDM Implementation http://www.mdmgeek.com/2014/10/09/3-looked-aspects-mdm-implementation/ Thu, 09 Oct 2014 18:06:37 +0000 http://www.mdmgeek.com/?p=1162 For decades, we have relied upon data integration approach to setting up master data management systems. While there is nothing unruly about the approach itself, the perception it creates on the implementation team has been troubling to say the least. I wrote an article here about approaching MDM in a holistic and agile fashion. I […]

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For decades, we have relied upon data integration approach to setting up master data management systems. While there is nothing unruly about the approach itself, the perception it creates on the implementation team has been troubling to say the least. MDM Implementation

I wrote an article here about approaching MDM in a holistic and agile fashion. I debated here about taking a business driven approach to be successful with MDM. The focus ought to be on identifying the data issues and aligning MDM with data governance to bring master data to a common, agreeable, enterprise standard structure. Data integration then just becomes a way to move the data, clean it and maintain it in a place which will act as single version of truth.

Given that quality data creation is your primary goal, following 3 aspects become the key drivers of an MDM implementation. Unfortunately, these are also the 3 most under looked features as I have experienced over the years.

Data Discovery and Profiling

What happens when you (Master Data) visit a doctor (MDM System)? The first thing your doctor wants to know is to see what your health history and symptoms (Data Quality) looks like. This is so he can find the perfect treatment (Transformation) for you and improve the ‘quality’ of your health.

How do you feel if your doctor never checks your symptoms before giving you a medicine? Data discovery is very important for MDM

Same analogy applies to MDM. Master Data suffer from quality issues such as non-standard representation, in-consistencies, missing and defaulted values. As you bring this data into MDM system, you have to run it across a systematic discovery process where anomalies are identified and documented. A profiling step ensures data is tested against rules and fixed.

I wrote about this step in my earlier blog on five key factors in architecting a master data solution. Missing this step is sure sign of failure and will lead to data quality issues getting replicated to MDM system. How do you feel if your doctor never checks your symptoms before giving you a medicine?

Enriching MDM with External Data Services

In the past, many systems and business processes have relied on external data sources while making their key decisions. Sometimes adding additional value and many times part of a compliance procedure, these external sources have proved to be of significant value. They have helped in making marketing and sales team take proper action based on intelligence available outside organization walls.

Social and other big data sources can enrich your master data and help make it more relevant and current.

Same principle applies to MDM system. In fact, MDM allows a well-coordinated re-use of external data. This helps in building complete profile of your master data records in a central place, so all your applications can leverage this intelligence.

Added to this is the availability of social and other big data sources that can further make your master data more relevant and current. A well-planned enrichment process from commercially available data providers can help you make easier segmentation and prospecting of your master data. This should be a key aspect of your MDM journey as it helps in areas such as – address standardization and certification, social data enrichment, data quality improvement and real world alignment.

Reference Data Management

One of the most painful parts of the MDM implementation is when customers fail to distinguish reference data from master data. There are few reasons why this is a significant issue. One, reference data requires a focused and dedicated effort in itself to manage it well. Two, reference data generally gains more value when it is widely re-used and referenced. Three, failure to handle this data and standardizing it will cause complexities to MDM implementation itself.

Reference data requires a focused and dedicated effort in itself to manage it efficiently

I have seen customers building temporary staging areas which are a huge pain to maintain for the implementation team. They also cause taxing issues in terms of accountability. Result of all of this is that you will have – dropped records, duplicated values and overhead of manually maintaining mappings for individual sources. While on this topic, do visit my earlier blogs to read about reference data management.

MDM implementations continue to be characterized by lengthy timelines, effort and obscurities. Knowing the best practices and learning from experience is one of the best ways to ensure you make your project fail proof. I hope the three aspects I discussed here will help you plan your MDM journey well.

3 cheers to your success!! Please provide your feedback via comments. I love to hear from you.

Image courtesy of John Kasawa/sippakorn/FreeDigitalPhotos.net

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House Holding in MDM System http://www.mdmgeek.com/2014/09/18/house-holding-mdm-system/ http://www.mdmgeek.com/2014/09/18/house-holding-mdm-system/#comments Thu, 18 Sep 2014 14:52:18 +0000 http://www.mdmgeek.com/?p=1144 A recent blog post on DataMentors discussed about house holding – a process of grouping related customer records. House holding is one of the most prominent aspects of selling by many industries today. What house holding information allows is the ability to find out the aggregated relationship of a family as a whole with the […]

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A recent blog post on DataMentors discussed about house holding – a process of grouping related customer records. House holding is one of the most prominent aspects of selling by many industries today.

House HoldingWhat house holding information allows is the ability to find out the aggregated relationship of a family as a whole with the organization. For example, a retailer may want to group customers from the same family unit to reduce cost of marketing and also cater to the preferences of the household versus individual customer preferences.

Talking from an MDM perspective, capturing household information in the system adds ample value to your solution. Since this data is crucial to marketing, managing this information in MDM, which is already a core system for trusted customer data, makes for a great selling point.

House-holding process comes with a unique set of challenges. Here are some of the key opportunities I have witnessed in many MDM implementations –

Deriving House Holding Data

A core challenge with house holding is to find ways in which this information is derived. Several aspects can be considered which include incubation of name parsing and address matching. Some times customers may actually be providing this knowledge. But as I have seen, this information is not very explicit. The other clue, which is helpful, is derived from customer’s account information such as joint accounts owned by two customers who share common last name and address. Phone numbers can also be used as criteria to increase the confidence level of grouping.

When we aggregate all these above techniques, we can have a wealth of knowledge linking parties in our master data management repository.

Regrouping of House Holding Data

House holding information, just like any other master data, is mutable.  Some of the factors, which cause the change are – marriage, divorce, birth and death of a family member. So, if we are keeping all the distinct parties in a system like MDM, we need to periodically regroup them depending on the changes which might have happened. This can either happen in real time as the data gets added and updated, or can be done on a scheduled basis using a batch job depending on the criticality of the data requirements.

Distinguishing Customers and Individuals

When you group parties into households, we have to ensure there is a clear indication to show customers versus individuals. Here, individual is a person in the household who do not yet have a contract with the organization. This clear indication helps during cross-sell and up-sell opportunities to create proper messaging.

Grouping of customers seems like a simple process but in reality is one of complex exertion. If done correctly, this information can be of a great value-add to your MDM system and can help your marketing thrive by targeting right customers and prospects.

What are your thoughts? Do you have experience working with house holding data in a MDM system (or otherwise)? Do share.

Image courtesy of Stuart Miles/FreeDigitalPhotos.net

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Good Work You Do With MDM Does Not Go Unnoticed http://www.mdmgeek.com/2014/09/05/good-work-you-do-with-mdm-does-not-go-unnoticed/ Fri, 05 Sep 2014 14:05:17 +0000 http://www.mdmgeek.com/?p=1114 Over the years, I have worked with many customers to help deliver successful MDM projects. It’s immensely entertaining to be involved with clients directly, working with them on a day-to-day basis and help solve their data quality issues. I have thoroughly enjoyed these engagements as well as the celebration, which follows when a project (or […]

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Over the years, I have worked with many customers to help deliver successful MDM projects. It’s immensely entertaining to be involved with clients directly, working with them on a day-to-day basis and help solve their data quality issues. I have thoroughly enjoyed these engagements as well as the celebration, which follows when a project (or at least portion of it anyway) gets completed successfully. The recognition and accolades are a huge bounty that I cherish. After all, who doesn’t like a little bit of pampering for the hard work they put in?

Similar sentiments are attached to writing I do here on my MDM blog – A Geek’s Point of View. If an article creates a buzz and gets a good feedback, I think I have earned my fair share for the time I invest here.

Last year my blog was selected by BizTech Magazine as one of 50 Must-Read IT Blogs of 2013. While I consider this as an overgenerous honor, it sure was an indication of how www.mdmdgeek.com has created enormous impression just 3 years since its inception. I would like to think of it as a result of the quality content coming straight out of my experience and my drive towards everything Master Data Management.

Here is the quote from BizTech Magazine:

No, this isn’t a blog about how to secure your company’s iPhones and Android devices. IBM technical specialist Prashant Chandramohan educates readers on the benefits of the other MDM: Master Data Management software.

Last week, Exigent Networks, a Business Technology Solutions company published this beautiful info-graphic highlighting the best bloggers in the niche of 2014. I am thrilled to see www.mdmgeek.com highlighted here again. The info-graphic features my blog with following quote, a true reflection of its positive impact on the MDM community.

MDM Geek is a uniquely excellent IT blog as it specializes in the area of master data management software. It is vital resource for IT professionals who have to deal with MDM on a regular basis. Helmed by IBM Technical Specialist Prashant Chandramohan, the blog educates readers on the benefits of MDM and provides expert tips and best practices guides to readers.

These are proud moments and something I commemorate. It feels good to know that hard work and ‘give-back’ attitude we possess doesn’t go un-noticed. And here is a promise – while I am at the cross roads (hint!), I will continue to write and share what I have learnt in my decade long experience in this awesome world of master data, the data that I breathe and live.

MDM

 

 Image courtesy of Stuart Miles/FreeDigitalPhotos.net

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