Tag Archives: reference data

The Five C’s of Data Management

The Five C’s of Data Management

The Five C’s of Data Management

A few days ago, I came across a post, 5 C’s of MDM (Case, Content, Connecting, Cleansing, and Controlling), by Peter Krensky, Sr. Research Associate, Aberdeen Group and this response by Alan Duncan with his 5 C’s (Communicate, Co-operate, Collaborate, Cajole and Coerce). I like Alan’s list much better. Even though I work for a product company specializing in information management technology, the secret to successful enterprise information management (EIM) is in tackling the business and organizational issues, not the technology challenges. Fundamentally, data management at the enterprise level is an agreement problem, not a technology problem.

So, here I go with my 5 C’s: (more…)

Posted in Application ILM, Big Data, Data Governance, Data Integration, Enterprise Data Management, Integration Competency Centers, Master Data Management | Tagged , , , , , | Leave a comment

Building A Better Data Warehouse

The devil, as they say, is in the detail. Your organization might have invested years of effort and millions of dollars in an enterprise data warehouse, but unless the data in it is accurate and free of contradiction, it can lead to misinformed business decisions and wasted IT resources.

We’re seeing an increasing number of organizations confront the issue of data quality in their data warehousing environments in efforts to sharpen business insights in a challenging economic climate. Many are turning to master data management (MDM) to address the devilish data details that can undermine the value of a data warehousing investment.

Consider this: Just 24 percent of data warehouses deliver “high value” to their organizations, according to a survey by The Data Warehousing Institute (TDWI).[1] Twelve percent are low value and 64 percent are moderate value “but could deliver more,” TDWI’s report states. For many organizations, questionable data quality is the reason why data warehouses fall short of their potential. (more…)

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MDM – What’s The Cost Of Bad Data In Financial Services?

One of the most critical first steps for financial services firms looking to implement multidomain master data management (MDM) is to quantify the cost savings they could achieve.

Unfortunately, a thorough analysis of potential ROI is also one of the steps least followed (a key culprit being disconnects between business and IT).

This shortcoming is spotlighted in a new Informatica white paper, “Five Steps to Managing Reference Data More Effectively in Investment Banking,” which outlines key questions to ask in sizing up the cost implications of bad data and antiquated systems, such as:

  • How long does it take to introduce a new security to trade?
  • How many settlements need to be fixed manually?
  • How many redundant data feeds does your firm have to manage?
  • How accurate and complete are your end-of-day reports?
  • Do you have the data you need to minimize risk and exposure? (more…)
Posted in Enterprise Data Management, Financial Services, Governance, Risk and Compliance, Master Data Management, Operational Efficiency | Tagged , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | Leave a comment

Top 3 Trends in 2009 that Transformed the MDM Market

With the New Year dawning I wanted to look back at some industry trends from the past twelve months, and then look at ahead at what we’re likely to see in 2010. So, this week: recap. Next week: predictions.

“Multidomain MDM” Goes Mainstream
From its inception MDM was meant to be “multidomain” – a solution for multiple data types. This stood in contrast to CDI or PIM, each of which focuses on a single domain. But with CDI morphing into “MDM for Customer Data” and PIM to “MDM for Product Data,” the terminology got a bit muddled. Hence the somewhat redundant “multidomain MDM” came into common usage in 2009 to differentiate it from single-domain MDM. As we saw with the Gartner numbers that I reviewed in my last post, the obvious benefits of managing all domains via a single platform, easier maintenance, and the advantages of leveraging existing investments, are spurring increased adoption. Still, confusion remained over multidomain MDM in the last year, not just with terminology, but also with capabilities. I addressed this in a late November post, but to reiterate: It’s not just about the data model, a true multidomain MDM hub has to be able to model, cleanse, match and relate.

Proactive Data Governance Takes Root
Many IT decision-makers came to the realization in 2009 that reactive data governance is bad because it is not responsive to real-time business needs. Business users increasingly demanded real-time data availability and data stewards worked to put proactive data governance into place to meet these demands. My friend Dan Power covered this topic earlier this year, showing that authoring data directly in an MDM hub enables firms to decouple data entry from traditional CRM and ERP systems, and establish the hub as both the System of Entry and the System of Record.

Taking Aim At The Reference Data Problem
Early on in 2009 we started seeing a lot of customer activity around the “reference data problem” and that interest remained strong through the year. To quickly summarize: certain business processes, order-to-cash for instance, oftentimes use three or more different systems interacting with each other to complete the loop. If their “look-up code” data isn’t standardized (one uses “USA” the second “U.S.” the third “United States”) problems ensue. As a category, reference data is similar to, though distinct from, master data. Yet the similarities are such that effective MDM solutions are perfect for solving the reference data problem. My colleague Manish Sood blogged on this topic in detail here and here.

Be sure to come back next week to see our bold predictions for 2010!


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How to Solve the Reference Data Problem using MDM (Part 2 of 2)

In my first post on the subject, I began exploring the underlying reasons why reference data management is so important for effective sales strategies and new business efforts. In this second post, let’s take a look at some real-world scenarios. Consider a setting in which a group of marketing analysts from a retail firm want to run a report to discover the overlap of customers across various channels such as in-store sales (POS), web sales and mail order sales. The methodology would require populating a data mart with information from each of these channels, and perhaps augmented with information from the company’s account systems and customer relationship management system. In order for the business intelligence applications to slice and dice the data from the various systems, reference data conflicts between each of the sources would need to be resolved first. It is impossible to make apples-to-apples comparisons when your business intelligence application is working with fundamentally dissimilar data sets.

The issues surrounding system diversity and proliferation make reference data a concern in operational settings also. Imagine this same retailer plans to open a series of new stores. Before the doors can open the retailer needs to create a whole new set of reference data for the new locations, and this information needs to be entered or replicated into all the other systems the company relies on to support critical operations: point of sale, supply chain, ERP and so forth. These are the systems that support just-in-time inventory, real-time transactional processing, and many other processes that drive consumer retail operations today. If the new reference codes aren’t in place when the doors open, the retailer would not be able to process purchases, track inventory, or perform many of the critical IT actions and procedures critical to the business.

The reality for large IT organizations today is that ripping out legacy systems and replacing them with new integrated systems is simply not a workable or affordable solution. Which is why it is imperative that organizations have the ability to easily integrate installed systems, create composite applications and integrate new systems into current environments—and have a more streamlined process for managing these shared data assets. Siperian MDM Hub gives IT administrators, data stewards and project leaders exactly these capabilities. Specifically, it provides complete control over critical reference data management processes:
1. Reference data creation
2. Reference data mapping from various sources
3. Reference data workflow and collaboration
4. Hierarchical reference data management
5. Inbound reference data resolution
6. Outbound reference data resolution
7. Reference data services

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The Reference Data Problem (Part 1 of 2)

Business depends on information. Effective sales strategies, new business efforts and many day-to-day operations hinge on the ability to draw information from a variety of sources to obtain needed answers. But many companies have difficulty bringing together integrated views of data from the full diversity of sources across the enterprise. The problem is most often depicted as stemming from outdated IT infrastructure, or incompatible applications and data sources. In fact, lack of visibility into enterprise data is best understood as a reference data management challenge.

Commonly referred to as “code tables” or “lookup tables,” reference data provides context for or categorizes data within the database, or even information outside the database. Basic pieces of information such as time zones, geographical information, zip codes and currency designations are typical reference data, though the category also includes such things as chart of accounts, financial business unit hierarchies, legal entities, accounting master data, financial reference data and class of trade information. The unifying thread being that reference data creates a detailed framework within which the enterprise can record and understand transactional information as it changes over time.

The reason that reference data poses a challenge to visibility into important business information is that databases often have distinct reference data structures (see chart). In order to combine, share or search for data across multiple databases, any conflicts between the various reference data sets must be resolved first. Reference data management in small-scale, point-to-point integration projects is generally not a major concern; resolving conflicts can be time-consuming and labor-intensive, but is definitely solvable. Where the situation becomes problematic is when organizations need to integrate larger and larger numbers of underlying systems, or incorporate new systems into an existing infrastructure.

Not surprisingly, the reference data problem crops up most often in very large companies that have heterogeneous operations that came about through acquisitions or through rapid organic growth. Within today’s Fortune 500-sized organizations, where it is not at all unusual for more than one hundred customer data sources and systems to be in use, reference data management can be a critical showstopper when it comes to cross-system integration or the creation of new systems relying on divergent data sources. This holds true with both analytical and operational integration. In my next post I will explore how firms today are making creative use of master data management technologies to solve their reference data management problems.

Simple Code Look-up

Source System

Code Value

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The reference data problem stems from the fact that different systems use different codes for the same information.

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