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April 13, 2009

Data Quality Steps For Successful MDM Program

Filed under: Data Cleansing, Data Quality — Tags: — Olga Belokurskaya @ 4:57 am

It’s surely no secret that data quality management and MDM are two key factors of enterprise information management. They are interrelated for without DQM, MDM is simply a pile of the data storage as well as DQM cannot bring ROI to the organization without MDM. Actually data quality management plays a role of a building block of an MDM hub as quality and accurate data is a key to the success of an MDM program.

In-depth analysis of the quality and health of data is a prerequisite of the MDM program. Here are data quality management steps suggested at Information-Management.com, which are needed to support an agile MDM program:

  1. Identify and qualify the master data and its sources. The definition of master data may be different for different business units. The first step involves identifying and qualifying master data for each business unit in the organization
  2. Identify and define the global and local data elements. More than one system may store/generate the same master information. Additionally, there could also be a global version as well as local versions of the master data. Perform detailed analysis to understand the commonalities and differences between local, global and global-local attributes of data elements.
  3. Identify the data elements that require data cleansing and correction. At this stage, the data elements supporting the MDM hub that require data cleansing and correction have to be identified. Communication with the stakeholders is necessary so that as part of the MDM initiative, data quality will be injected into these selected data elements on an organization-wide basis.
  4. Perform data discovery and analysis. Data collected from source applications needs to be analyzed to understand the sufficiency, accuracy, consistency and redundancy issues associated with data sets. Analyze source data from both business and technical perspectives.
  5. Define the strategy for initial and incremental data quality management. A well-defined strategy should be in place to support initial and incremental data cleansing for the MDM hub. Asynchronous data cleansing using the batch processes can be adopted for initial data cleansing. Industry-standard ETL and DQM commercial off-the-shelf tools should be used for initial data cleansing. The incremental data cleansing will be supported using synchronous/real-time data cleansing.
  6. Monitor and manage the data quality of the MDM hub. Continuous data vigilance is required to maintain up-to-date and quality data in an MDM hub. Data quality needs to be analyzed on a periodic basis to identify the trends associated with the data and its impact over the organization MDM program.

In fact, data quality management is the foundation for an effective and successful MDM implementation. A well defined strategy improves the success probability of an MDM program. Organization should embark a data discovery and analysis phase to understand the health, quality and origin of the master data.

March 20, 2008

5 things to Watch out for in Data Warehousing

Filed under: Data Cleansing, Data Integration, Data Quality, Data Warehousing — Tags: — Alena Semeshko @ 7:45 am

There’s been talk of the concept of data warehousing being misleading, failing to deliver efficient solutions at the enterprise level and frequently causing problems upon implementation. Problems like that, again, don’t come out of nowhere, there usually are good reasons behind them. In this post I’l try to sum up a few things you should definitely try to watch out for when tackling your data warehouses:

1) First and foremost – Data Quality. When your data is dirty, outdated and/or inconsistent upon entering the warehouse, the results you are gonna get won’t be any better, really. Data Warehousing is not supposed to deal with your erroneous data, it’s not supposed to perform data cleansing. These processes need to take place BEFORE your data gets even close to the warehouse, that I s, your data integration strategy needs to address low quality data problem.

2) Come to think of it, Data Integration is the second thing to watch out for. Do your integration tools live up to your requirements? Can your software handle the data volumes you have? Will it comply with the newly added to your warehouse source systems and subject areas? How high is the level of automation of your integration system? Can you avoid mannual intervention? You gotta ask yourself all of these questions before you complain that your warehous isn’t providing you with the quality of information you expected.

3) Next, dreaming too big. When you build sand castles you gotta realize they’ll disappear in a matter of days, even hours. Your can’t have it all and at the same time, you can’t have your pie and eat it too. Brreaking the project into small segments, giving them enough time to deliver and having patience is the key to having a pleasant experience with your data warehousing solution. What? Did you think you can fix all the mess in your data in a matter of days? =)

4) Then, don’t go rushing into solutions. Don’t panic. Yes, warehouse projects require time and effort on your part. Yes, it’s gonna be complicated at first. But that’s not the reason to stop with one project and rush into another. Stick with your first choice, fix it, work on it. Multiple projects will waste your resources and end up as another silo aimlessly taking up your corporate resources.

5) Finally, make sure you have a scalable architecture that you can redesign according to your increasing needs. Your business grows, sometimes grows quicker than you think (the number of customers increases, they have more information, more data to be processed) and you want your solution to continue to perform on the same level and live up to your expectations.

The list goes on actually, as there are more things to watch out for… but these are the first that come to mind. =)

March 5, 2008

How Data Warehousing Rules

Filed under: Data Warehousing — Tags: — Alena Semeshko @ 2:24 am

Back to yesterday’s post on BI. With Data Warehousing being an indispensible attribute of BI, I’d say it’s also one of the key components in making the company’s decision-making lifecycle more eficient and productive.
DW

March 4, 2008

Why Data Warehousing? Why Business Intelligence?

Filed under: Data Warehousing — Tags: , — Alena Semeshko @ 8:33 am

In the world of Business Intelligence there’s no place for people who manage by gut. Auch that hurts, huh? But that’s true. People who use their intuition or gut feeling to make major decisions in business usually lose to those using BI in support of management decisions. It’s like with cars: your serviceman knows exactly what that noise under your hood means and what has to be repaired or replaced in your car, while you might only suspect that something’s wrong with the engine or breaks or gearbox and if you were to repair your car, you’d be more likely to break something else than repair what’s broken. Employing BI strategies and techniques, like, for instance, data warehousing, provides the security and assurance you need to keep your business up and be sure of your decisions.

When success depends on how quickly a company responds to rapidly changing market conditions, BI is where you turn for help. It fast-forwards the decision-making processes and provides you with the insight necessary to make the right decisions faster.

With the modern technologies of data integration, warehousing and analysis, you get a single complete view of your organization’s past, present and potential future with the major problematic areas already figured out for you. All that is left is for this perspective to be put into action.

*get going*