Archive for the ‘Data Hygiene’ Tag

npENGAGE – Data Hygiene

npENGAGE – Data Hygiene

Good article… Thanks Mary Dempsey.

Go to this link and sign up for one of the many newsletters which focuses on different aspects of your business.  I believe the section I found this article in was Analytics – Data Hygiene, Is a bad address costing your organization?

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Wiki – Data Cleansing

Wiki – Data Cleansing

Data cleansing, data cleaning or data scrubbing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. Used mainly in databases, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant, etc. parts of the data and then replacing, modifying, or deleting this dirty data.

After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores.

Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at entry time, rather than on batches of data.
The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.

PC Magazine’s Definition

PC Magazine’s Definition

The condition of data in a database. Clean data are error free or have very few errors. Dirty data have errors, including incorrect spelling and punctuation of names and addresses, redundant data in several records or simply erroneous data (not the correct amounts, names, etc.).

Is Your Data Getting Better or Worse?

Over the past 25 years for those of us that have been in the industry that long… We have seen a number of advancements in data hygiene applications and techniques? Regardless, if you are old or new to the industry – What data hygiene software best suits your current needs today?

(LOL: Even the word Data Hygiene is old school… today we use more robust terms like Data Management, Data Quality, Enterprise Data Quality, Master Data Management, Data Governance, etc…)

Dots On A Map Provide Unique Insights Into Data Quality

This was a presentation I originally prepared back in 2005, but is probably even more applicable in 2009 given the impact using a GIS tool can have on visualizing data quality – customer addresses on a map! The next time you conduct a customer “data” assessment – try this! You can also see a high level data profile I prepared for this trade area of specific customers.

What Different Routines Do You Consider Important When “Data Profiling” In Order To Reveal The Quality Of Information In A Data Source?

There are several different types of data quality tools in the marketplace today to essentially do one important thing – cleanse, validate, correct, and enhance your data.

In order to better understand what the “quality expectation” is for YOUR CLIENT a baseline (or scorecard) must be established for each source system. Data profiling is an ideal way to reveal and share the results with others in order to make an informed decision and rank your findings.

Premier-International’s EPACTL Tool (Applaud)

Premier-International is based in Chicago and has software and consulting services:

What is Applaud?

Applaud is the only “EPACTL” tool – the only single software product with integrated tools to extract, profile, analyze, cleanse, transform and load data.

EPACTL is a new breed of software that provides integrated tools to accomplish all requirements of data quality and data migration/consolidation projects.

After reviewing the website, here are some of the key service offerings I would like to share which has been directly taken from their website to avoid mis-representation:

1.) Data Migration and Data Conversion – Migrating data from legacy systems to a new replacement system.

2.) Data Consolidation – Consolidating data from multiple instances of the same system or multiple disparate systems.

3.) Data Cleansing – Cleansing data and supporting data quality initiatives.

4.) Data Quality Audits – Performing data quality audits.

5.) Data Integration – Constructing interfaces between on-going systems.

6.) Data Management for IT – Building customized data management solutions.

7.) Data Management for Employee Benefits – Delivering customized data management solutions for employee benefit consultants and actuaries.

8.) Rapid Application Development – Using Applaud’s RAD tools to deliver dynamic system solutions fast.

If you want to learn more about Applaud and Premier International, visit…

http://www.premier-international.com/company_about_us.aspx?SubMenuID=11

If there are any readers out their who have knowledge about Premier-International or Applaud, please feel free to comment.

From TDAN: 11 Predictions About Data Quality Space

Diby Malakar has written an interesting article on possible upcoming trends regarding Data Quality given the current economic climate:

http://www.tdan.com/view-featured-columns/9859

Read this article and more at TDAN – The Data Administration Newsletter.

Data Cleansing With Datactics

Datactics delivers rapidly deployed and user friendly products, converting and cleansing data from disparate sources in multiple languages into reliable business information for Fortune 500 and other leading companies.

Be first to comment about any success stories or background regarding this company.

Gartner Says… Companies Want to Get The Data Right

Here is a good < 10 minute video on MDM from Gartner = November 2008 by Ted Friedman, Vice President covering Data Integration, Data Quality and Data Warehousing.

My high-level notes:

1.) Ties to critical business initiatives are a must.

2.) Gartner is seeing “pre-packaged” product offerings on the rise.

3.) Appliance offerings also… “Datawarehouse in a Box”.

4.) Challenges of Data Integration and Data Quality – automating data transformation and data cleansing routines.

5.) Companies getting even more serious about Data Quality given the regulatory issues.

6.) Data Quality and its impact on loss productivity, inaccurate data, etc.

7.) Companies want to get the data right.

8.) Business issues > IT issues according to Gartner

9.) Key question to ask your client is what does Data Quality mean to you?

10.) Dimensions are several – identify key metrics and they must be fact-based.

11.) Data Quality tools continue to emerge in the industry.

12.) Information Management issues are top of mind.

If your company has a Master Data Managment (MDM)

offering you would like to share – click here – and it will take

you to another blog = www.masterdatamanagement.wordpress.com

where you can request to have your company name added to the “links” section of the blog.   Include a brief description, as well.

Here is Ted’s video: