Data cleanup unlock the potential at a corporate scale
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Data Cleanup: Unlock the potential at a corporate scale. Thibault Dambrine. IT professional for 25 years Network Designer ETL Data Warehouse Analyst Interface Specialist ERP Developer Data Quality Experience:

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Data Cleanup: Unlock the potential at a corporate scale


ThibaultDambrine

  • IT professional for 25 years

    • Network Designer

    • ETL Data Warehouse Analyst

    • Interface Specialist

    • ERP Developer

  • Data Quality Experience:

    • Tasked to work on the pre-conversion data cleanup project during the Shell JDE to SAP transition


Introduction

  • Premise:

    “What would a Company-wide Data Quality Initiative look like?”

  • Base:

    • Experience setting up a data cleanup team, prior to a JDE to SAP data conversion

    • Realizing the potential for increasing the data value within the corporation


Defining Data Quality

  • Intrinsic Data Quality:

    • Accuracy, Completeness, Uniqueness

    • Reliability, Security and Accessibility

    • Contextual Data Quality:

    • Timeliness, Relevance

    • Inter-operability, consistency of identifiers

  • Accessibility and Representational Data Quality

    • Ease of understanding

    • Consistency of identifiers

    • Consistency in structures


$

Quantifying the cost of Quality: The 1-10-100 Rule - Additional cost = Less Competitive Business

$

Prevention Cost

$

Correction Cost

Failure Cost

The 1-10-100 Quality Cost Rule


The 1-10-100 Rule A Data Quality Example:

The 1-10-100 Quality Rule Applied to mailing Data:

It costs:

  • $1.00 to verify data at data entry time

  • $10.00 to clean the data after the fact

  • $100.00 to mop up errors caused by bad data

    • Packages mailed in the wrong address

    • Lost revenue

    • Lost customers

    • Bad (sloppy) reputation

    • Additional carrying cost for bad data


Data Quality: The Up Side!

Trust

  • Consistent data inquiry results build confidence in information systems

  • Tractability across Business and IT domains

  • Consistent data identifiers

    • promotes internal cross-department reporting

    • Consistency

    • Confidence in results

      Productivity

  • Removing redundant or near-redundant data

  • Maximizes re-use of data

  • Reduces the amount of data being processed

  • Reduces errors

    Reliability

    Consistently good quality data is data you can count on!


A Data Cleanup Initiative

  • Where to start?

  • Who will enforce such quality initiatives?

  • How will the data quality be maintained on on-going basis?


DQB Task 1: Identify Sponsor and Data Quality Boss

To Identify sponsor:

  • Communicate clear understanding of the cost of bad data

  • Use the 1-10-100 rule

  • Initiative has to be backed with

    • Money

    • Authority

    • Responsibility


Identify Data Quality Boss – DQB

  • Must be knowledgeable on data quality

  • Must be knowledgeable on the Business

  • Will be responsible for data quality

  • Will have authority to make changes

    Note:

    Responsibility without authority will not work


DQB Task 2: Identify Data Sets

  • Identify/inventory high-level data sets e.g.

    • CMDB

    • ERP

      • Master Data e.g.

        • Customer Master

        • Item Master

      • Transaction Data e.g.

        • PO’s & Invoices

        • Inventory movements

  • Assign data sets to departments, potential lists of Data owners

  • Note: The final data owners may not be the one initially penciled in at this stage


DQB Task 3: Identify Business Side Data Owners

  • Data Owners will effectively be the local, more granular, Data Quality Bosses. Again, they will need to

    • Be responsible for the data at their level

    • Have authority to request changes at their level

    • Have bottom up knowledge of the data, understand what “should be there”

  • Setup meetings with every department, in line with the Data Sets identified, with aim of coming up with a set of Data Owners

    • Have a presentation ready

    • Look for individuals who have been in the Business for a long time, who are well respected, who understand the data, the dependencies, and know who to talk to, to get answers, from the bottom up


DQB Task 4: Request from Data Owners the “Data Quality Specification” or DQS

  • DQS is a document that spells out the data quality rules e.g.

    • No duplicates or near-duplicates

    • Data older than x years should be purged or archived

    • Data Dependencies such as no detail without a header or no invoice without a PO

    • No duplicates

    • Consistency e.g. data format

    • Quality audit e.g. postal code matches address

    • More…

  • Note: Some rules will apply in all DQS Documents

  • There is value in sharing, reviewing and updating the DQS over time.

  • Data quality issues are not always apparent until a first cut of data is cleaned up


DQS (part of Task 4 ) Also look for:

  • Data Islands

    • Lack of consistent identifiers inhibit a single view of the big picture

  • Data Opportunity

    • Could correlated data sets be more useful to the Business?

  • Data Surprises

    • Misplaced Data

    • Information buried in free-form fields


DQB Task 5: Build IT Data Quality Team

  • Data Quality

    • Cannot be a “side job” or a part-time task

    • Must be staffed with individuals who understand data. Best candidates

      • Proficient in SQL, data extract techniques

      • Understand ETL tools and techniques

      • Are detailed-oriented

      • Experience: Data Warehouse staff is good fishing grounds for such individuals


Mid-Presentation Recap: All the Ingredients are now in placeThe real work can start!

  • Name Data Quality Sponsor & Data Quality Boss (DQB)

  • Identify Data Sets

  • Data Quality Owners

  • Data Quality Specifications (the DQ Roadmaps)

  • IT Data Cleansing Team


Introducing: the Data Quality Cycle

  • We now have

    • a sponsor

    • Identified data sets and data owners

      • They have produced Data Quality Specifications

    • An IT Team ready to work on the first Data Quality measurements, based on the DQS

  • Next step: Initiate the cleanup

    • Not a single iteration but one that will be repeated in a cyclic fashion


The Data Quality Cycle: Simple View

DATA


Data Quality Cycle - Corporate Version

Analyze Data

Improve Data

Continuous Improvement

Monitor Progress

Formalize Schedule

Make Progress VISIBLE


Step 1: Identify Bad DataBad Data Definition: Does not adhere to DQS

  • Coordinate meetings to translate DQS documents into a suite of repeatable data cleansing procedures

  • Very important that these procedures should be repeatable, schedulable on regular basis

  • Initial Focus: Identify Bad Data

    • Bad data(does not adhere to DQS),

    • Inconsistent data

    • Old Data

    • Note: DQS will spell out rules for “old” and “inconsistent”

  • Ensure results are reported in a format readable by Management at executive level. This initiative has to be VISIBLE


Step 2: Data Cleansing

  • Data Cleansing can be done in two ways:

    • Automated, IT based cleanup

    • Business-based, manual cleanup

  • Once the bad data is identified, determine who must do what

    • Business-based,

      • manual cleanup appropriate for more subtle tasks, e.g. to determine which of two duplicates identified should be kept. These tasks may require additional research, phone calls etc.

    • Automated,

      • IT based cleanup good for simpler tasks e.g. making telephone number formats consistent

      • Can be also sub-contracted to specialized data quality companies


IT-based Data Cleansing and Outsource Considerations

  • Data cleansing may take valuable time from the Business, which is not available – Data Cleanup effort may suffer as a result

  • Not all data cleansing is a simple SQL

  • Not all data is most confidential

    When considering data cleansing tasks, look at all possible options

  • Outsourcing some data cleansing tasks may be more economical than doing it all in-house


Step 3: Measure Progress

  • All programs, procedures written with the aim of identifying data quality issues should

    • Be stored, like any other programming assets

    • Be repeatable and be schedulable

    • Provide aggregate measures to describe the data cleanup status e.g.

      • X duplicates

      • Y old records

      • Z invoices without PO

    • Progress

      • Has to be measured in a published dashboard

      • Has to be visible by the entire organization to provide a sense of value


Step 4: Data Hygiene: * Schedule the Cleanup/Review Tasks * Ensure results are visible

  • Bad data is created EVERY DAY

  • Data quality is an on-going effort

  • Establish, publish a schedule, part of the dashboard

  • Ensure there is visibility and accountability to ensure the levels of bad data

    • are going down with time

    • Or are kept at a minimal level


Step 5: Sharpen the Saw

Once the data cleanup cycle is established

  • Review Results

  • Review DQS documents periodically (setup schedule)

  • Get Business input

    • Improve process

    • Give input on improvements to be made

  • Ask the Business to come up with performance improvement measures born from the Data Quality initiative


Conclusion

Two sets of Five activities best define the Data Quality

The Foundation Setup

  • Identify Data Quality Sponsor & Data Quality Boss (DQB)

  • Identify Data Sets

  • Identify Business Side Data Owners

  • Define Data Quality Specifications (DQS) - the DQ Roadmaps

  • Appoint IT Data Cleansing Team

    The Data Quality Cycle – Ongoing

  • Identify Bad Data

  • Initiate Data Cleansing

  • Measure Progress

  • Initiate Data Hygiene, Data Cleanup Cycle

    • Schedule Cleanup/Reviews

    • Ensure progress visible

  • Sharpen the Saw


Links

  • How to improve Data Qualityhttp://www.informit.com/articles/article.aspx?p=399325&seqNum=3

  • Predefined data quality rule definitions

    http://pic.dhe.ibm.com/infocenter/iisinfsv/v9r1/index.jsp?topic=%2Fcom.ibm.swg.im.iis.ia.quality.doc%2Ftopics%2Fpdr_predef.html

  • Creating Effective Business Rules: Interview with Graham Witthttp://dataqualitypro.com/data-quality-pro-blog/how-to-create-effective-business-rules-graham-witt

  • Gartner Magic Quadrant on Data Quality Tools – “Demand for data quality tools remains strong”

    http://www.citia.co.uk/content/files/50_161-377.pdf


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