Mark Humphries – Data quality: delivered


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In 2009 koos groene energieleverancier Essent België ervoor om hun Customer Care en facturatiesysteem up te daten. Essent maakte van deze opportuniteit gebruik om alle bedrijfsprocessen die gericht waren op “customer and operational excellence” te optimaliseren. Om dit project te verwezenlijken, waren er drastische verbeteringen in datakwaliteit en -beheer nodig. Dit is het verhaal van hoe Essent België succesvol van niveau 1 naar niveau 2 gaat met een Data Quality Maturity Model.

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  • This is an animated slide that explains how the deregulated energy market works. It shows the flow of energy, of information (in contracts) and of money. Important points to note here, are: - an energy supplier converts the information from the customer into orders for the wholesale market and how SLPs and EAVs work here - the balance responsible converts this into orders for individual power stations - the money follows the data and the power, and that’s why it’s worth buying green energy from your supplier - the supplier and the balance responsible are new roles in the deregulated market – neither has assets – both rely completely on data and money for their business
  • This is the business case as it stands after Dynamo – corrected to show what the actual investment was, as well as which costs we can now save, based on the solutions that we actually implemented. As with most business cases it is not as favourable when it was initially made, but it is still very positive.
  • This is an animated slide that shows a real example of how poor data quality actually happens. The most important message here is how shocking, real life examples can complement the business case. It helps those who don’t intuitively “get” data quality to understand why data quality is costing us so much money and how the proposed changes can help to eliminate the problems. Although it is based on a real life example, the names and addresses have been changed to protect the innocent. All the errors shown here could be eliminated by systematic controls of addresses with Road 65, systematic checks with the Access register, systematic checks on existing customers before creating new customers, and a little more attention on behalf of the end users – like checking the name and address that the customer writes on a letter.
  • This is an architectural view of the DQ & Migration platform. Important points to note here include DQ & Migration worked on the same platform, allowing us to focus on DQ issues that were relevant to migration. Maximum use from 3rd party tools and data was used to validate and correct our data. There is a difference between tools and data. For other issues we had to build the logic ourselves, like validation of VAT numbers or bank account numbers The platform used both 3rd party products and custom built solutions as appropriate. But the whole was integrated into a single, consistent platform. The data pump was an important custom built element. This enabled us to make corrections at the business logic layer, thereby ensuring application integrity. All updates followed the same structure – object name, unique key, old values and new values. Updates are only committed if the old values have not changed. This is more robust than working with timestamps. The users play an important role in the whole, because they can correct the cases where the rules do not give an obvious answer. Although the numbers are often smaller than what can be corrected automatically, these are frequently the most important changes which bring the most value.
  • Mark Humphries – Data quality: delivered

    1. 1. Data Quality Delivered Essent Belgium is maturing
    2. 2. Our business <ul><li>We buy energy from the wholesale market </li></ul><ul><li>We sell energy to the retail market </li></ul><ul><li>Selling price – purchase price </li></ul><ul><ul><li>gross profit </li></ul></ul><ul><li>We collect transport, distribution and taxes </li></ul><ul><li>That´s all we do </li></ul>
    3. 3. Project Dynamo Objectives : - Simplification and automation of processes - Focus on error handling - Increased data quality - Support for new market processes (MIG 4.0, SEPA, …)
    4. 4. The financial business case All amounts in k€
    5. 5. The shocking business case customer moves new customer 11 Customer number 67635725 Name Francoise Declerc Date of birth 5/7/1969 Address Rue Du Chene 12 4680 Hermée Customer number 67268392 Name André Richard Date of birth null Address Rue Georges Simenon 7 4680 Hermée EAN 541456700000827610 Address Essent Rue Georges Simenon 7 4680 Hermée Address DGO Rue Georges Simenon 7 4680 Oupeye EAN 541456700000672615 Address Essent Rue Du Chene 12 4680 Hermée Address DGO Rue du Chêne 12 4680 Oupeye Customer number 67256292 Name Andre Richard Date of birth 15/3/1971 Address Rue du Chêne 12 4680 Oupeye
    6. 6. Cleaning for Dynamo Customer Installation Tools Data extract correct update insert export manual cleansing Axapta Back Office DQ & Migration Mecoms Dynamo WDM Graydon Access Register DNB Snapshots Road 65 data pump scripts Human Inference
    7. 7. From reactive to proactive
    8. 8. Process or Data? <ul><li>Errors in incoming data </li></ul><ul><li>Errors in master data </li></ul><ul><li>Errors in reference data </li></ul><ul><li>Bugs in processes </li></ul><ul><li>All lead to errors in output data </li></ul>New data in Master Data Reference Data New data out
    9. 9. The 80 – 20 rule VW Golf €12.000 VW Touareg €60.000
    10. 10. Data Quality & Lean <ul><li>Monthly cycle evaluating validity </li></ul><ul><li>Focus on input needs of processes </li></ul><ul><li>Business Process Analysts </li></ul><ul><li>Process Improvements & Cleaning </li></ul>
    11. 11. What’s next?