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Monitor the Quality of your Master Data THOMAS RAVN March 16th th 2010, San Francisco
© Platon Platon A leading Information Management consulting firm Independent of software vendors Headquarter in Copenhagen, Denmark 220+ employees in 9 offices 300+ customers and 800+ projects Founded in 1999 Employee owned company Platon received good feedback in our satisfaction survey. Clients cited the following strengths: experience and skill of consultants, business focus and the ability to remain focused on the needs of the client, and a strong methodological approach Gartner July
© Platon Key Concepts and Definitions MDM Information Management is the discipline of managing and leveraging information in a company as a strategic asset Master Data Management (MDM) is the structured management of Master Data in terms of definitions, governance, architecture, technology and processes Data Governance Data Governance is the cross- functional discipline of managing, improving, monitoring, maintaining, and protecting data Information Management 3 Data Quality Management is the discipline of ensuring high quality data in enterprise systems DQM
© Platon Components of an effective MDM approach 4 MDM Business ownership, responsibility, accountability Common definitions Effective Master Data processes Data Quality Management Protect, validate and integrate data in IT applications IT Change control Formalize business ownership and stewardship around data. Ensure that Master Data is taken into account each and every time a business process or an IT system is changed. Control in which systems Master Data is entered and how it is synchronized across systems. Manage Master Data Repository. To be able to share data you need to share definitions and business rules. Definitions require management, rigor and documentation. Capturing Master data efficiently needs to be built into the business processes. Equally consistent usage of Master Data needs to be ensured across business processes and business functions. Measure and monitor the quality of data
© Platon Typical Data Problems NoNameAddressPurchase IBM187 N.Pk. Str. Salem NH , I.B.M. Inc.187 N.Pk. St. Sarem NH , International Bus. M.187 No. Park St Salem NH , Int. Bus. Machines187 Park Ave Salem NH , Inter-Nation Consults15 Main St. Andover MA , Int. Bus. ConsultantsPO Box 9 Boston MA , I.B. ManufacturingPark Blvd. Boston MA , How much did we spend with IBM last year?
© Platon Typical Data Problems NameStreetZip CodeCity CAFÉ SPORTSCLUB15 3 rd Street10001New York CAFÉ SPORT KLUB15 Third St..NYC Is this the same customer? Are these the same products? Description, System 2 1 L Cappucino - Mathilde Cafe FETA W/OLIVES & GARLIC 60G, ML YOG. PEACH/BANANA Description, System 1 1/1L Mathilde Cafe Ice Cappucino 45+ FETA M/OLI+HVIDL 60G, 45+ YOGHURT PÆRE/BANAN, 1000ML
© Platon Typical Problems - 3 A common problem is overloading of fields, which is the misuse of a field compared to the intended use. Often because the field the user wanted to use wasnt available in the application Sometimes a field might even have been used for different purposes by different parts of the organization 7 Customer NoName Fax Customer
© Platon Where Does the Bad Data Come From? 8 State is a required field – regardless of country
© Platon Where Does the Bad Data Come From? 9
© Platon Top 5 Sources of Bad Data 1.Lack of ownership and clearly defined responsinility 2.Lack of common definitions for data 3.Lack of control of field usage 4.Lack of process control 5.Lack of synchronization between systems 10
© Platon What is Good Data Quality? 11 Larry English: Quality exists solely in the eye of a customer of a product or service based on the value they perceive Information quality is consistently meeting end customers expectations through information and information services, enabling them to perform their jobs effectively To define information quality, one must identify the "customer" of the data - the knowledge worker who requires data to perform his or her job Larry English: Quality exists solely in the eye of a customer of a product or service based on the value they perceive Information quality is consistently meeting end customers expectations through information and information services, enabling them to perform their jobs effectively To define information quality, one must identify the "customer" of the data - the knowledge worker who requires data to perform his or her job Platon definition: Data Quality is the degree to which data meets the defined standards Platon definition: Data Quality is the degree to which data meets the defined standards
© Platon Information producers will create information only to the quality level for which they are trained, measured and held accountable. Larry English The Law of Information Creation 12
© Platon Data Standards & Data Quality Its all about the Meta Data… 13 Good Meta Data is prequisite to achieve great data quality (inferred from the trained part of the Law of Information Creation) You can only achieve high quality data if you have standards to measure against!
© Platon Defining Good Data standards 14 Business description Data entry format and conventions Definition owner Stakeholders Definition and keys Life cycle Classification(s) Hierarchies For every entity define:For every field define: Consider what a user needs to know to produce high quality data Business Owner(s)
© Platon15 Data Standards – An Example Challenges Relating the data definitions to the process documentation Keeping the definitions up to date The same piece of information may be entered in multiple different systems
© Platon Defining Good Data standards There are two basic approaches to defining your data standards 1.Define a system independent Enterprise Information Model and then map attributes to system fields, or 2.Define data definitions for a system (screen/table) specific view of data If you have one primary system where a data entity is used, option 2 is preferable If you have many different systems where the same data entity is used, option 1 is preferable 16
© Platon Generating Garbage Garbage In = Garbage Out Quality Standard1 In + Quality Standard2 In = Garbage Out 17
© Platon18 Data Quality Monitoring Like most other things, data quality can only be managed properly if it is measured and monitored A data quality monitoring concept is necessary to ensure that you identify Trends in data quality Data quality issues before they impact critical business processes Areas where process improvements are needed
© Platon Data Quality Monitoring For this to work, clearly-defined standards, targets for data quality and follow-up mechanisms are required There is little point in monitoring the quality of your data if no one in the business feels responsible and if clear business rules data have not yet been defined Thus a data quality monitoring concept should go hand in hand with a data governance model 19
© Platon The Dimensions of Data Quality Validity Accuracy Consistency Integrity Data Quality Timeliness Completeness Does data reflect the real world objects or a trusted source? Are business rules on field and table relationships met? Are shared data elements synchronized correct across the system landscape? Do we have all required data? Are all data values within the valid domain for the field? Are data available at the time needed? 20
© Platon KPI Examples in the different dimensions DimensionKPI Example CompletenessPct of active customer records with an address ValidityPct of active US customers with a phone number of 10 digits AccuracyPct of active customers with an mailing address that is verified as correct against Dun & Bradstreet ConsistencyPct. of customer records shared between our CRM system and our ERP system that has identical values for name, address and telephone number. IntegrityPct. of active product records with [type] = Service where [weight] = 0, or Pct. of open sales orders that refer to an active customer. TimelinessPct. of supplier records where the time from request of a new record to completion and release of the record is less then 24 hours 21
© Platon22 The Dimensions of Data Quality Business Impact Difficulty of Measurement Completeness Validity Integrity Timeliness Consistency Accuracy
© Platon23 The steps in building a monitoring concept Building a data quality monitoring concept involves the following five basic steps: 1.Identify stakeholders 2.Conduct interviews with stakeholders and selected business users 3.Identify data quality candidate KPIs 4.Select KPIs for data quality monitoring 5.For each KPI, define details
© Platon Finding Good Data Quality KPIs Perform a thorough data assessment (profiling) exercise searching for common data quality problems and look for abnormalities Collect business input Business process requirements Data quality pain points Business Intelligence Business KPIs XXX DEFINED KPIs KPIFrqTargetUoM A B C KPI Candidates To find good data quality KPIs collect business input through interviews with stakeholders (use Interviewing Technique) and a data assessment. The technique Data Profiling contains more details on how to analyze data 24
© Platon Tying Data Quality KPIs to Business Processes It is essential that KPIs are not just made up, so your organization has something to measure Dont measure data quality because its great to have high quality data. Measure it because your business processes depend on it Derive data quality KPIs from business process requirements Start with a high level business process like procurement (also known as a macro process) and then break it down. 25
© Platon Tying Data Quality KPIs to Business Processes Procurement No duplicate vendors Correct industry code for vendors Correct placement in hierarchy (parent vendor) Correct address for vendors Business Meta Data DEFINED KPIs KPIFrqTargetUoM A B C Data quality requirements Business Meta Data is required to define the actual KPIs. Ex: A vendor record is uniquely defined as an address of a vendor where we place orders, receive shipments from or….. Define the data entities used within the process Material Master Data Data Entity Scope Macro process Process Is the required data quality aspect meaningful to monitor? It may be better to improve data validation or perhaps problems are not experienced Spend analysis Vendor Selection 26 Vendor Master Data
© Platon Tying Data Quality KPIs to Business Processes Using a simple model like the one illustrated on the previous slide allows you to tie data quality KPIs to business processes and to business stakeholders This relationship is critical for the success of the data quality monitoring initiative. Clearly illustrating how poor data quality impacts specific business processes is instrumental in getting the executive support and the business buy in When conducting data quality KPI interviews you may encounter KPI suggestions like measure if there is a valid relationship between gross weight and product type. Ask why this is important and which process this is important for A particular data quality KPI may be important for multiple different processes. Document the relationship to all relevant processes 27
© Platon Defining Data Quality KPIs Data quality KPIs should express the important characteristics of quality of a particular data element Typically units of measures are percentages, ratios, or number of occurrences For consistency reasons, try to harmonize the measures. If for instance one measure is number of customers without a postal code while another is percentage of customers with a valid VAT-no a list of measures will look strange, since one measure should be as high as possible, and the other as low as possible A good simple approach is to define all data quality KPIs as percentages, with a 100% meaning all records meet the criteria behind this KPI Be careful not to define too many measures, as this will just make the organizational implementation more difficult Pay attention to controlling fields (like material type) that may determine rules like whether a specific attribute is required 28
© Platon Defining Hierarchies Use hierarchical measures where possible, so that measures can be rolled up in regions and countries for instance In the below example a KPI related to customer data is broken down in individual countries to allow detailed follow up A concern here is that fields may be used differently in different countries. Given the below data insight, it might make sense to define a separate KPIs for CA and perhaps ignore MX and US KPI: Customer Fax number correctly formatted US Customers CA Customers MX Customers 5% 43% 77% Value Avg. Value 25% Recs 85,000 38,000 19,000 Data Insight Fax numbers are not required for US customers since all communication is done via . Fax is the primary communication channel with Canadian customers. Only some customers in Mexico have a fax machine. 29
© Platon Defining KPI Thresholds Along with each KPI two thresholds should be defined: Lowest acceptable value Without specifying the lowest acceptable value (or worst value), its difficult to know when to react If the measure falls below this threshold action is required Target value Without target values, you dont know when the quality is ok. Remember fit- for-purpose Specifying a low and target threshold allows for traffic light reporting that provides an easy overview Defining appropriate thresholds can be difficult as even a single product record with wrong dimensions may cause serious process impact. But without any indication of when to be alerted any form of automated monitoring is difficult Target Value: 95 % Lowest acceptable value: 80 % 30
© Platon 31 Indirect Measures Consider critical fields (e.g. weight of a product or customer type) where the correct value is of utmost importance, but its close to impossible to define the rules to check if a new value entered is correct…. One approach is to measure indirectly by for instance reporting what users have changed these values for which products over the last 24 hours, week or whatever is appropriate in your organization
© Platon Cross field KPIs and Process KPIs Common KPIs that are not related to a single field Number of new customer records created this week Average time from request to completion of a new material record Number of materials with a non-unique description (or pct. of materials with a unique description) Number of vendors, where a different payment is defined in different purchasing organizations Number of open sales orders referring to an inactive customer 32
© Platon Think Prevention! Every possible business rule related to completeness, integrity, consistency and validity should be enforced by the system at the time of data entry. If it isnt, consider implementing a data input validation rule rather than allowing bad data to be entered and then measure it! However, there are cases, where the business logic of a field is too ambiguous to be enforced by a simple input validation rule. Process (workflow) adjustments may also be the answer. 33
© Platon34 Documentation of KPIs KPI Name:A meaningful name of the KPI that expresses what is being measured Objective:Why do you measure this? What business processes are impacted if there data is not ok? Dimensions:What data quality dimensions (integrity, validity, etc.) are this KPI related to? Frequency of measure:How often do you wish to report on this KPI? Daily, daily, weekly or monthly? Unit of measure:What is the unit of the KPI? Number of records, pct of records, number of bad values, etc.? Lowest acceptable measure: Threshold that indicates if the data quality aspect the KPI represents is at a minimal acceptable level. The value here must be in the unit of measure of the KPI. Target value:At what value is the KPI considered to represent data quality at a high level? Responsible:The person responsible for the particular KPI. Formula: The tables and fields that are used to analyze and calculate the KPI. This is the functional design formula that forms the basis for the technical implementation. Hierarchies: When reporting on a KPI it is very useful to be able to slice and dice the measure according to different dimensions or hierarchies. For a customer data KPI for instance, good hierarchies would be regions, country, company code and account group. Being able to view the KPI through a hierarchy also makes it easier to follow up with specific groups of business users. Notes and assumptions:If certain assumptions are made about the KPI make sure to document it here
© Platon35 Remember! Quality is in the Eye of the beholder! Data quality is defined by our Information Customers Data is not always clean or dirty in itself – it may depend on the viewpoint and a defined standard Focus on whats important to those that use the data
© Platon Monitoring Process A simple example 36 Publish KPI Analyze KPIs Evaluate root cause Implement Improvements Plan corrective actions Low value in KPI? Y N
© Platon37 Monitor the Quality of your Master Data Thomas Ravn Practice Director, MDM E: M: PLATON US INC. 5 PENN PLAZA, 23 rd Floor NEW YORK NY
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