Data Quality; is this the key to driving value out of your investment in SAP? Data Quality; is this the key to

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1 Driving Whitby Whitby value Partners Partners from Business Driving Intelligence value from Business Business Intelligence Intelligence Whitby Partners 78 York Street London W1H 1DP UK Tel: +44 (0) Data Quality; is this the key to Data Quality; is this the key to driving value out of your investment in SAP? driving value out of your investment in? A BUSINESS BUSINESS INTELLIGENCE INTELLIGENCE WHITE WHITE PAPER PAPER FOR BUSINESS FOR BUSINESS DECISION DECISION MAKERSMAKERS. steve.whitby@whitbypartners.com Whitby Partners 78 York Street London W1H 1DP UK Tel: +44 (0) Whitby Partners Whitby Partners 1 Copyright Whitby Partners

2 Summary SUMMARY One version One of the version truth of is the a term truth long is associated a term long with associated with SAP syste ERP(Enterprise Resource Planning) systems. It sets expectations within an organisation that, finally, there will be a so expectations within an organisation that, finally, there will be a solution delivers that delivers a consistent a consistent set of timely set of and timely accurate and information. All too accurate information. All too often this expectation is not met. Nowhere expectation more so is than not amongst met. Nowhere the recipients more so of than amongst the recipie management reports. management reports. Often this is attributed to over-enthusiastic selling of the software. In reality, the required software functionality exists within Often most this ERP is attributed solutions. Failure to over-enthusiastic to meet selling of the software expectations is usually caused by all parties involved (including services the required partners) software not appreciating functionality the usually exists within SAP. Failur importance expectations that data quality is more plays usually in ensuring caused a by solution all parties involved (includin delivers value and meets expectations. partners) not appreciating the importance that data quality plays i Organisations can address the issues posed by data quality by ensuring a solution that: delivers value and meets expectations. Data quality is included in both their information technology Organisations (IT) and can business address strategies. the issues posed by data quality by ensu IT team members have a comprehensive appreciation Data quality of, not is only included what data in both the their business information technology (IT wants, but how it is to be applied. business strategies. Business team members have an understanding of the limitations IT team of members their data have and a the comprehensive challenges faced appreciation of, not on in ensuring that it is of sufficient quality. All concerned the business have wants, understanding but how it is of to the be tools applied. and processes in place that support data quality. Business team members have an understanding of the limitation data and the challenges faced in ensuring that it is of sufficient We can not understand why SAP Author Comment does not do more to promote Issues related to quality can apply these to hidden many gems different in BW. types of data for example, the definition of facts and figures such as price and cost and time periods such as days, weeks, months quarters etc.. Introduction All concerned have an understanding of the functionality that e SAP and especially SAP Business Warehouse (BW), to support da The organisation that gets data quality right invariably has more cost effective, timely and appropriate Business Intelligence (BI). Based on quality data, BI gives decisionmakers an insight into the size, shape, health and direction of their business. With this insight, they gain a key differentiator in a very competitive and changing world. Data quality should be at the centre of an IT strategy and be based on a pragmatic balance between business needs and technical limitations. Itsʼ effectiveness should be measured. Data quality requirements will change in line 2

3 Author Comment More specifically we look at this question in the context of a Business to Business (B2B) environment. with evolving business needs. Funding & resources should to be allocated to both establish and maintain data quality. In this White Paper, we set out to understand what data quality is and, just as importantly, what it can deliver to an organisation. We examine the different aspects or types of data quality. The scenario of Customer Information is used, where appropriate, to demonstrate practical examples of the challenges faced. By way of example we focus on the apparently simple question of who are our largest customers? We also explore the realities of getting data quality right. Finally we make recommendations as to what steps organisations can take to ensure best practice and help drive significant and tangible return from their investment in BI. One of our first challenges was that we used different reporting time frames. It looked like costing a fortune to sort it out. Fortunately, we had a strong business sponsor who forced through a compromise. The Key Challenges of Data Quality Data quality has many dimensions. We deal with each in turn. Be aware that many terms are used to describe data quality and many are interchangeable. Wherever possible, we attempt to use business rather than technical terms to describe data quality. However, what is most important is not the appropriateness of the term, but the business implication underpinning it. Integrity Often used generically to describe data quality in its specific sense, integrity refers to the matching of data between different logical systems. By way of example, if a customer record and account number are of one size and format on, say, an in-house developed system and a different size and format on a bought in ERP system, then they are lacking in integrity. A common size and format needs to be determined before all the Customer records can be merged. Frequently this is called a common data model, and it is integral in achieving data quality. 3

4 An organisation very fluid will often and can have potentially the need to have merge a far legacy greater demand for data sources of data with other sources. For example, it may be very important both that quantity historical and data scope. is loaded To determine into the the system. level of data quality ne Without historical data, like for like reporting (spotting trends) is not reporting possible requires until months an insight and sometimes into: years after the system is implemented. Additionally many the business needs to be addressed organisations will become involved in some form of merger or acquisition. the Whilst potential sources return of data that from meeting the new this party business need will bring may be eventually transferred over to the dominant ERP system; there the may technical well be a cost pressing of delivering need in the the data short to meet this need term to merge the two (or more!) sets of Customer records the operational cost of sourcing the base data to get early visibility of the Customer base. the cost and practicalities of maintaining the data Getting the data right in the first place is hard enough. Keeping it that way is even more difficult and worse more boring. Before we implemented SAP BW, we spent 40% of our time simply assessing the impact of changes. Consistency Data Migration My data integrity has been sorted; my Customer records are all on one Very data often model. there I is now a need have to a carry single out view a major of my one-off data mig customers! Well, only partly true. Within this single view you may have to bring many data duplicate from legacy records. systems For example; into the IBM new reporting environm UK may be on one record, IBM UK Ltd on another, can present challenges and opportunities. How can we understand International Business Machines on another etc. Additionally, already you may in place? have IBM How UK is it Ltd organised? recorded under What do two we have to do to b separate account numbers. If you want to know who your largest Customers the appropriate are by sales level volume, of quality? you If will this almost is a must do exercise, do certainly want to merge these records into one, at least for an opportunity to formalise a Data Quality Program? reporting purposes. Equally for Supplies, you may wish to do this to get higher rebates. If this is not enough of a problem, the next challenge is that many Customers belong to Groups. Maintenance When we ask who our largest customers are, we usually mean discrete customers and groups. We are not only There interested is a temptation in the consolidated to view data view quality of IBM as UK, a discrete program. O but also in the tance consolidated is how will view the of data IBM be Group. maintained Finally, once it is place? How f when looking at trends over time, for example who was our largest What customer tools last are year, in place this to year support etc.? it? there Who is will be responsible? W another challenge: organisations frequently acquire or divest. How management and when do procedure we reflect should this in our be applied? largest How will we accommo customer report? In summary, we need to de-duplicate impact of organisational changes? and/or merge records for our report and a large part of this process is a business rather than a technical judgement. Security Data security, whilst arguably not a data quality issue, can become particularly when data is loaded into a Data Warehouse from und systems. For example, creating a common view of Customers and th profitability is likely to be highly sensitive data. Who can access it? that access be controlled? 4

5 Driving Driving value value from from Cleanliness A Customer record may also contain dirty data. Simple examples of this might be IB N UK when we meant IBM UK or an incorrect post (zip) code. There will be a requirement to identify these errors and clean them. Whilst the post code may not appear to be relevant to the report, it may well be relevant for the cleaning process and follow-up actions. For example, one of the easiest ways of identifying the duplicate customer names may well be by matching the post codes. Our first pass at Market Analysis was meaningless. We had nine records for the same Customer. We reckoned it should have been one Group with three subsidiaries. Completeness This essentially refers to the absence of key elements of data that might be required for our report. For example there may be no post code present, or no account manager recorded. Clearly, we need to understand what fields are missing and in what quantity. We will also need to assess the difficulties, costs and effort involved in making them complete. Finally, we should do this in the light of the business benefits that this investment will bring. Relevance Data can have integrity, be consistent, clean and complete but still potentially not deliver the business insight required. A supposedly simple question such as How many Customers do we have? may have different answers depending upon the context of the question. For example how many customers do we have with payment arrears? may be readily answerable within the existing data. How many customers do we have in, and what is our market share of, say the Insurance market, may not be. Do we count gross sales or net sales and how do we accommodate rebates? How do we handle the fact that we count Zurich Insurance as eight accounts, whereas the market data defines them as three accounts? It would seem that the question of how many customers do we have? has a different answer depending upon the perspective. Is it really correct that the Finance Department say 2,000 and the Marketing Department say 1,500? 5

6 The realities of Data Quality Consultant Comment Timeliness of data is critical to user satisfaction. Whilst it is not strictly a data quality issue it is important to recognise that a data quality strategy needs to fit within the restrictions of business timetables and system performance. How adequate is the base data? The reality is that most organisations will want many differing views of the same set of data. Sometimes that data is readily available from the transaction/operational systems, more often it is not. Additionally, even if quality data is available in its basic form, it will still need to go through significant transformation before it is fit for purpose. For example, answering the question; Which were our fastest growing sales divisions over the last three years? If there has been a reorganisation of the sales divisions within the period, customer accounts will need to be mapped to a common divisional structure. How can I get at that data? The technical view of data quality is How integrated, clean and complete is it? The business view is How relevant is the data and, just as importantly, can I get at it in a reasonable timeframe? Making data relevant and accessible will inevitability lead to the need to implement some form of Enterprise Data Warehouse (EDW). An EDW is essentially a separate server or computer on which data from multiple sources can be loaded and transformed into a form that is required for reporting. From a technical perspective, the EDW also addresses the key needs of protecting the stability of the underlying transaction systems/security and performance. What level of Quality do I need? One hundred percent data quality may be achievable in some isolated areas but not across an organisation. The need for data quality is determined by two basic requirements. Firstly, do we have the correct data to run our operational systems and secondly, do we have good enough data to deliver the desired reports and support our decision making process. Generally, the operational requirements are easier to determine. Reporting requirements can be much more challenging in that reporting needs can be very fluid and potentially have a far greater demand for data in terms of both quantity and 6

7 scope. To determine the level of data quality needed for reporting requires an insight into: the business decision-making needs to be addressed the potential return that meeting this business need will bring the technical cost of delivering the data to meet this need the operational cost of sourcing the base data the cost and practicalities of maintaining the data Data Migration Getting the data right in the first place is hard enough. Keeping it that way is even more difficult and worse more boring. Before we implemented our EDW, we spent 40% of our time simply assessing the impact of changes. Very often there is a need to carry out a major one-off data migration exercise to bring data from legacy systems into the new reporting environment. This can present challenges and opportunities. How can we understand what data is already in place? How is it organised? What do we have to do to bring it up to the appropriate level of quality? If this is a must do exercise, does it present an opportunity to formalise a Data Quality Program? Maintenance There is a temptation to view data quality as a discrete program. Of equal importance is how will the data be maintained once it is place? How frequently? What tools are in place to support it? Who will be responsible? What change management procedure should be applied? How will we accommodate the impact of organisational changes? Security Data security, whilst arguably not a data quality issue, can become relevant, particularly when data is loaded into a Data Warehouse from underlying systems. For example, creating a common view of Customers and their associated profitability is likely to be highly sensitive data. Who can access it? How will that access be controlled? How will that control be audited? 7

8 Additionally, some data may be share price sensitive. Where this is the case, Organisations are obliged, by law, to maintain an appropriate level of security. Managing the Process Many organisations discover that the process to achieve data quality will be complex and time consuming. There will be many sources of data to be extracted from underlying systems; these sources may be ready for extraction at different times, may require rapid extraction to keep reports current, have different transformation rules and require different levels of intervention. The volume of data may be large and the time available for extraction, limited by the mission critical nature of the underlying system. Data volumes may dictate that only records that have changed be uploaded. How and where will these changes be identified? How will we know what systems are ready for extraction? What if certain files are unavailable, how can we ensure data is not loaded twice? How can we ensure that this process is managed in a disciplined and costeffective manner? How can we ensure that test data for systems can be kept separate from operational data? Data Audits In the Financial Services sector, Sarbanes-Oxley and Basel II are Government mandates on the quality of data. Not only must data be correct, it must be demonstrably so and auditable. There will be times when there is a requirement to not only to have quality data but also to prove or audit the quality of the data. In certain markets, for example Financial Services, this is becoming a legislative requirement. There will be a need to have a fully documented and comprehensible process that shows how the reported data was arrived at. It is also worth noting that this is an essential foundation to cost effective maintenance. 8

9 Key Recommendations Organisations should ensure a balance exists between the Business and IT requirements, in particular, that; If we had been told what they (the Business) planned to use the data for, then we (the development team) would have taken a different approach. Data quality is included in both their information technology (IT) and business strategies. IT team members have a comprehensive appreciation of, not only what data the business wants, but how it is to be applied. Business team members have an understanding of the limitations of their data and the challenges faced in ensuring it is of sufficient quality. Where possible, business requirements are specified in light of data quality. Business cases take in to account the dynamic nature of BI. Metrics to determine success are put in place and monitored. Resources are allocated to sustain success once it is achieved. Responsibility and Accountability for Data Quality is established - this may require the creation of new roles. A careful review of existing investments in BI should be performed before procuring new software to confirm that: All concerned have an understanding of the functionality that exists within existing licensed software. That independent advice and guidance is obtained. 9

10 Conclusion Data quality is a corner stone of any successful ERP system. The level of data quality required by an organisation needs to be dictated by the benefits it will bring - and this may vary by data type. Frequently the optimal place to address Data Quality is within the underlying transaction system. However the level of Data Quality does not need to be perfect, merely good enough to support the required level of decision making. Having the business and IT sharing a common view of what is good enough is essential. To achieve this, Accountability and Responsibility for Data Quality (Stewardship) and the supporting processes and technology need to be in place. About Whitby Partners Whitby Partners is an organisation dedicated to Business Intelligence. In particular the goal of driving the adoption of within organisations to ensure significant and tangible return on investment. Focusing on the front end and back end of the business intelligence lifecycle we offer services to help customer define and develop their BI strategies and work with customers during execution to ensure the resulting BI solutions get adopted and deliver business value. Copyright Whitby Partners, no part of this document may be copied or distributed without permission. Whitby Partners 78 York Street London W1H 1DP UK Tel: +44 (0) steve.whitby@whitbypartners.com 10

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