Data Quality Assessment - Better Data

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Deloitte’s data quality assessment Better data. Better business.
How we can help Our data quality assessment is a fast and effective way of quantifying the problem: 1. Select a business process / data set 2. Analyse the quality of data 3. Identiy the impact to the business 4. Identify options to address and resolve the problem to deliver a positive ROI. Using market leading data quality and profiling tools combined with our unique business insights and perspectives, we can quickly identify data quality issues and tell you how they impact your business. Our philosophy is simple – data quality improvement must deliver a positive ROI. With better data, you can build and run a better business. Data only exists to support business applications. Data quality should only be measured in terms of its fitness for specific business purposes. Few organisations collect or maintain data that is able to truly meet business demands. Though the costs of poor data are significant, the ability to generate positive returns on investments from data quality projects are undoubted. Business relies heavily on information technology to support day to day operations and provide intelligence and insight on performance. Technology in turn is only as good as the data that runs through it – ‘rubbish in, rubbish out’ remains a fundamental truism. We have a structured approach to pinpointing and tackling data quality issues, helping you to quantify the quality of your current data and then providing practical suggestions to help you create fit for purpose data. Common symptoms of poor data quality • Do you have multiple instances of customer, product and supplier data? • Does it take a long time to gather reliable management information? • Do your customers complain that you don’t get their details right? • Does your business run on spreadsheets? • Are your order to cash and procure to pay processes beset by delay, error and rework? • Do your management reports not add up?

Figure 1: Data quality impact
Order to cash and customers Quality customer data - to increase O2C efficiency, reduce costs and enhance customer intelligence Quality assets data - will avoid errors in financial statements and reduce asset management costs Quality vendor data - will enable volume discounts, improve cash flow and vendor analysis Quality financial data - will speed up reporting processes and increase accuracy Quality materials data - will address production inefficiencies and quality problems

Example processes

Capital additions, disposals and fixed assets Procure to pay and vendors Record to report and GL structure Inventory, production and materials

“Data quality has a direct and significant impact on your top and bottom lines”.
What we deliver Depending on scope, an assessment is typically 2-3 weeks culminating in a report and presentation that includes: An executive summary: a summary of the results of the analysis, drawing out the key findings, risks and recommendations. Data quality results summary (see Figure 2): a breakdown of the profiling result by data set and field, the results summary highlights areas of greatest risk and summarises the headline results at a data field level. Data quality risks and recommendations (see Figure 3): describes full findings and outlines risks related to quality issues identified. Describes recommendations to address data quality issues and mitigate risks. Figure 2: Data quality assessment results summary
Profiling results summary Organisation Data Object Business Process (Owner) System(s) Profiled Sample Date Profiled XYZ plc Vendor Master Procure to Pay (A Smith) SAP, E-Procurement 100% of records 1st May 2008 1.2 Vendor Payment Terms

Figure 3: Data quality assessment risks and recommendations
Overall Risk Summary

Data element

Headline findings

Risk and recommendations

1.1 Vendor Name

300 active names identified in SAP with no corresponding e-Procurement name.10% Vendor names duplicated in e-Procurement. 25% of names do not correspond with external list database of company names

Risk: Duplicate vendor records make it difficult to run spend analysis and negotiate volume discounts. Risk that duplicated orders and payments will be initiated Recommendation: Remediate vendor records, removing duplicates and use external source to standardise names. Implement create and update control over vendor master Risk: Impact on cash flow if payments being made within policy standard terms Recommendation: Review all non-standard payment terms against contracts

22% of vendor payment terms not in line with company policy in SAP

Data element

Headline findings

1.1 Vendor Name

300 active names identified in SAP with no corresponding e-Procurement name. 10% Vendor names duplicated in e-Procurement (See R1.1) 22% of vendor payment terms not in line with company policy in SAP (See R 1.2) 6% of vendors flagged as electronic settlement have no corresponding bank details (See R 1.3) 12% of vendors identified as inactive in SAP remain active in e-Procurement (See R 1.4)

1.2 Vendor Payment Terms

Moving up the maturity curve The data quality assessment is most likely to result in a series of tactical projects to deliver some immediate business value. But what then? By implementing controls, metrics and governance in a tactical way, the business moves some way up the data maturity curve. How then does the business stay there and continue to improve and mature? How does the business tackle quality issues at a cross functional and even international level? And to avoid repeating and duplicating governance structures and quality reporting, how can these initiatives be consolidated and driven in a more strategic manner? Deloitte’s broader data maturity service offerings are tailored to addressing these questions and building a business case to deliver. Contacts Craig Turnbull Director [email protected] Cell: (0)83 307 1307 Direct: +27 (0)11 806 5415 Andrew Broeders Senior Manager [email protected] Cell: (0)74 122 2888 Direct: +27 (0)11 806 6192

Consistency

Relevance

1.3 Vendor Bank Details

1.4 Vendor Active Flag

Deloitte refers to one or more of Deloitte Touche Tohmatsu, a Swiss Verein, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/za/aboutus for a detailed description of the legal structure of Deloitte Touche Tohmatsu and its member firms. © 2009 Deloitte & Touche. All rights reserved. Member of Deloitte Touche Tohmatsu Designed and produced by the Studio at Deloitte, Johannesburg. (10085/les)

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