[Webinar Slides] How to Increase Your Profits by Improving Your Data Accuracy
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The document discusses improving data quality and accuracy. It begins with an introduction of the speakers, Seth Maislin and Greg Council. Maislin then discusses establishing governance over company data and linking data quality metrics to business objectives and key performance indicators. Council discusses measuring the functional and performance abilities of data extraction systems, particularly focusing on accuracy at the data element level rather than just character recognition rates. He outlines Parascript's programs for optimizing systems and processes to ensure high quality data extraction.
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[Webinar Slides] How to Increase Your Profits by Improving Your Data Accuracy
- 4. Underwri(en by: Presented by: Quality vs. Profitability: An Observa(on Features: • poor data = business loss • diminishing returns • “good enough” exists • culture ma(ers
- 5. Underwri(en by: Presented by: Correla(on or Causa(on? § Most organizaKons acknowledge the correlaKon between data quality and business value, but few can say which metrics of data quality maRer most. § With data, “good enough” is a strategic choice. If you can extract … Then you can improve … zip codes geographical analysis names of dependents fraud detecKon dollar amounts arithmeKc validaKon, workflow everything over Kme United States tax and financial systems anything savings, speed, capability, growth, … U.S. Federal Tax Form
- 6. Underwri(en by: Presented by: Governing Data “ARrac(veness” § Data quality is mulKdimensional. § Which features ma(er most? Why? (How do you set targets?) § It’s nearly impossible to opKmize all dimensions at once. • accurate • available • certain/precise • clean • complete • consistent across sources • integrity • formaRed (conformity) • reasonable/logical • relevant/valid • reliable • (mely/current • traceable/audited
- 8. Underwri(en by: Presented by: Data Is an Asset • something you can sell • direct input for a business process • contextual input for making a business decision • workflow trigger • macro-level self-awareness (governance) To know if your data are good enough, you don’t measure the data. You measure its effects. Data governance requires an integrated system of metrics and KPIs.
- 9. Underwri(en by: Presented by: Processes enable objecKves L I N K A G E Enterprise Strategy Revenue Growth Content supports processes ObjecKves align with strategy CEO: “How will this increase revenue?” Business Processes Measuring here (process indicators) Sessions Search Relevance Leads Conversion Rates Process Scorecards Business Unit ObjecKves Measuring here (business outcomes) New Business Opportuni(es Average Order Size Total Account Revenue Outcome Scorecards Digital Team: “How do I know taxonomy / content / search is working?” Digital Content Working & Measuring here (content, IA, taxonomy, search, product data, etc.) Site Search Digital Content & SEO Web Analy(cs Content Scorecards CTR Data Fill Content etc. Metrics & KPIs Focus Governance
- 10. Underwri(en by: Presented by: Gedng Data Quality to Pay Off Enterprise Strategy Business Processes Business Unit ObjecKves Digital Content • deliverables quality • good return on IT spending cost / profit • increased throughput growth • prioriKzed team behaviors produc4vity • error reducKon / compliance liability L I N K A G E • brand percepKon, reputaKon quality • revenue dollars cost / profit • customers, market share growth • business strategy realizaKon produc4vity • market awareness liability
- 11. Underwri(en by: Presented by: Use Cases: How to Leverage Financial Data • accurate and Kmely accounts management • be(er spend alignment with business strategy; innovaKon recogniKon • improved decision making from data-driven ROI; smarter R&D • acKve asset management (human, technology, physical, digital) • smart business culture design • intelligent asset and partner acquisiKon, compeKKve procurement • strong risk management • improved compliance • customer and marketplace intelligence (correlaKons, shocks, trends, gaps) • streamlined operaKons
- 12. Underwri(en by: Presented by: Building the linkage is the hardest part. 1. Establish a system of company-wide governance. § IdenKfy and understand the company’s data domains § Document processes and assign accountabiliKes § Establish a Center of Data Excellence, and socialize § Implement metrics to measure governance itself 2. Decide what ma(ers at your level. § Know the strategic KPIs for each part of the business § IdenKfy gaps between exisKng and necessary metrics § Inventory and profile your data; calculate baselines § Set targets collaboraKvely and find synergies 3. Build your data projects roadmap. L I N K A G E
- 13. Underwri(en by: Presented by: Other Resources and Contact § The Evolving Role of the Chief Data Officer: Increasing Influence in the C-suite (on-demand webcast) § Digital Governance: It’s a Numbers Game (Metrics, KPI’s, and ROI, that is) (arKcle) Seth Maislin seth.maislin@earley.com 781-775-0684 @sethmaislin www.earley.com
- 16. Underwri(en by: Presented by: Observa(ons and Ques(ons PERFORMANCE ABILITY Many organizaKons focus too much on func8onal capabiliKes of the system, and not the performance of the system. § What is the error rate of your staff? § What is the error rate of your systems? § What is the actual uplio/cost created by your systems? Is it wrong to ask “how accurate is your system”? (For example, OCR accuracy?) § Yes and No – Know what to ask for What is the best way to measure performance, and how can high performance be achieved? System Data Flow
- 22. Underwri(en by: Presented by: Underwri(en by: Presented by: Discussing “Accuracy” § Is OCR accuracy relevant? Yes but…. • It doesn’t tell you data-level error of a system; typically only character-level accuracy. • It doesn’t inform you of the cost savings or producKvity gains. o For example, “How many data elements do not require any intervenKon or review?” § Your data accuracy is not the same as the OCR accuracy. § Most (or all of) OCR accuracy is measured at the character level. § Your form data is important at a word or field level. If OCR output is 99% accurate at the character level, then considering a page with 500 words with an average of 6 le(ers per word, 30 characters will be incorrect. Those 30 errors can be distributed across mulKple words leading to a higher actual error rate. A word-level error rate could be 6%, not 1%. If part of the 6% error includes an account number of SSN, that’s a big problem.
- 23. Underwri(en by: Presented by: Gedng at Actual Data Accuracy How much data is located and what is the error rate field-by-field? With this informaKon, you can have a solid understanding of gains. § SSN has a read rate of 85% and error of .5% translates to: Only 15% of data requires any review at all. § The remainder can go straight through. READ RATE ERROR RATE 100% 100% Confidence threshold = 15 Confidence threshold = 50 FuncKonal- only System SSN Other Data
- 24. Underwri(en by: Presented by: When It Comes to Performance PERFORMANCE ABILITY Classifica(on: § Recall and precision. A document classifier will not correctly idenKfy all documents and performance is key. • Precision (Relevance). If the system idenKfied 8 invoices in a batch of 20 documents, where 5 are correct and 3 are incorrect, precision is 5/8 or 62.5%. • Recall (Completeness). If there were actually 15 invoices in the batch of 20, recall is 5/15 or 33.3% Data Loca(on/Extrac(on: § Read Rate. The percentage of data that is located and extracted. § Error Rate. The percentage of that located/extracted data that is not correct. § Confidence Threshold. The staKsKcally-significant point that allows for data to move straight through the system. § All at the data element level System Data Flow
- 25. Underwri(en by: Presented by: Technology Knowhow & Staff Exper(se Significant unique experience with AI and pa(ern recogniKon experKse that sets the team apart. Applied ExperKse ExperKse Level Domain ExperKseY Ability to perform sophisKcated analysis of documents, data sets, and develop soluKons with tuned performance. In-depth experience with data extracKon deployments and track record of delivering high quality results on-Kme. Typical sooware vendors use informaKon systems and sooware development staff with general sooware skills to build soluKons What to Look For:
- 27. Underwri(en by: Presented by: Parascript Accelerator Program for BPOs Monitor Results Significantly reduce costs through automaKon of data entry-intensive processes using precision technology and a proven consultaKve approach that idenKfies baseline performance and concrete, acKonable areas for performance improvement. Here are the phases of the program: 1. Onsite discovery 2. Baseline performance analysis 3. Cost reducKon opportunity idenKficaKon 4. Business accelerator analysis report delivery 5. Comprehensive in-person briefing of the results