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Data Sourcing Best Practices for Reporting (Webinar slides)
The webinar discusses 10 best practices for data sourcing, emphasizing the importance of data quality for business efficiency and decision-making. It highlights that poor data quality costs U.S. companies $611 billion annually and offers strategies for improving data sourcing and quality management. Key recommendations include profiling data, catching quality issues early, actively measuring data quality, and planning for inevitable changes.
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Data Sourcing Best Practices for Reporting (Webinar slides)
- 1. 10 Data SourcingBest Practices 27 of February Webinar – Thursday th 2014
- 2. Welcome Introducing the speakers… Adem Turgut LeadBusiness Analyst SolveXia Cameron Deed Senior Consultant Yellowfin Agenda for this webinar: Why is data quality important? Our 10 best practices Demonstration – From data to visualisation Q&A
- 3. “Simplified our business…” NickSutherland, Cofounder of CT Connections Corporate Travel Management Online Reporting & Analytics “Productivity gains that are both dramatic but continuous and incremental” Darren Robinson, Actuary at Clearview Insurance Process Automation Data Warehousing
- 4. If you arelooking for a user-friendly tool with collaborative and mobile capabilities that I refer to as the next generation of BI software, take a look at Yellowfin David Menninger VP & Research Director Ventana Research
- 5. Data Quality Story Overbookedby 10,000 tickets Manual spreadsheet error - telegraph.co.uk
- 6. Your data hasreach… Where data from a report is used: Utilised by: Within department 31% Inter-departmental 69% CEO 42% * Panko and Port, 2012
- 7. Just how muchof an issue is data quality? 1 in 10 organisations rate their data quality as “excellent” Poor data quality accounts for 20% of business process costs $611bn The cost of poor data quality to US Companies each year * Gartner, TDWI
- 8. And we wantmore… x44 by 2020 2009 – enough data to fill a stack of DVDs to the moon and back 2020 – Grow by 44x Less than 1% of available data is analysed 1% 93% of execs believe they are losing revenue as a result of not fully leveraging the information they collect * IDC, Oracle and EMC
- 9. What is dataquality? HOW TRUSTED RELIABLE AND IS YOUR CREDIBLE DATA? Complete Accurate Available Consiste nt
- 10. Why is dataquality important? “It can increase customer satisfaction” “It improves the success rate of enterprise initiatives like Business Intelligence…” “It supports accountability” “It ensures the best use of our resources” “It reduces the cost of rework” “It increases our efficiency” “It ensures we have the best possible understanding of our customers and employees” “It gives us accurate and timely information to manage our business”
- 11. Building high quality“supply chains” of data GET THE RIGHT DATA MEASURE FOR QUALITY BE AGILE
- 12. 1 Focus onthe outcome ISSUES Analysis Paralysis Letting data dictate what is “important” Limited time and energy to focus
- 13. RECOMMENDATIONS 1 Focus onthe outcome …then the data. Start with the outcome… Focus on what matters
- 14. 2 Profile yourdata ISSUES Data supplier doesn’t know your data needs The data you source is as good as ….
- 15. RECOMMENDATIONS 2 Profile yourdata Write your data profile Structure, Format, Frequency, Age, Delivery Method Communicate it to data providers Identify issues and gaps
- 16. 3 Get asclose to the source as possible ISSUES When your source data is somebody else’s spreadsheet…. Availability of data Human Error Risk Unexpected Changes Additional effort and complexity
- 17. RECOMMENDATIONS 3 Get asclose to the source as possible PLAN CAUTION Be cautious of manual spreadsheets Skip the spreadsheet as a source Communicate and measure for quality
- 18. EXAMPLE 3 Get asclose to the source as possible Insurance Intermediary Insurance Broker Monthly CFO Report Data sourced from manual spreadsheet Time consuming and risky Monthly CFO Report
- 19. 4 Streamline datasources ISSUES Using multiple sources Redundant data Increased complexity and quality risk
- 20. 4 Streamline datasources EXAMPLE Identify redundant data Focus on the essentials Cut out the stuff you don’t need
- 21. ISSUES 5 Set dataquality expectations Perfectionism Burnout Focusing on things that few care about..
- 22. RECOMMENDATIONS 5 Set dataquality expectations Focus on high impact data RELAX (a little) Tolerances and ranges for quality and accuracy
- 23. 6 Catch dataquality issues early 1-10-100 Rule: If found at the start of journey Early ISSUES $1 If found in the middle of the journey $10 Late If found at the end of the journey $100 * Total Quality Management
- 24. RECOMMENDATIONS 6 Catch dataquality issues early Implement quality measures near the start of the data supply chain Use the “start” as a reference point when checking data further down the journey
- 25. EXAMPLE 6 Catch dataquality issues early Australian Life Insurer New Business Reporting
- 26. ISSUES 7 Actively measurequality Invalid Assumption: If the data meets our expectations today, it will going forward No simple way to identify if data is correct What happens when we do find an issue?
- 27. RECOMMENDATIONS 7 Actively measurequality NOT GOOD OK GOOD Define metrics for your data quality Measure for quality on a consistent basis Address consistent issues with strategic solutions (e.g. data cleansing)
- 28. EXAMPLE 7 Actively measurequality Margin Lending Group Client Credit Reports
- 29. 8 Expect Change.Embrace It. ISSUES We all know change is coming Business activity, changes in strategies and systems. So rigid that you need to “reset”
- 30. Score and rankpotential changes H Likelihood RECOMMENDATIONS 8 Expect Change. Embrace It. Focus on high likelihood/impact changes L L H Impact Have a plan in place for high risk items
- 31. 9 Plan forchange ISSUES A change occurs, then what? Lack of clear policies and rules on who needs to do what… Knowledge resting in the minds of key individuals
- 32. RECOMMENDATIONS 9 Plan forchange CAUTION In the event of a change the following people will… Policies and rules Documentation Tracking Changes
- 33. EXAMPLE 9 Plan forchange Big 4 Bank Actuarial Valuation
- 34. 1 Controlled human interaction 0 ISSUES Valueof human interaction with data… … at the cost of data quality Uncontrolled manipulation of data
- 35. RECOMMENDATIONS 1 Controlled human interaction 0 Avoiduncontrolled manipulation Facilitate controlled and discrete changes Make sure it is traceable
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- 39. THANK YOU solvexia.com carolyn.eames@solvexia.com @solvexia SolveXia PtyLtd www yellowfinbi.com pr@yellowfin.bi @yellowfinbi Yellowfin LinkedIn User Group