Data Sourcing Best Practices for Reporting (Webinar slides)

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Why watch?

Are you trapped in reporting hell?

Do you spend hours struggling to manually produce the reports management demands? Are you working with disparate islands of outdated data? And, after all that hard work, are the reports produced inaccurate and untrustworthy?

Watch this on-demand Webinar from SolveXia and Yellowfin – Data Sourcing Best Practices for Reporting – to discover how to build reliable supply chains of data in just 30-minutes. Learn how to quickly and easily go from source data to killer report – every time.

Only dependable and repeatable processes can produce quality data and reports. Ensure your reporting generates the business insights you need. Let SolveXia and Yellowfin show you how.


What will you learn?

Think the ability to deliver world-class, up-to-date and accurate reports that anyone can access, analyze and act on is important? Then this Webinar is a must.

Watch the on-demand version to learn how to:
•Create business critical reports on which you and your organization can rely
•Deliver sleek, sexy and intuitive charts, reports and dashboards to anyone, anywhere, anytime on any device
•Become the information Superhero you were meant to be!

The data that underpins any reporting system must be managed properly to make sure it’s clean, relevant and delivered in a timely manner to maximize the ability of enterprise BI solutions to produce actionable insights. Do you know how?

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Data Sourcing Best Practices for Reporting (Webinar slides)

  1. 1. 10 Data Sourcing Best Practices 27 of February Webinar – Thursday th 2014
  2. 2. Welcome Introducing the speakers… Adem Turgut Lead Business 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. 3. “Simplified our business…” Nick Sutherland, 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. 4. If you are looking 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. 5. Data Quality Story Overbooked by 10,000 tickets Manual spreadsheet error - telegraph.co.uk
  6. 6. Your data has reach… Where data from a report is used: Utilised by: Within department 31% Inter-departmental 69% CEO 42% * Panko and Port, 2012
  7. 7. Just how much of 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. 8. And we want more… 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. 9. What is data quality? HOW TRUSTED RELIABLE AND IS YOUR CREDIBLE DATA? Complete Accurate Available Consiste nt
  10. 10. Why is data quality 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. 11. Building high quality “supply chains” of data GET THE RIGHT DATA MEASURE FOR QUALITY BE AGILE
  12. 12. 1 Focus on the outcome ISSUES Analysis Paralysis Letting data dictate what is “important” Limited time and energy to focus
  13. 13. RECOMMENDATIONS 1 Focus on the outcome …then the data. Start with the outcome… Focus on what matters
  14. 14. 2 Profile your data ISSUES Data supplier doesn’t know your data needs The data you source is as good as ….
  15. 15. RECOMMENDATIONS 2 Profile your data Write your data profile Structure, Format, Frequency, Age, Delivery Method Communicate it to data providers Identify issues and gaps
  16. 16. 3 Get as close 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. 17. RECOMMENDATIONS 3 Get as close to the source as possible PLAN CAUTION Be cautious of manual spreadsheets Skip the spreadsheet as a source Communicate and measure for quality
  18. 18. EXAMPLE 3 Get as close 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. 19. 4 Streamline data sources ISSUES Using multiple sources Redundant data Increased complexity and quality risk
  20. 20. 4 Streamline data sources EXAMPLE Identify redundant data Focus on the essentials Cut out the stuff you don’t need
  21. 21. ISSUES 5 Set data quality expectations Perfectionism  Burnout Focusing on things that few care about..
  22. 22. RECOMMENDATIONS 5 Set data quality expectations Focus on high impact data RELAX (a little) Tolerances and ranges for quality and accuracy
  23. 23. 6 Catch data quality 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. 24. RECOMMENDATIONS 6 Catch data quality 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. 25. EXAMPLE 6 Catch data quality issues early Australian Life Insurer New Business Reporting
  26. 26. ISSUES 7 Actively measure quality 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. 27. RECOMMENDATIONS 7 Actively measure quality 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. 28. EXAMPLE 7 Actively measure quality Margin Lending Group Client Credit Reports
  29. 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. 30. Score and rank potential 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. 31. 9 Plan for change 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. 32. RECOMMENDATIONS 9 Plan for change CAUTION In the event of a change the following people will… Policies and rules Documentation Tracking Changes
  33. 33. EXAMPLE 9 Plan for change Big 4 Bank Actuarial Valuation
  34. 34. 1 Controlled human interaction 0 ISSUES Value of human interaction with data… … at the cost of data quality Uncontrolled manipulation of data
  35. 35. RECOMMENDATIONS 1 Controlled human interaction 0 Avoid uncontrolled manipulation Facilitate controlled and discrete changes Make sure it is traceable
  36. 36. Demonstration
  37. 37. Visualisation Process Automation Storage (Managed Tables)
  38. 38. Q&A
  39. 39. THANK YOU solvexia.com carolyn.eames@solvexia.com @solvexia SolveXia Pty Ltd www yellowfinbi.com pr@yellowfin.bi @yellowfinbi Yellowfin LinkedIn User Group

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