10 Data Sourcing Best Practices. Webinar Thursday 27 th of February 2014

Transcription

1 10 Data Sourcing Best Practices Webinar Thursday 27 th of February 2014

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 Simplified our business Nick Sutherland, Cofounder of CT Connections Corporate Travel Management Online Reporting & Analytics Process Automation Productivity gains that are both dramatic but continuous and incremental Darren Robinson, Actuary at Clearview Insurance Data Warehousing

4 David Menninger VP & Research Director Ventana Research

5 Data Quality Story Overbooked by 10,000 tickets Manual spreadsheet error - telegraph.co.uk

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 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 And we want more x44 by 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 data quality? Complete Accurate Available Consiste nt

10 Why is data quality important? It can increase customer satisfaction It ensures the best use of our resources It supports accountability It increases our efficiency It improves the success rate of enterprise initiatives like Business Intelligence It reduces the cost of rework 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

12 1 Focus on the outcome Analysis Paralysis ISSUES Letting data dictate what is important Limited time and energy to focus

13 1 Focus on the outcome RECOMMENDATIONS Start with the outcome then the data. Focus on what matters

14 2 Profile your data Data supplier doesn t know your data needs ISSUES The data you source is as good as.

15 2 Profile your data RECOMMENDATIONS Write your data profile Structure, Format, Frequency, Age, Delivery Method Communicate it to data providers Identify issues and gaps

16 3 Get as close to the source as possible ISSUES When your source data is somebody else s spreadsheet. Human Error Risk Availability of data Unexpected Changes Additional effort and complexity

17 3 Get as close to the source as possible RECOMMENDATIONS CAUTION Be cautious of manual spreadsheets Skip the spreadsheet as a source PLAN Communicate and measure for quality

18 3 Get as close to the source as possible EXAMPLE Insurance Intermediary Monthly CFO Report Data sourced from manual spreadsheet Time consuming and risky Insurance Broker Monthly CFO Report

19 4 Streamline data sources Using multiple sources ISSUES Redundant data Increased complexity and quality risk

20 4 Streamline data sources Identify redundant data EXAMPLE Focus on the essentials Cut out the stuff you don t need

21 5 Set data quality expectations ISSUES Perfectionism Burnout Focusing on things that few care about..

22 5 Set data quality expectations RECOMMENDATIONS RELAX (a little) Focus on high impact data Tolerances and ranges for quality and accuracy

23 6 Catch data quality issues early Rule: Early If found at the start of journey ISSUES Late $1 $10 $100 If found in the middle of the journey If found at the end of the journey * Total Quality Management

24 6 Catch data quality issues early RECOMMENDATIONS 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 6EXAMPLE Catch data quality issues early Australian Life Insurer New Business Reporting

26 7 Actively measure quality ISSUES 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 7 Actively measure quality RECOMMENDATIONS 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 7EXAMPLE Actively measure quality Margin Lending Group Client Credit Reports

29 8 Expect Change. Embrace It. We all know change is coming ISSUES Business activity, changes in strategies and systems. So rigid that you need to reset

30 8 Expect Change. Embrace It. RECOMMENDATIONS Likelihood H L L Impact H Score and rank potential changes Focus on high likelihood/impact changes Have a plan in place for high risk items

31 9 Plan for change A change occurs, then what? ISSUES Lack of clear policies and rules on who needs to do what Knowledge resting in the minds of key individuals

32 9RECOMMENDATIONS Plan for change CAUTION In the event of a change the following people will Policies and rules Documentation Tracking Changes

33 9EXAMPLE Plan for change Big 4 Bank Actuarial Valuation

34 1 0 Controlled human interaction Value of human interaction with data ISSUES at the cost of data quality Uncontrolled manipulation of data

35 1 0 Controlled human interaction RECOMMENDATIONS Avoid uncontrolled manipulation Facilitate controlled and discrete changes Make sure it is traceable

36 Demonstration

37 Visualisation Process Automation Storage (Managed Tables)

38 Q & A

39 THANK YOU SolveXia Pty Ltd www Yellowfin LinkedIn User Group