data sourcing best practices for reporting (webinar slides)
DESCRIPTION
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 Are you working with disparate islands of outdated data?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?TRANSCRIPT
10 Data Sourcing Best Practices Webinar – Thursday 27th of February
2014
Welcome
Why is data quality important?
Our 10 best practices
Demonstration – From data to visualisation
Q&A
Agenda for this webinar:
Introducing the speakers…
Adem Turgut Lead Business
Analyst SolveXia
Cameron Deed Senior Consultant
Yellowfin
Process Automation
Data Warehousing
Online Reporting & Analytics
“Productivity gains that are both dramatic but continuous and incremental”
Darren Robinson, Actuary at Clearview Insurance
“Simplified our business…”
Nick Sutherland, Co-founder of CT Connections Corporate Travel Management
“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
Data Quality Story
Overbooked by 10,000 tickets
Manual spreadsheet error
- telegraph.co.uk
Your data has reach…
* Panko and Port, 2012
Inter-departmental 69%
Within department
31%
CEO 42%
Where data from a report is used: Utilised by:
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
And we want more…
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
93% of execs believe they are losing revenue as a result of not fully leveraging the information they collect
* IDC, Oracle and EMC
1%
x44 by 2020
What is data quality?
HOW RELIABLE IS YOUR DATA?
TRUSTED AND
CREDIBLE
Complete
Accurate
Available Consiste
nt
Why is data quality important?
“It gives us accurate and timely information to manage our business”
“It supports accountability”
“It ensures the best use of our resources”
“It increases our efficiency”
“It reduces the cost of rework”
“It can increase customer satisfaction”
“It ensures we have the best possible understanding of our customers and employees”
“It improves the success rate of enterprise initiatives like Business Intelligence…”
Building high quality “supply chains” of data
MEASURE FOR QUALITY
GET THE RIGHT DATA
BE AGILE
Focus on the outcome
Analysis Paralysis
Letting data dictate what is “important”
Limited time and energy to focus
1IS
SU
ES
Focus on the outcome 1
Start with the outcome…
…then the data.
Focus on what matters R
EC
OM
ME
ND
ATIO
NS
Profile your data 2Data supplier doesn’t know your data needs
The data you source is as good as ….
ISS
UE
S
Profile your data 2Write your data profile Structure, Format, Frequency, Age, Delivery Method
Communicate it to data providers
Identify issues and gaps
RE
CO
MM
EN
DAT
ION
S
Get as close to the source as possible 3
When your source data is somebody else’s spreadsheet….
Human Error Risk
Unexpected Changes Additional effort and complexity
Availability of data
ISS
UE
S
Get as close to the source as possible 3
CAUTION
Be cautious of manual
spreadsheets
Skip the spreadsheet as a
source
PLAN Communicate and measure for quality
RE
CO
MM
EN
DAT
ION
S
Get as close to the source as possible 3
Insurance Intermediary Monthly CFO Report Data sourced from manual spreadsheet Time consuming and risky
EX
AM
PLE
Insurance Broker Monthly CFO Report
Streamline data sources 4
Using multiple sources Redundant data Increased complexity and quality risk
ISS
UE
S
Streamline data sources 4
Identify redundant data Focus on the essentials Cut out the stuff you don’t need
EX
AM
PLE
Set data quality expectations 5
Perfectionism Burnout
Focusing on things that few care about.. ISS
UE
S
Set data quality expectations 5
Focus on high impact data
Tolerances and ranges for quality and accuracy
RE
CO
MM
EN
DAT
ION
S
RELAX (a little)
Catch data quality issues early 6
Early
$1
$10
$100
If found in the middle of the journey
If found at the end of the journey Late
* Total Quality Management
If found at the start of journey
1-10-100 Rule:
ISS
UE
S
Catch data quality issues early 6
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
RE
CO
MM
EN
DAT
ION
S
Catch data quality issues early 6E
XA
MP
LE
Australian Life Insurer New Business Reporting
Actively measure quality 7IS
SU
ES
No simple way to identify if data is correct
Invalid Assumption: If the data meets our expectations today, it will going forward
What happens when we do find an issue?
Actively measure quality 7OK
GOOD
NOT GOOD
Define metrics for your data quality
Measure for quality on a consistent basis
Address consistent issues with strategic solutions (e.g. data cleansing)
RE
CO
MM
EN
DAT
ION
S
Actively measure quality 7E
XA
MP
LE
Margin Lending Group Client Credit Reports
Expect Change. Embrace It. 8
We all know change is coming
Business activity, changes in strategies and systems.
So rigid that you need to “reset”
ISS
UE
S
Expect Change. Embrace It. 8Li
kelih
ood
Impact L
L
H
H
Focus on high likelihood/impact changes
Score and rank potential changes
Have a plan in place for high risk items
RE
CO
MM
EN
DAT
ION
S
Plan for change 9
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
ISS
UE
S
Plan for change 9R
EC
OM
ME
ND
ATIO
NS
CAUTION In the event of a change the following people will…
Policies and rules Tracking Changes
Documentation
Plan for change 9E
XA
MP
LE
Big 4 Bank Actuarial Valuation
Controlled human interaction 10
Value of human interaction with data…
… at the cost of data quality
Uncontrolled manipulation of data
ISS
UE
S
Controlled human interaction 10
Avoid uncontrolled manipulation Facilitate controlled and discrete changes Make sure it is traceable
RE
CO
MM
EN
DAT
ION
S
Demonstration
Process Automation
Storage (Managed Tables)
Visualisation
Q & A
THANK YOU
Yellowfin LinkedIn User Group
@solvexia
SolveXia Pty Ltd
yellowfinbi.com
solvexia.com
@yellowfinbi
www