Data Sourcing Best Practices for Reporting (Webinar slides)

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

carolyn.eames@solvexia.com

@solvexia

SolveXia Pty Ltd

yellowfinbi.com

pr@yellowfin.bi

solvexia.com

@yellowfinbi

www