Sourcing Good Data10 best practices

Welcome

Why is data quality

important?Our 10 best practices

Agenda:

Data Quality Story

Overbooked 10,000 tickets for event

Manual spreadsheet error

- telegraph.co.uk

Your data has reach…

* Panko and Port, 2012

Inter-departmental

69%

Within

department

31%

42%

Where data from a report is used: % of data in spreadsheets that influences CEO

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 RELIABLEIS YOUR DATA?

TRUSTEDAND

CREDIBLE

Complete

Accurate

Available

Consistent

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

MEASUREFOR QUALITY

GET THERIGHT 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 outcome1

Start with

the

outcome…

…then the

data.

Focus on

what matters

REC

OM

MEN

DA

TIO

NS

Profile your data2

Data supplier doesn’t know

your data needs

The data you source is as

good as the information

you provide to the

supplier…

ISSU

ES

Profile your data2

Write your data profileStructure, Format, Frequency, Age, Delivery Method

Communicate it to data providers

Opportunity to identify issues and gaps

REC

OM

MEN

DA

TIO

NS

Get as close to the source as possible3

When your source data is somebody else’s

spreadsheet….

Human Error Risk

Unexpected Changes

Additional effort and complexity

Availability of data

ISSU

ES

Get as close to the source as possible3

CAUTION

Be cautious of

manual

spreadsheets

Skip the

spreadsheet as a

source

PLAN

Communicate and

measure for quality

REC

OM

MEN

DA

TIO

NS

Streamline data sources4

Using multiple sources

Redundant data

Increased complexity and quality risk

ISSU

ES

Streamline data sources4

Identify redundant data

Focus on the essentials

Cut out the stuff you don’t need

REC

OM

MEN

DA

TIO

NS

Set data quality expectations5

Perfectionism Burnout

You can’t expect to focus on everythingISSU

ES

Set data quality expectations5

Focus on high impact data

Employ tolerances and ranges for quality and accuracy

REC

OM

MEN

DA

TIO

NS

RELAX(a little)

Catch data quality issues early6

Early

$1

$10

$100

If found in the

middle of the

journey

If found at the end

of the journeyLate

* Total Quality Management

If found at the

start of journey

1-10-100 Rule:

ISSU

ES

Catch data quality issues early6

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

REC

OM

MEN

DA

TIO

NS

Actively measure quality7IS

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 quality7

OK

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)

REC

OM

MEN

DA

TIO

NS

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”

ISSU

ES

Expect Change. Embrace It.8Li

kelih

oo

d

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

REC

OM

MEN

DA

TIO

NS

Plan for change9

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

ISSU

ES

Plan for change9R

EC

OM

MEN

DA

TIO

NS

CAUTION

In the event

of a change

the following

people will…

Policies and rules Tracking ChangesDocumentation

Controlled human interaction10

Value of human interaction with data…

… at the cost of data quality

Uncontrolled manipulation of data

ISSU

ES

Controlled human interaction10

Avoid uncontrolled manipulation

Facilitate controlled and discrete changes

Make sure it is traceable

REC

OM

MEN

DA

TIO

NS

Recap

1 Focus on the outcome

2 Profile your data

3 Get close to the source

4 Streamline data sources

5 Set data quality expectations

Recap

6 Catch data quality issues early

7 Measure quality

8 Expect and embrace change

9 Plan for change

10 Controlled human interaction

Thank You