Quality From The Eye Of Business Intelligence

“Quality at the heart of BI”

By: Kamel Badawy

Agenda

Introduction What Does Quality Means to BI? BI Data Quality Dimensions Current Standing & Where to? BI Roadmap Towards Data Quality

Introduction

Organizations are discovering that data quality deficiencies have a significant impact on their most strategic business initiatives, often holding them back from achieving:

Growth Agility Competitiveness Transparency

In addition to challenges with growth and agility, compliance and transparency pressures increasingly bring DATA QUALITY issues to the fore — it is no longer acceptable to ignore flaws in data, and organizations must prove the accuracy of information that they report internally to top management or to auditors, regulators and the public

BI Objective:Is to Build clean, accurate & reliable data warehouse

BI should deliver data that is necessary for decision-makers

What Does Quality Means to BI?

Quality is critical to Data Warehouse andBusiness Intelligence. Better informed, more

reliable decisions come from using the right data quality technology during the process of loading a

data warehouse. It is important the data is accurate, complete, and consistent across data

sources.

Data Quality is a multi-dimensional measurement of the adequacy of a particular datum or data sets. In business, data quality is measured to

determine whether or not data can be used as a basis for reliable Business Intelligence and for

making organizational decisions.

REDUCTIONCut BI project

failure rates in half

COSTLower overall cost

of BI/Data Warehouse

solutions

VISIBILITYImplementing a

culture of measurement provides clear visibility for all

parties

Why BI Data Quality?

Numbers & Facts

Of organizations believe they’re

negatively affected by inaccurate data

Of businesses admit their data is not

accurate

35.00%

30.00%

30.00%

29.00%

23.00%

20.00%

11.00%

3.00%

0% 5% 10% 15% 20% 25% 30% 35%

LOW QUALITY, ACCURACY OF DATA

LIMITED DIRECT BENEFIT TO MY ROLE

DIFFICULTIES IN ASSESSING WHICH DATA IS TRULY USEFUL

LACK OF NECESSARY SKILLS

PROBLEMS TO COMMUNICATE DATA

LACK SUFFICIENT EXPERTISE

ABILITY TO TAKE ACTIONS BASED ON DATA

PRESENTATION OF DATA IS IN AN UNUSABLE FORMAT

Barriers to integrating more data in decision making

25% of Critical Data in the World’s Top

Companies is Flawed

How confident organization are in

their data

BI Data Quality Dimensions

Data Quality Dimensions

Description

ValidityData accurately represents reality or a verifiable source

CompletenessRecords are not missing fields and datasets are not missing instances

IntegrityThe appropriate links and relationships exist among data

ConsistencyData that exists in multiple locations is similarly represented and/or structured

Uniqueness Data that exists in multiple places has the same value

TimelinessData is updated with sufficient frequency to meet business requirements

AccessibilityData is easily retrieved and/or integrated into business processes

Data Quality Dimensions

Description

ExistenceData reflective of meaningful events, objects and ideas to the business has been collected

UsabilityStakeholders understand and are able to leverage this data

ClarityData has a unique meaning and can be easily comprehended

Believability Data is deemed credible by those using it

ObjectivityData is unbiased and impartial and not dependent on the judgment, interpretation or evaluation of individuals

RelevancyThe data is applicable to one or more business process or decision

Current Standing & Where to?

Most organizations are having quality gap between their core business enterprise data and the databases

that contains information

Such quality gap in data must be identified to be able to create the correct alignment

Once the quality gap is identified and closed; the flow of data and information shall create the required synergy

and accuracy for operations

Accurate data shall be classified into data marts to be able to enter to the data reservoir for processing

Now data shall be processed into dashboards and reports that helps in better decision making

What’s new?!Big Data Vs. Business Intelligence

Creating Questions Vs. Answering Questions

BI Roadmap Towards Data Quality

Focus on the Right Things

Establish a clear line of sight between the KPI/KRI impact of data and data quality improvement

Use data profiling early and often

Design and implement data quality dashboards for critical information such as master data.

Fit for Purpose

Clearly define what is meant by "good enough" data quality

Establish a data & report standards across the organization

Move from a truth-based semantic model to a trust-based semantic model.

Thank You