Date post: | 10-Feb-2017 |
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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