DATA DRIVEN-BUSINESS
ROAD MAP
VISION AND MISSION
Our vision:
Improving our business value chain through the use of data analytics.
Our Mission:
Build an agile organization able to unlock business value through
 efficient use of organizational assets
 predicting and leveraging market opportunities
 Improved customer satisfaction and retention
 continual improvement
“If you do not know how to ask the right question, you discover nothing”. (W. Edwards Deming)
WHAT TO EXPECT
People
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Process
Data Governance
Frame works
Data-driven Culture
Human capacity
Data
Reports
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Technology
Visibility
Consistency
Authority
Quality
Integrity
Analysis
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Action
Data storage
Data security
Data quality
Data management
Value
To obtain value, our data has to produce reports with information that can provide insights
required to drive or create value.
BENEFITS
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Increase business intelligence
Proactive insight of business needs
Gain competitive advantage
Gain Consumer insight
Improved operation efficiency
Eliminate silos
Reduce supply chain risk
Organization that effectively utilize data, out perform others by approximately 20%. (Drazen
et al, 2014)
Data is in the heart of all human living , an organization not utilizing data places itself at a
disadvantage of not tapping into a soil of information which can be translated to wisdom.
STRATEGIC ROAD MAP
Define data
management
processes/align
metrics
Focus effort and
strategy on data
Analytics
Deploy
technology
Point B
Point A
Senior
management
support
Create common
leadership for data
analytics
Acquire
capacity
Acquire
technology
Company wide
training on
tools/Processes
Make data
accessible to all
CHALLENGES
Current Challenge
To Do
Decentralized data
Integrating company systems ensuring data is available to all who require it
Lack of data culture
Senior leadership championed culture shift
Unknown and ungoverned data
Establish data governance processes, identify what to measure and relate
metrics
Lack of Human capability with data
analytics
Centralized data analytics team, also organization wide data intelligence
development
Operational silos
Build trust and encourage collaboration
Incompatible data tool
Identify and deploy the right tool to improve efficiency
“Do you have data to back that up?” should be a question no one should be afraid to ask and everyone should expect
and be ready to answer. (Arsenault, 2014)
Without senior leadership commitment, a culture change will be impossible to drive
MATURITY
Knowledge
Prescriptive
[Transformation]
Predictive
[Insight-Driven)
Descriptive
[Cost Reduction]
Data
Value
DATA MANAGEMENT
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Data Warehousing
Data Integrity
Data quality
Data governance
Data integration
Systems and Tools
Success will depend on good consistent
data management across the value
chain and data life cycle
Good data
Management
Effective data management will
guarantee accurate analysis and
information that will guide our
decision
Recent study by Gartner indicate that poor data quality is the major cause for about 40% failure in business
initiatives (Narayan, 2019)
DATA QUALITY ASPECTS
Completeness
Timeliness
Consistency
Our data must at all time
conform to defined metrics
and standards
Data
Quality
Accuracy
Integrity
Conformity
Garbage in Garbage out
Available data must be
trusted to be complete and
protected and addressing
business requirements.
No Quality data = No Quality decisions
DATA VALIDATION
Data validation will involve checks designed to guarantee that data use is rational,
accurate and acceptable.
Source Verification
Data-issue tracking
 Test for completeness
(Loop back verification)
 Workflow management
 Error flags
 Statistical checks
(Reduce risk of wrong
capture of data)
 Process control
Data Stewards
 Roles/Responsibility to
manage data.
 Tracking & monitoring
for errors
DATA GOVERNANCE FRAMEWORK
Data Driven Decisions and Measurable Outcomes
Data governance drives the availability of high quality, secured data to enable a data driven decision making
Data
Ownership
Data
Stewardship
Roles /
access level
Reliable
information
flow
Knowledge &
information
sharing
Process
/Policies/
Standards
Data
validation /
quality
framework
Policies /
security /
audits
Data
integration /
data
architecture
Reporting
and
Analytics
People, Processes and Technology
UBC,2020
VALUE OF DATA GOVERNANCE
Value 1
How
Completeness and
comprehensiveness of
data
Value 2
Legitimacy
and Validity
of data
• Define process to
manage data as an
organizational asset.
• Quality management
• Build collaboration
• Drive data culture
How
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Business value
Audit and audit trails
Plans for business continuity
Govern Access control
Manage Change Control
Define data Standards
Define Responsibilities and ownership
Visibility
Value 3
Accuracy and
Precision
How
• Data validation rules
• Define Metrics and error flags
• Select appropriate systems and
service providers
• Govern data Security
• Standardize report templates
LEVERAGING BIG DATA
Big data analytics involves examining large structured and unstructured data from
inhouse and public sources in order to uncover hidden patterns and create insight.
Big data Sources:
Social media, customer data, Financial data, Operational data
How can exploiting big data help marketing and sales:
 Increase customer acquisition and retention
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Reduce advertising cost while providing marketing insights
Pricing optimization
Targeted marketing and brand awareness
Drives innovation and product development
Supply chain management
DATA ANALYTICS
Data analytics involves extracting information from data
Descriptive
Explains
what happed
Diagnostic
Explains why
it happed
Predictive
Forecast
what might
happed
Prescriptive
Recommends
solution for what
might happen
As an organization grows in data maturity, its data analytics capability increases.
Mehta,, A. 2017
Note: progression is not a sequence of use but a representation of level of complexity required to run such analytics.
STAKEHOLDER
 Departmental managers
Internal Stakeholders
 IT Support
 All Employees
 Senior Management
 Business Owners
Project
 Creditors
 Shareholders
 Suppliers
 Customers
 Investors
 Trade unions
 Government
 Media/Society
External Stakeholders
Stakeholders are various interest groups drawn from the different influence and impact arears of the project
TOOLS FOR STAKEHOLDERS
IDENTIFICATION AND ANALYSIS
 Meetings – Progressive engagement with key stakeholders
 Surveys – Through targeted interviews, obtain perspective
Stakeholders Identification
 Data Gathering – Review past five years financial data for players
 Expert Judgment – Engage data consultants for insight
 Documentation reviews – Review past five year records for business players
 Brainstorming – With key stakeholders more insight will be obtained
 Power/Interest grid – Need to determine and prioritize interest and influence
Stakeholders Analysis
 Data Gathering – Through benchmarking with best practice
 Root Cause Analysis – Understanding assumptions about stakeholders
 Data display - Stakeholders engagement assessment matrix
PMBOK
The road to success will be an early inclusion and by-in of all stakeholders across the data life cycle
DATA SECURITY AGENDA
Our 10 key elements for a Data-centric security strategy
3 Data Classification
6 Encryption Strategies
1 Data Collection
4 Data Tagging
2 Data Analytics
5
9 Cloud Access
7 Gateway Control
Data Loss Prevention
10
Continuous Education
8 Access Management
The Overall focus will be on data integrity which will ensure that
our data is collected, handled, stored and retrieved with no
unauthorized alteration
DATA PRESENTATION
Guiding principles
Simplified message with no falsification of data
Simplified diagrams through graphs, tables or charts
Standardized reporting templates
Clear definition of target audience
All graphs and charts must contain a legend
Data Visualization techniques
Dashboards and scorecards
Scenario development
Applied business analytics
Statistical techniques
Case scenario
DATA PRESENTATION
Our Story
Reports shall be concise containing the below elements to support and guide the business.
Measuring strategic metrics
Provide insight to current position
Present Clear performance benchmarks
Help in Learning and reproduction of success
Build and harness momentum
Improve team collaboration
Motivate and encourage engagement
Encourage growth through action
(Kuilen & Jacques, 2015)
KEY SUCCESS MEASURES
Business view
Revenue Growth
Greater Customer Loyalty
Greater Efficiency
Reduced operational risk
Greater target of resources and reduced losses
Organizational View
Forward looking with advance methods to identifying problems
Easy access to data
Centralized data governance
Standardized tools and analytic platform
Data-centric processes
Data-centric resources
Business alignment across units
(LaValle et al, 2011)
References
Anderson,C. (2015). Being data-driven: It’s all about the culture. Retrieved from
https://www.oreilly.com/radar/being-data-driven-its-all-about-the-culture/
Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3),
1025-1044.
Davenport, T.,H. (2014). How strategists use big data to support internal business decisions, discovery and
production. Strategy & Leadership, 42(4), 45-50. doi:10.1108/SL-05-2014-0034
Drazen,N., Moore,C., Naftalsi,F. (2014). Ready to takeoff?. Retrieved from
https://www.ey.com/Publication/vwLUAssets/EY-ready-for-takeoff/$FILE/EY-ready-for-takeoff.pdf
Dykes, B,(2012). Analytics: 5 Key steps to generate value. Retrieved from
http://www.analyticshero.com/2012/10/03/analytics-5-key-steps-to-generate-value/
References
Hopkins, M. S., LaValle, S., Lesser, E., Shockley, R., & Kruschwitz, N. (2011). Big data, analytics and the
path from insights to value Retrieved
from https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ofs&
AN=511033103&site=ehost-live&scope=site
Kuilen, B & Jacques, R (2015). Hype on big data. Retrived from: https://management.co.nz
Lelea, M.A., Roba, G.M., Christinck, A., & Kaufmann, B. (2016). Title: All relevant stakeholders: a literature review of
stakeholder analysis to support inclusivity of innovation processes in farming and food systems. Retrieved from:
https://pdfs.semanticscholar.org/64f1/7d30052dc1f6bb55741eb1121445ddfbaf4a.pdf?_ga=2.50525409.957395672.
1592575282-741548018.1592575282
van de Kuilen, B., & Jacques, R. (2015). The big data hype: How to effectively use your business
intelligence Adrenalin Publishing Ltd. Retrieved
from https://lopes.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edsg
go&AN=edsgcl.437058769&site=eds-live&scope=site
THANK YOU