Data Driven Decisions
for Business
Context and Trends in Data Analytics
Learning outcomes for today
| Appreciate the role of data in organisations today and how data driven decisions can change behaviour |
Appreciate |
AssignmentTutorOnline
| Understand how data driven organisations deliver performance and competitive advantage |
Understand |
| Know how to apply data analytics frameworks to running successful data projects |
Know |
Roadmap for today
• Recap of last week and review of your Apply activity
• Why now? Drivers, trends and business benefits of data
analytics
• Managing data analytics projects – FRAMEWORKS
• Performance objectives and KPIs
• Activity 1 (Individual): How a DDDB approach transformed the
UK supermarket sector
• DDDB impacts on expectations and behaviour
• Activity 2 (Group): Applying DDDB principles in a university
context
• Your Consolidation homework for today – Task One of your
Assessment
• Key takeaways, Q&A and next steps
Recap – Data ‘analysis’ and data ‘analytics’
Source: Adapted from Big Data Fundamentals. Erl et al (2016)
Data analysis: Examining
data to find facts,
relationships, patterns,
insights, trends in support
of decision making
Data analytics: Development and
management of the complete data lifecycle
Recap: The Complexity-Value Model
Complexity
Value
| Descriptive Diagnostic Predictive Prescriptive Hindsight Insight foresight What happened? Why did it happen? What will happen? (autonomously) execute? Decision making autonomous con |
Response? How to
&
Source: Adapted from Big Data Fundamentals. Erl et al (2016)
Review of your Apply activity
If you have not yet posted to the Hub, do
so at the break
Why now? Technical drivers
Growth in IoT devices
Moore’s Law
Data, data and data
Source: Gartner, 2021
Source: Statista.com, 2021
BI platform sophistication
and competition
Why now?
– Big Data in a slide
Big Data
(lots and lots and lots of
structured, semistructured and
unstructured data)
AI
algorithms
(Clever – but 100%
understandable – maths)
+ =
Continuous
machine
learning
(Siri on Steroids)
Sustainable
competitive
advantage and
superior profitability
Managing data analytics projects – frameworks
A typical data analytics project lifecycle (comprising nine
stages)
Adapted from ‘Big Data Fundamentals’, Erl et al (2016)
Business case
development
Data aggregation &
representation
Data validation &
cleansing
Data extraction &
modelling
Data identification
and feasibility
analysis
Source data
acquisition & filtering
Data analysis Data visualisation Actionable results and execution
Stage 1 Stage 2 Stage 3
Stage 6 Stage 5 Stage 4
Stage 7 Stage 8 Stage 9
Feedback
loops at
every stage
Managing data analytics projects – frameworks
The PPDAC project framework
Adapted from ‘The Art of Statistics’, Spiegelhalter (2019)
Problem
Plan
Analysis Data
Conclusions
Understanding & defining the problem
Understanding stakeholders and their needs
& concerns
What to measure and how
KPIs?
Timescales, resources, costs
Defining the hypothesis to test
Sources, interfaces
Cleansing
Management
Data definitions
Integrate data
Statistical tests
Summary tables
Charting and visualisation
Interpretation
Communications with stakeholders
Recommendations
Next steps
Delivering superior performance
Source: Slack et al, 2019
Slack’s five generic performance
objectives
• Quality -> being right
• Speed -> being fast
• Dependability -> being on time
• Flexibility -> being able to change
• Cost -> being productive
But – these need to
be tailored for
different contexts,
and KPIs defined and
tracked
Key Performance Indicators (KPIs)
Linked to Overall
Strategic Goals and
Objectives
Central, Quantifiable Reference Point for gauging business performance and
benchmarking over time and across operations
Displays Actual
Measurement
against Threshold
Values
Measure
Operational
Efficiency and Risks
Demonstrate
Regulatory
Compliance
Whilst related, KPIs should not be confused with Critical Success Factors (CSFs)
which are factors required to support and drive KPI performance. These factors might
be skills, activities, actions, behaviours or attitudes that support KPI delivery.
Creating Value from Data
Business Intelligence
KPIs
Dashboards
Business intelligence (BI) is a data and technology-driven process for analysing data
and delivering actionable information that helps managers and other stakeholders make
business decisions.
As part of the BI process, organisations collect data from internal IT systems, feedback
data repositories and external sources, prepare it for analysis, run queries against the
data and create data visualisations, BI dashboards and reports to make the analytics
results available and understandable to business users for operational decision making,
tactical planning and strategic development.
The goal of BI initiatives is to drive better decisions that enable organisations to increase
revenue, improve operational efficiency and effectiveness, increase revenue and gain
competitive advantages over business rivals. Governments, NGOs and non-profit
organisations also use BI, for the core purposes of efficiency, effectiveness and
communications.
Dashboards
Source: https://www.thesmallman.com/dashboards
A classic data analytics case study
Source: Big Data Demystified, David Stephenson (2018)
Clubcard
launched
(Short case study text)
http://www.fmcgcentral.co.uk/resources/blog/2019/03/d
unnhumby-and-tesco-partnership/
Activity 1 (Individual):
How Tesco became a data driven
organisation?
Read the short case study in the Collaborate section of your hub for
this week on how Tesco used data to develop an industry gamechanging loyalty-based marketing capability:
1. What project, technical and other factors and approaches enabled
Tesco and its data analysis consultants Dunnhumby to develop a
successful data-driven Clubcard strategy?
2. How did Clubcard enable Tesco drive superior performance?
3. How was this superior performance measured (think KPIs)?
30 minutes
Wrap-up
Factors
• Senior-level buy-in, first-mover advantage, lowrisk pilots
• Cheap and standardised data communications,
processing storage
• Virtuous circle principle
• Sensitivity analysis testing
• Integration of Clubcard into an integrated
customer experience process
• Keeping it simple
Clubcard enabled Tesco to:
• Develop direct and ‘personal conversations’
with individual customers
• Tailor its offering more specifically to individual
store demographics
• Develop deep insights into customer buying
habits and preferences
• Build a virtuous circle of sales. The more a
customer spent, the more they were rewarded,
the more they visited shops, the more they
spent…
KPIs
• Response rates to promotions (40-60% uplift at times)
• Market share
• Customer feedback
• Competitor response
• Stock price performance
• Turnover, profitability and growth metrics.
DDDB impacts on expectations and behaviour
Sources: See for example: Always On, Cellan-Jones (2021) Hello World, Fry (2018)
A conceptual application of DDDB principles
– university operations
External data
sources
Tutors
Students
Student
support
Data users*
Module
Grades
Academic
support
Formative
assessments
Regulators
Attendance
Hub engagement
(forum engagement)
Hub engagement
(module materials)
CSF data
Hub engagement
(MCQ performance)
Degree grade
Session &
module feedback
Administrative
PSO metrics support
Central databases +
e.g. Microsoft Power BI
Enrolment data
KPI
*AKA stakeholders
Activity 2 (Group):
Think about the DDDB university application
outlined
Work in three groups, Red, Amber and Green
1. Red: What specific university operations could a DDDB approach
help improve? Think about Performance Objectives
2. Amber: What KPIs will a DDDB approach help improve?
3. Green: What ‘futures’ benefits might a big-data machine learning
type DDDB approach deliver. Think creatively and ‘outside the box’.
30 minutes
| Operations Process Outcomes Faster student query response More targeted learner intervention ‘Real-time’ formative assessment Faster learner intervention Consistent query response Data-rich learner interaction Potential Solutions |
KPI Benefits Higher grades Higher pass rates Faster student journeys – enrolment to graduation Operational efficiency Increased learning capacity Skills matched to the job market |
Automated FAQ
responses
Automated
Personalised
learning feedback
Tailored personal
learning
Futures
Your Consolidate Activity – Develop Task One of your Assessment
Also remember to complete this week’s MCQ
(this is monitored)
Your Consolidate Activity – Develop Task One of your Assessment
Also remember to complete this week’s MCQ
(this is monitored)
Takeaways and endsession discussion
Successful completion of this topic provides you with the
following valuable understanding and skills for a business
manager:
• How and why data analytics is developing in business
and how that drives performance
• The key stages, tasks and deliverables of a data
analytics project #frameworksrule
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