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BI
BUSINESS INTELLIGENCE
INFS 2036
Business Intelligence
We e k 9
D ATA I N T E G R AT I O N +
C E N T R A L D ATA R E P O S I TO R I E S +
S T R AT E G I C VA L U E O F
I N F O R M AT I O N I N T H E F U T U R E
Course Outline
INFS 2036 2
The Why
of BI
The How of BI
Emerging
BI Concepts
Future Analytics
Week 1 Why data is important to business + The BI Process Week 2 Front-End: Data Visualisation Week 3 Back-End: Organisation Information Systems Week 4 From Data to Intelligence Week 5 Lifecycle model and project management Week 6 Analytics - predicting the future & performance management Week 7 Privacy, Ethics, Legal Issues, Trust Week 8 Mining Technologies Week 9 Data Integration at an Enterprise and Cross- Organisation Level + Strategic value of information in the future Week 10 You’re the CEO (Exam Review)
Week 9 Reading/Viewing Textbook
- Chapter 3: 3.1 Opening Vignette to 3.3 Data warehousing process (pages 154-165)
- Chapter 3: 3.5 Data Integration and the Extraction, Transformation and Load process (pages 171-176)
- Chapter 8: 8.1 Opening Vignette to 8.2. Internet of Things (pages 444-455)
- Chapter 8: 8.3. Cloud Computing and Business Analytics (pages 455-466) INFS 2036 3 Additional reading/viewing – see Workshop Notes page (course site)
- OECD Brief: Automation and Independent Work in a Digital Economy
- HBR: Artificial Intelligence for the Real World (up to “1. Understanding The Technologies”)
- McKinsey: Notes from the AI frontier: Applications and value of deep learning
- Focus On … Challenges of Turbulent
Business Environments
- From Week 1 to the Future
- Exam Review! Key Points + Workshop Activities
Data integration via ETL processes + DW,
administration and security issues, including:
- Extract Transform Load processes
- Data Warehouse modelling + implementation
- The impact of Technology and the Internet
- Internet of Things, Industry 4.0, Self-Service BI
- Artificial Intelligence + workplace automation of
the global business environment
Topic Presentation + Practice
W h a t w e ’ r e d o i n g t h i s w e e k
INFS 2036 4 Agenda – Week 9 THEORY PRACTICE This Week’s Case Study
Retailers using technology +
the Internet for industry
disruption
INFS 2036 7
We’ve seen visualisation, data
mining + predictive analytics.
Today we’ll focus on ETL,
Integration + Reporting
https://paristech.com/blog/investing-in-information/
Where is BI Going in the Future? INFS 2036 8 In-store refrigerators will bring real-time analytics backed by insights generated by ovens.
The Future is Now INFS 2036 9
What is needed for this to work?
How can data be processed, stored
and integrated to support this
functionality?
What is the potential for industry
disruption?
ETL + Data Warehouses 10 Extract, transform + load data onto the data warehouse Provides a centralised data management + retrieval system
Business Intelligence combines structured and
unstructured data from internal and external data
sources to provide insight that could not be gained
from one data source.
How does it achieve this?
ETL INFS 2036 13
T H E P O W E R B E H I N D T H E T H R O N E
ETL helps create Business Intelligence by:
1. Delivering a single point-of-view
Managing and combining multiple data sets into a single, unified view making it easier to analyse, visualise + make sense of large data sets.
2. Providing historical context
ETL allows an enterprise to combine legacy data with data collected from new platforms and applications to produce a long-term view of data, so that older data sets can be viewed alongside more recent information.
3. Improving efficiency and productivity
ETL tools automate the process of hand-coded data migration freeing up time so developers can spend more time on innovation and less time managing data.
4. Supports decision making
The resulting data enables new insights supporting operational, tactical and strategic decision-making.
Data Warehouses (DW) INFS 2036 14
T H E P O W E R B E H I N D T H E T H R O N E
- Supporting regular updates and occasional updates of data
- Storing and promoting:
- Master data (e.g. students, locations)
- Data standards (e.g. countries, industry classifications)
- Metadata
- Data quality.
- Supporting all levels of management decision making
(Strategic, Tactical, Operational) including metrics and PIs.
For these reasons a Data Warehouse is employed to do the analytic work.
A Data Warehouse helps to support Business Intelligence by:
Data Warehouse Storage
Data Warehouses (DW) INFS 2036 15
T H E P O W E R B E H I N D T H E T H R O N E
- An important tool in the big data era.
- Confidentiality and security considerations increase due to joining data.
- Needs to follow policy and practices of the organisation (e.g. UniSA policy
A-46 “Confidentiality of students' personal information”) – consent.
- Enable summary data + detailed data to be available separately for
different users (e.g. individual teacher evaluations vs. overall course
evaluation).
- Expensive to scale thus cloud storage becomes more attractive – allows
for scaling up, speed from servers across geographic locations, reliability,
security and cost.
ETL + DW Enables … INFS 2036 16 Dashboards
Data from different
systems brought together
for summary +
detailed analysis
Scorecards Impact Diagrams
Data Warehouses INFS 2036 19
F U T U R E C H A L L E N G E S + D I R E C T I O N S
- Data Warehouse technology consistent over the last several decades.
- The advent of Big Data has exposed technological and architectural gaps that some legacy warehouses
aren’t equipped to handle due to:
- Data Variety
- Latency (due to large volumes of data)
- Data silos (due to differing data sources and/or collection systems)
- Data Science compatibility
- Most legacy data warehouses slow down the ability to acquire and ingest data. Makes it difficult to keep pace with transforming + extracting insights
- Alternatives include data lakes, data marts and cloud implementations.
- Modernising Data Warehouses opens up opportunities to take advantage of evolving technology as well as Internet technologies – Internet of Things (IoT).
Internet of Things (IoT) INFS 2036 20
I m p a c t o f Te c h n o l o g y + t h e I n t e r n e t
Internet of Things (IoT) Billions of physical devices around the world connected to the Internet, collecting + sharing data.
- Does not include computers or any other device expected to have an Internet connection (e.g. a smartwatch is an IoT but a smartphone is not).
- Any physical object can be transformed into an IoT device. e.g. lightbulb, tractor, store shelves, plane engine, cities …
- Three components needed to transform a device to an IoT device: 1. Physical: product’s mechanical and electrical parts. 2. Smart: sensors, data storage, embedded operating system to replace mechanical or electrical parts. 3. Connectivity: ports, antennae and protocols enabling wired or wireless connectivity.
- The success of IoT depends on data and what is done with it.
CONSUMER INTERNET of THINGS INFS 2036 21
C I o T
Industrial Internet of Things (IIoT) INFS 2036 22
I n d u s t r y 4. 0
- IoT allows data to be exchanged between the product and its operating environment,
its maker, its users and other products + systems.
- Supports idea enterprises should have access to more data about their own products +
their own internal systems + a greater ability to make changes as a result.
- Industry 4.0 – use of data, automation, machine
learning and data exchange in manufacturing.
- Technologies create a “smart factory” where machines,
systems, and humans communicate with each other
to coordinate and monitor progress.
INFS 2036 25
M A C H I N E L E A R N I N G ( S O M E T I M E S E D I B L E )
AI Over 13 million pizzas scanned since 2017. Customers approve the pizza ahead of delivery. DOM Pizza Checker boosted product quality scores by more than 15%. The DOM Pizza Checker will next be used to surveil workplaces for under-performing stores … The hairdresser call went smoothly! The restaurant call also ran smoothly from the AI side. Google used Deep Learning to ensure Google Assistant is a useful functioning tool engineering manager enjoying a^ Google Duplex lead and the^ without^ human intervention. meal booked using Google Duplex. Semi-supervised Unsupervised
INFS 2036 26
D E E P L E A R N I N G
AI
Deep Learning (DL)
another subset of AI — machines use algorithms to learn from structured data using algorithms which can modify itself without human intervention.
- DL is similar to, but more sophisticated than machine learning.
- Multiple layers of algorithms each providing a different interpretation to the data it processes - this is an artificial neural network.
- Advanced Statistics based on the functionality of brain neural networks.
- Well-suited for analytical tasks e.g. Google translate, image recognition. Increased uptake across organisations: “Being able to use a deep learning algorithm to understand machine code … so that we can fix something before it fails, is really cool, and that's what we're doing at the moment.” Data science (Networks) team manager, Telstra
INFS 2036 27
H E A LT H S U R V E I L L A N C E T E C H N O L O G Y ( H S T )
AI Ethics? Privacy? Data Security?
E M O T I O N A L S U R V E I L L A N C E T E C H N O L O G Y ( E S T )
S E N T I M E N T S U R V E I L L A N C E T E C H N O L O G Y ( S S T )
One company using the brain-monitoring tech says profits have increased by $ million since 2014.
28 Focus on Week 9
T U R B U L E N T B U S I N E S S E N V I R O N M E N T S
Digital Transformation + Industry Disruption
- Digital transformation: the greater and purposeful use of technology and data to support strategy
- Strategy: why, what and how of an organisation
- Industry disruption:
- Need a longer and broader view of the changes affecting my industry
- Need to be anticipative, adaptive
- Consider: how often do you make a decision using data now vs. not using data previously?
- Previously data was in hands of experts/intermediaries INFS 2036
- reviews of products
- how trustworthy that company is
- which predictive word to use in a text
- which way to drive or car to buy
INFS 2036 31 TURBULENT BUSINESS ENVIRONMENTS
W o r k p l a c e A u t o m a t i o n
Big data/Really big data (IoT) + Algorithms + Machine Learning
Applications include:
- Driverless cars, Assembly line/Manufacturing, Retail/Checkout, Data
Entry, Loan Officers, Fast Food cooks, receptionists, para-legal,
bartenders, security guards, reporters, financial advisors …
- 30% of activities are automatable for 60% of occupations (McKinsey).
- 60% of U.S. companies expecting to use AI or advanced automation by 2022.
32 TURBULENT BUSINESS ENVIRONMENTS
E X T E R N A L B U S I N E S S E N V I R O N M E N T
External environments are:
- Turbulent
- Complex
- Global
- Uncertain
- Ambiguous
- Incomplete
- Need external environmental analysis to better
understand and cope with changing conditions:
scanning, monitoring, forecasting + assessing.
- One widely-experienced problem is customer churn. INFS 2036
TURBULENT BUSINESS ENVIRONMENTS
C U S T O M E R C H U R N
- Customer churn (customer turnover) is the number or percentage of
customers that stop paying for your subscription, product or service over a
certain period of time.
- One of the biggest enemies of any company and happens for many reasons
including poor customer service and lack of brand loyalty.
- Customer attrition is one of the most important KPIs as a signal of customer
engagement and overall brand loyalty.
- Understanding and predicting what’s causing customer churn can create a
valuable metric in business.
INFS 2036 33
INFS 2036 34 TURBULENT BUSINESS ENVIRONMENTS
N E T F L I X : I N D U S T R Y D I S R U P T O R!
- Netflix disrupted the Free-to-Air television industry with small monthly fees, no lock-in contracts, a very large range of shows, no ad breaks, original programming and TV “whenever and wherever you like”.
- Issues with Free-to-Air included:
- Outdated broadcasting model
- Watch it now or don’t watch it
- Reality TV ad nauseum
- Lack of customer control or choice — watch shows as and when screened (no binge watching).
- Netflix designed to complement TV however customer churn was enormous (40 million paying subscribers in 2014 to 150+ million by Q2 ).
- How did Netflix grow their customer base and dominate the market? Free-to-Air TV viewership
37 TURBULENT BUSINESS ENVIRONMENTS
O T H E R T U R B U L E N T E N V I R O N M E N T S
- Wicked problems: complex social or cultural problems:
- the amount of information available to ‘solve’ the problem is unmeasurable
- no agreement can be reached on its definition
- impossible to gauge if a sufficient solution has been achieved e.g.
homelessness, environmental issues, healthcare.
- Complex problems: tthose that include the ability to approach a problem from
multiple, sometimes competing, perspectives e.g. a decline in retail, terrorism.
- Cynefin Framework is one way of better understanding what is happening as
different problems warrant different solutions.
INFS 2036
38 IS ANY INDUSTRY ‘SAFE’ FROM DISRUPTION?
T H E F U T U R E O F T H E F U T U R E
INFS 2036
39 TURBULENT BUSINESS ENVIRONMENTS
I N D U S T R Y + D I G I TA L D I S R U P T I O N
There are always patterns,
you just have to learn to look
for them.
INFS 2036
R E TA I L E R S U S I N G T E C H N O L O G Y
F O R I N D U S T R Y D I S R U P T I O N
Industry Disruption INFS 2036 40 THEORY PRACTICE
How would or could amazon go use
∗ ETL
∗ DWs
∗ IoTs
∗ AI (ML, DL, ES, HS, SS)?
The only limit is your own vision – think big J
