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BI
BUSINESS INTELLIGENCE
INFS 2036
Business Intelligence
We e k 6
B U S I N E S S A N A LY T I C S +
B U S I N E S S P E R F O R M A N C E
M A N A G E M E N T
BI
BUSINESS INTELLIGENCE
INFS 2036
Business Intelligence
We e k 6
B U S I N E S S A N A LY T I C S +
B U S I N E S S P E R F O R M A N C E
M A N A G E M E N T
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 10 You’re the CEO (Exam Review) Week 8 Mining Technologies Week 9 Data Integration at an Enterprise and Cross-Organisation Level + Strategic value of information in the future LINKING CORPORATE
Week 6 Reading/Viewing B U S I N E S S A N A LY T I C S + B U S I N E S S P E R F O R M A N C E M A N A G E M E N T INFS 2036 3 Textbook
- Chapter 3: 3.9. Business Performance Management - 3.11. Balanced Scorecards (pp. 196-205)
- Review 1.2. Changing Business Environments and Evolving Needs for Decision Support and Analytics and 1.3. Evolution of Computerized Decision Support to Analytics/Data Science (pp. 37-41)
- Chapter 7: 7.7. Big Data and Stream Analytics to 7.8 Application of Stream Analytics (pp. 432-438)
- None! Additional reading/viewing – see Workshop Notes page (course site)
- Focus On … Closed-Loop BPM Cycle
- Putting it Into Practice
- What’s coming next… Key Points + Workshop Activities
Business analytics and business performance
management, including
- Linking strategy to execution
- Link between corporate and BI strategy
- How Performance Indicators and Metrics
relate to performance management
- Operational, tactical and strategic
performance management
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 6 THEORY PRACTICE This Week’s Case Studies Starbucks + A University attracting students
Business Intelligence vs Business Analytics INFS 2036 7 Intelligence is what you have and analytics is what you can do with it
Howard Behar, ex-President of Starbucks International, grew the company from 28 to 15,000 locations by: ✮ Staying Connected — he knew each employee, their family and goals, using Starbucks to support those goals. ✮ Making Culture Intentional — as the company grew each territory manager stayed connected to the employees; ✮ Measuring What Matters — a metric for employee success was doing something special for another employee, a customer, or the community. Metrics on how many coffees or pastries they sold were not used! ✮ Building the right team — when building the international team he passed on the candidates asking their new title + salary. He hired the candidate who asked "How can I help make it happen?" ✮ Keeping his eye on the big picture — leadership was based on care, compassion and putting people and values first (compared to struggling companies focusing solely on cost management and organisational charts). m e a s u r i n g w h a t m a t t e r s INFS 2036 (^) https://www.inc.com/ian-altman/how-starbucks-kept-its-culture-while-growing-from- 28 - to- 15000 - stores.html 8 Starbucks has built a business valued at over $75 billion by focusing on people and communities, not coffee. This Week’s Case Study — Starbucks Starbucks measures its success in employee accomplishments (not lattes)
L I N K I N G C O R P O R AT E + B I S T R AT E G Y
INFS 2036 9 Optimise Organisational Performance
Discovery
Response
Monitoring
Predicting
Goals
PIs
Metrics
Targets/KPIs met Note: same star symbol used to make visual connection, also no different indicator when target is well exceeded: Monday 7,267 over 10,000 target Drill down detail Trend Information daily and weekly, visually and in detail Only provides so much information – the next step would be why am I both exceeding and not meeting targets?
A S I M P L E S T O R Y
Performance + Prediction INFS 2036 10 Discovery Response Monitoring Predicting Goals PIs + Metrics
L I N K I N G C O R P O R AT E + B I S T R AT E G Y
Optimise Organisational Performance INFS 2036 13 Organisations perform DISCOVERY OF THE WHAT + WHY Find out more about business problems and opportunities + what is causing them and why? E.g. business problem/opportunity a Woolworths manager would have with lettuces? Then they give their RESPONSE The action taken to change or optimise business processes E.g. hire more staff who will perform well and stay, change lettuce suppliers And check their response by MONITORING Is the implemented change is working? E.g. are the lettuces fresh? If it doesn’t work then do more DISCOVERY to find out what/why … Discovery Response Monitoring Discovery Response Monitoring DISCOVERY (what/why) + RESPONSE + MONITORING
Corporate Plans, Performance Indicators, Metrics 14 Corporate Plans agreed plan to achieve a goal and how to get there be the ‘fresh food people’ by ensuring fresh lettuces every day Performance Indicators indicator or progress towards a goal using metric(s) freshness of lettuces on the shelves Metrics business activities that are measured time spent on shelf, temperature of lettuces, …
- It is better for any organisation to be proactive – to know in advance – what will cause the process to go off track. An organisation who can predict and prepare can hopefully avoid problems.
- After performing DISCOVERY/RESPONSE/MONITORING several times targets can be set in advance to reflect what an organisation should aim for in its Corporate Plans, Performance Indicators and Metrics so it can be adaptive and responsive to performance that is off-target.
L I N K I N G C O R P O R AT E + B I S T R AT E G Y
**Goals PIs
Metrics** INFS 2036
15 This Week’s Case Study: Starbucks A C T I V I T Y
How could Starbucks apply
DISCOVERY (what/why) + RESPONSE + MONITORING
to this priority?
Inspire and nurture the human spirit
through quality coffee.
Our Coffee It has always been, and will always be, about quality. We're passionate about ethically sourcing the finest coffee beans, roasting them with great care, and improving the lives of people who grow them. We care deeply about all of this; our work is never done https://www.starbucks.com.au/Mission-Statement.php
PRIORITY
L I N K I N G C O R P O R AT E + B I S T R AT E G Y
INFS 2036 16 Optimise Organisational Performance
Discovery
Response
Monitoring
Predicting
Goals
PIs
Metrics
L I N K I N G C O R P O R AT E A N D B I S T R AT E G Y
Optimise Organisational Performance INFS 2036 19 Where do we want to be? Corporate Plans/Performance Indicators /Metrics How are we going? Past trends/Future forecasts Why are we on/off/ahead of track? What are the contributing success/failure factors? How do they interrelate and impact each other? What are the cost/demand/risk drivers? Identifying and improving business processes What can we do about it? Which changes could we make and where? Business Analytics **Optimise organisational performance Drive changes in organisational processes Goals PIs
Metrics Discovery Monitoring**^ Predicting Response Get the data Use the data organisational performance^ Optimise Drive changes in organisational processes
L I N K I N G C O R P O R AT E + B I S T R AT E G Y
INFS 2036 20 Optimise Organisational Performance
Discovery
Response
Monitoring
Predicting
Goals
PIs
Metrics
P E R F O R M A N C E I N D I C AT O R S ( P I s ) + M E T R I C S Optimise Organisational Performance INFS 2036 21 events data non-PIs PIs Every item of data is a potential metric (‘000s to pick from) events produce data Non-PIs data indirectly/not related to organisation performance (e.g. number hours lights switched on) PIs data related to organisation performance (e.g. how many hits on corporate website) metric
P E R F O R M A N C E I N D I C AT O R S + M E T R I C S
Optimise Organisational Performance INFS 2036 22 PIs Metrics
Mechanisms to focus an organisation’s efforts
- Often formed from multiple sources
- Used to drive behaviour/focus on aspect of the organisation
Metric
- A (business) activity that is measured
- E.g. Sales in the Southern Region
Performance Indicator – metric with a target
- E.g. Increase of sales in Southern Region by 5% within 12 months PIs track whether you hit business objectives/targets while metrics track processes. Event – a sale was made Data – sales amount, sales locations, sales date + time etc.
P E R F O R M A N C E I N D I C AT O R S v s K E Y P E R F O R M A N C E I N D I C AT O R S Optimise Organisational Performance INFS 2036 25 PIs may include focused and longer-term measures:
- Profitability of the top 10% of customers
- Net profit on a particular product group
- Percentage increase in sales in a particular region
- Percentage participation of employees in training scheme KPI’s vary based on strategic objectives however share common characteristics:
- Forward-facing + have significant impact on fulfilling a strategic objective – e.g. transition to online teaching without closing the University
- They are linked to responsibilities of teams and individuals e.g. Course Co-ordinators to move all courses online in 3 days.
- They generally impact all other performance measures
- They are non-financial
- They have short measurement cycles and must be constantly monitored PIs KPIs Starbucks measures its success in employee accomplishments (not lattes)
INFS 2036 26 Optimise Organisational Performance
- A mix of past, present, and future
- Financial and non-financial (e.g. profit, customer satisfaction)
- Leading and lagging - future and past
- Internal and external (e.g. new car rollout time, responsiveness of suppliers)
- Quantitative and Qualitative
- Short term and long term (e.g. new customers this quarter, next 5 years)
- Balance the needs of shareholders, employees, partners, suppliers, stakeholders, etc.
- PIs and KPIs let everyone in the organisation know, including management, what is important and what is agreed
- Alignment–PIs can be very useful to help achieve alignment. P I s + K P I s You don’t want too many PIs and KPIs but not too few then what mix should you have …
27 This Week’s Case Study —Starbucks A C T I V I T Y Write a metric for Quality and turn this metric into a Performance Indicator. How does the PI differ if we do/don’t have a baseline?
These metrics can be turned into Performance Indicators e.g.:
- Achieving 100% of coffee suppliers that are ethical
- Improving at least 75% of grower’s lives
- Subjective targets so we need to get specific!
- What does ethical mean?
- How do you measure lives improved? PIs Metrics Our Coffee It has always been, and will always be, about quality. We're passionate about ethically sourcing the finest coffee beans, roasting them with great care, and improving the lives of people who grow them. We care deeply about all of this; our work is never done
28 What are the metrics? What PIs can we write? Which are KPIs? Incorporating PIs into tools e.g. dashboards This Week’s Case Study Starbucks A C T I V I T Y
1. Strategise: Where do we want to go? - Requires setting goals and objectives - Identifying + stating the organisation’s mission, vision and objectives (e.g. Starbucks) - Developing plans at the strategic, tactical and operational level to achieve objectives 2. Plan: How do we get there? - Requires establishing initiatives and plans to achieve those goals - Uses budgets, plans, forecasts, models, initiatives, targets (detailed operational and financial plans) 3. Monitor/Analyse: How are we doing? - Requires monitoring actual performance against goals and objectives. - Uses performance dashboards, reports, analytical tools 4. Act/Adjust: What do we need to do differently? - Requires taking corrective action - Uses interpretation, collaboration, assessment, decisions, acting, adjusting and tracking 31 CLOSED-LOOP BPM CYCLE A M O R E D E TA I L E D L O O K
Business Performance Management INFS 2036 2019 32
Get the data Use the data
Optimise
organisational
performance
Drive changes
in
organisational
processes
… A N D B U S I N E S S I N T E L L I G E N C E
L I N K I N G C O R P O R AT E A N D B I S T R AT E G Y
Optimise Organisational Performance INFS 2036 33
1. Where do we want to be? - Corporate Plans/Performance Indicators /Metrics 2. How are we going? - Past trends/Future forecasts 3. Why are we on/off/ahead of track? - What are the contributing success/failure factors? - How do they interrelate and impact each other? - What are the cost/demand/risk drivers? - Identifying and improving business processes 4. What can we do about it? - Which changes could we make and where? organisational^ Optimise performance Business Analytics DISCOVERY (what/why) + RESPONSE + MONITORING making as part of their usual procedures, and SMEs are the ones pres- ently struggling to enhance top management decisions while adding more data for the analysis process. Aligning the people, technology, and organizational resources to become a data-driven company is problematic ( making and increase organizational output; this is possible when aWeill & Ross, 2009). Given BD can enhance the decision- selection of analytical methods is used to extract sense from the data, such as:
- descriptive analytics rent state of a business situation in a way that developments, patterns scrutinizes data and information to define the cur- and exceptions become evident, in the form of producing standard re- ports, ad hoc reports, and alerts (Joseph & Johnson, 2013);
- inquisitive analytics propositions, for example, analytical drill downs into data, statistical is about probing data to certify/reject business
- analysis, factor analysis (predictive analytics is concerned with forecasting and statistical model-Bihani & Patil, 2014);
- ling to determine the future possibilities (prescriptive analytics is about optimization and randomized testing toWaller & Fawcett, 2013); assess how businesses enhance their service levels while decreasing the expenses (Joseph & Johnson, 2013); and
- pre-emptive analytics ary actions on events that may undesirably in is about having the capacity to take precaution-fluence the organiza- tional performance, for example, identifying the possible perils and recommending mitigating strategies far ahead in time (Szongott, Henne, & von Voigt, 2012). improved decision-making and organizational performance by making^ Advocates assert that these types of analytical methods support in everything more translucent and quanti inconsistencies as well as potential concerns and opportunities.fiable, while further uncovering Fig. 2 illustrates the classi Q2 are based on thesefication of BDA methods and the SLR five categories. findings for
- Research methodology insights to the phenomenon of big data and bit data analytics, the^ In an attempt to better understand and provide more detailed authors respond to the special issue call on Technology and Organizational Resource Management (speci Big Data and Analytics infically focus- ing on conducting Big Data Challenges and Big Data Analytics methods theorized [in extant – A comprehensive state-of-the-art review that presents research literature], proposed [by research scholars], and employed [by organizations]) through a SLR methodology as opposed to narrative or descriptive reviews ( Charters, 2007; Wang, Gunasekaran, Ngai, & Papadopoulos, 2016Tranfield, Denyer, & Smart, 2003; Kitchenham &). In support of the former approach, report that SLR is a rational, transparent and reproducible research Lettieri, Masella, and Radaelli (2009) methodology for the analysis of extant literature. Charters (2007) also highlight that SLR is a form of secondary study Kitchenham and and it is a distinct approach to establish, explore and deduce accessible proof associated to a particular research question (e.g. Q1 and Q2) in a way that is unprejudiced and (to a certain degree) repeatable. Alterna- tively, meta-based-approaches can be used to conducting a literature review and include the work of and Childe (2016), which adopt a bibliometric and network analysis Mishra, Gunasekaran, Papadopoulos, approach to obtain and compare in (in this example, Big Data in Supply Chains).fluential work in a specific domain DESCRIPTIVE Analytics PREDICTIVE Analytics PRESCRIPTIVE Analytics Inquisitive Analytics Pre-Emptive Analytics understanding e.g. “What^ Analytics that help in happened in the Business?” anticipating e.g. “What is likely^ Analytics that help in to happen in the future?” responding “So What?”^ Analytics that help in and “Now What?” comprehending e.g. “Why^ Analytics that help in is something happening in the business?” recommending “What is^ Analytics that help in required to do more?” Information Insights Decision Action Fig. 2. Classification of types of big data analytical methods. 266 U. Sivarajah et al. / Journal of Business Research 70 (2017) 263– 286 DIAGNOSTIC What happened? What is likely to happen? What are we required to do more of? Analytics are broken up into a lifecycle of decision-making
August Census Stay in/drop course September Assessment 1 submit/results October/November Assessment 2 submit/results August - November Engage with LO August Start classes Study Period 5 Course Co-ordinator Check enrolment levels OK Identify abnormal level of drop outs and cause, raise with teacher Class teacher Profile student cohort Identify non-submitters, failures, poor marks & contact them Arrange/refer to support services Check corrective action worked Check participation in face to face, engagement online Drive Changes in Organisational Processes E X A M P L E – S TA R T I N G AT U N I INFS 2036 34
INFS 2036 37 Traffic Light Metric Performance Satisfactory Neutral Unsatisfactory Supporting Tools: Metrics, PIs, Scorecards + Impact Diagrams THEORY PRACTICE S C O R E C A R D S
INFS 2036 38 Supporting Tools: Metrics, PIs, Scorecards + Impact Diagrams THEORY PRACTICE I M PA C T D I A G R A M S Which are PI(s) and KPI(s)? Traffic Light Metric Performance Satisfactory Neutral Unsatisfactory
INFS 2036 39 Metrics, PIs + Impact Diagrams THEORY PRACTICE
A large university is trying to attract more students.
1. What metrics would be needed for this?
2. What could some PIs be? KPIs?
3. Which would be used for BI? BA?
4. Produce an Impact Diagram for this priority
Strategic Senior Management Tactical Middle Management Operational Junior Management
For Week 7, think about:
1. Privacy: what are potential privacy issues with use of your personal data?
2. Ethics: what are some potential ethics issues with use of your data?
3. What’s the difference between Privacy + Ethics and how could
organisations achieve a balance between access to, and usability of, your
personal data?
Week 7 Case Study:
how do social media companies and other organisations use your
personal data? What is even considered data?
Privacy, Ethical + Legal Issues INFS 2036 40
Think-Come Back-Contribute!
