This summary covers an extensive yet informative summary of the articles covered between week 1 and week 5 of the course BIA. This summary excludes the articles of week 6 as they are better summarised in the lecture
- Rapid digitisation creates challenges:
- It is unevenly distributed within organisations - makes it di cult to stabilise/ simplify
increasing amounts of data (as a result of digitisation)
- Makes it more di cult to derive insight, and proceed based on this insight (inhibits
strategy creation)
Key point in article: show how big data is being used in practice to craft strategy and
company business model
- Explore opportunities to improve and innovate the business model
Improve Business Model
- Big data supports the measuring and monitoring of strategy in novel ways by o ering:
• New data: manufacturers long struggled with a lack of end-consumer visibility
(eg: gather customer-produced content, track actual product usage, understand
customer preferences and behaviour)
• New insight: utilising data for problem-solving; not new big data, new big
approaches and techniques (eg: high end statistics and visualisations)
• New actions: as companies become well-armed with big data and become
pro cient at gaining insights, they act di erently - faster and more wisely (eg: real-
time customer sentiment; changing tv show schedule)
• New action as a result of working smarter across an organisation
- Most e ective improvements result from creating well-articulated strategies informed by
data, then shaped accordingly
Innovate Business Model
- Companies use big data to nd di erent ways to generate revenues. Two approaches
exist:
1. Data Monetisation: the act of exchanging information based products and
services for money (or of equivalent value)
Includes a range of o erings:
1
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, • Raw data
• Enhanced data
• Reporting & analytics
• Process design
Can also include tangible products
Companies monetise by:
- Selling: companies receive money in exchange for information o erings
- Bartering: when companies choose to trade information in return for tools,
services or special deals (exchanging info for services; not including money)
- Wrapping: wrapping information (adding more data driven insight to other
products and services) eg: a taxi trip being wrapped with fare estimator
2. Digital transformation: increased amounts of new and di erent types of data
Blurring company boundaries: making it more di cult to tell where a partner
organisation engagement begins and ends
- Big data enables companies to transform - propels them into new industries and
ecosystems - alters traditional competitive landscapes
Conclusion: big data o ers rich, exciting opportunities to leverage and extend a
company’s business strategy toolbox
Generating value through big data is reliant upon strategy makers
Analytics: New Path to Value
- As new technologies collect more data, companies are looking for better ways to obtain
value from data and compete in the marketplace.
- Executives want businesses running on data-driven decisions - scenarios and
simulations that provide immediate guidance on the best actions to take
Key point in article: Understanding how the smartest companies use increased
information and analytics to gain competitive advantage
- Top performers:
• Put analytics to use in widest possible range of decisions
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, • Are twice as likely to use analytics to guide future strategies and use insights to guide
day-to-day operations
• Make decisions based on rigorous analysis
Three Levels of Capabilities
1. Aspirational: Farthest from achieving desired analytical goals
• Focus on e ciency or automation of existing processes
• Search for ways to cut costs
• Have few of the necessary building blocks to collect, understand, incorporate or act
on analytical insights
2. Experienced: Have gained some analytical experience
• They look to go beyond cost management
• Developing better ways to e ectively collect, incorporate and act on analytics
3. Transformed: Have substantial experience using analytics across a broad range of
functions
• They use analytics for competitive di erentiation
• Less focused on cutting costs
• Focused on driving customer pro tability and making targeted investments in niche
analytics
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, Getting the data right (collecting data) is not a top challenge: the biggest obstacle is the
lack of understanding of how to use analytics to improve the business.
The article suggests a ve-point methodology for successfully implementing analytics-
driven management and for rapidly creating value.
Each recommendation ful ls all three of the critical management needs:
1. Reduced time to value
2. Increased likelihood of transformation that’s both signi cant and enduring
3. Greater focus on achievable steps
The 5 recommendations are:
1. Focus on the biggest and highest value opportunities: have clear strategic business
direction, ensure everyone can see the value of it
• Be a local supporter
• Strong, committed sponsorship
• Link incentives and compensations to desired behaviours
2. Within each opportunity, start with questions, NOT DATA: rst de ning the insights and
questions needed to meet the big business objective and identify those pieces of data
needed for answers
3. Embed insights to drive actions and deliver value: embedded information makes
insights more understandable and actionable (use cases, analytics solution, work
ows)
• Alignment between business and IT strategies
• Ask to see what analytics went into decisions
• A strong data infrastructure
• The right analytical tools
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