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Lectures BIA
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Lectures of BIA
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Lecture 1 26-10Transformation process. Input goes into organization, organization does something with it then
generates output. In the transformation process, all different departments have their own
information requirements. Business intelligence delivers this information.
Roots go back to late 1960s. computing made its entry into the business world. In the early years,
mostly one way information systems. Grew to more interactive systems. Nowadays much discussion
of using the term analytics instead of intelligence.
Bi is a broad category of applications, technologies and processes. That aims at gathering, sorting,
accessing and analysing data. With the purpose of helping business users make better decisions.
Data sources became more varied and more dense. More source systems (increasingly big data).
Being able to analyse all this data is complex. Advances analytics are growing in popularity and
importance, both as decision support tools and as core business building blocks.
Woerner & Wixom. What does big data mean for business strategy toolbox. Role of data has moved
from supportive to a new source of value. Big data= high in volume, many data points. High in
velocity. High in variety. High in veracity, how trustworthy the data is. data and information
system have changed in their function and role, from supportive to new source of value. Need to
know framework
What is meant by analytics?
Three levels of analytics 1) descriptive 2)predictive 3)prescriptive.
1. descriptive. Analysis that happened in the past. Focusses on; what has occurred?. Looks at the
past, at what happened.
2. predictive. Focuses on: what will occur.
3. prescriptive. Focusses on what should occur.
gets more difficult when we get more towards prescriptive. Can see it as climbing a mountain, the
analytics pyramid.
Lavalle et al. where are organizations now? Asked questions to organizations about different aspects
of their analytics capability. Came up with a framework with 3 different analytics capability levels
1) aspirational, want to do something with it but don’t have skills etc to do something with it. 2)
experienced & 3) transformed.
Looked at different aspects. As you get more transformed, analytics is more present in all
departments in the organization. 3 levels also have different key obstacles toward analytics.
Different levels of data management, how skilled you are with working with data. need to know
different levels, not all different bullet points. Need to be able to classify organization in a level.
Couple of recommendations for implementing analytics.
need to have clear business need, so start with a specific question.
Focus on the biggest and highest value opportunities. So make prioritizations
Embed insights to drive actions and deliver value. It should be embedded in your
organization, not just one department. So really integrated
Keep existing capabilities while adding new ones.
Use an information agenda to plan for the future.
, If you want to implement advanced analytics in your organization, it is not sufficient to have
knowledge about data. need to have knowledge about your business domain and about modelling
of data. skills are distributed across the organization.
Role of the data scientist: uses advanced algorithms and interactive explorations tools to uncover
non-obvious patterns in data, have knowledge about modelling and data. usually has a
multidisciplinary background
Role of business analyst. Uses business intelligence tools and applications to understand and
improve business conditions and business processes. Can have various degrees of technical know-
how. Knowledge about the business domain.
Where to put the analytics team evolves with different capability levels. Individuals spread
throughout the organizations standalone unit in some form of cross-functional competence
centre.
Parmar et al --> patterns in creation value from data and analytics. Several generic patterns that
organization use to create value form data and analytics.
1. Augmenting products to generate data.
a. Using sensors, wireless communication and big data to gather data for product
improvement.
2. Digitizing physical assets
a. Ex-post or ex-ante digitization of physical assets
3. Combining data with and across industries
a. Coordination of data across industries/sectors through enhanced data integration
4. Trading data
5. Codifying a capability.
a. 4&5. Both patterns boil down to selling the data and developed capabilities under
patterns (1) to (3) others. Can only develop these patterns if you have developed
one of the other 3 patterns. Otherwise you can sell, trade it to other organizations.
Hartmann et al tried to say something about firms that use big data. what are these (start up)
organizations actually doing? Start up does not have background that hinders forming new
capabilities. Created a taxonomy of business models used by start-up firms. Map key activity
(aggregation(grouping the data at a higher level), analytics & data generation (generating data out of
data)) and key data source (tracked & generated, customer provided & free available).
Different business models based on key data source and key activity. Not learn methods of the
paper. Can ask questions about the different business models and dimensions. Able to reproduce
the table and explain a bit about each business model.
Exam preparation. Focus on knowledge and bridging theory and practice. 25 multiple choice
questions, 5 open questions.
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