Summary of the course -M-6 Business Analytics and Emerging Trends lectured by Dr. Hans Weigand and Murat Tunc in 2021 (block 2). All lectures, lecture slides and notes have been included in this summary. Also a very brief summary of the readings for this course are included in this document.
,Lecture 1 Business Analytic Trends (26-10)
Big Data and AI
• The appeal of Big Data:
5V’s of Big Data:
1. Volume
2. Variety
3. Velocity
4. Value
5. Veracity
Impose technical and managerial challenges:
• Technical challenges: How to deal with volume of the data, this requires a new powerful
technology like moving to the cloud for example → How to deal with real time data
processing.
• Managerial challenges: look at structure, data landscape looked structured with centralized
databases, very well defined. But nowadays data comes in from everywhere and it becomes
difficult to integrate these → you must manage this somehow.
Important challenges, but also represent opportunities and threats for the business.
Business value: many examples of companies that report about success stories with big saves or
revenues on the basis of data analytics. See slides for the examples; in inventory management,
marketing and in reducing fraudulent claims in insurance companies.
Tom Davenport: (…) “Peter Drucker, who noted almost 20 years ago that one of the risks of
corporate IT lay in the fact that because it relies only on internal data, it encourages a “degenerative
tendency…to focus inward on costs and efforts, rather than outward on opportunities, changes, and
threats.” One of the great advances of big data is that so much of the data now comes from external
sources” → it forced companies to use external data instead of only internal data. This use of
external data is already an advantage.
“Of all you can do with big data, developing new products and services is the most valuable” → Best
thing to do with big data is not so much in improving existing process but in developing new products
and services. So adding data services for example to an existing product.
Caveat 1: Big Data and Thick Data: There are many reasons for Nokia’s downfall, but one of the
biggest reasons that I witnessed in person was that the company over-relied on numbers. They put a
higher value on quantitative data, they didn’t know how to handle data that wasn’t easily
measurable, and that didn’t show up in existing reports.” (Tricia Wang, 2016) → Focus on big data is
good, but it’s not everything. E.g. Nokia trusted much on quantitative data (numbers) but at certain
moment they came into trouble because they trusted to much on these quantative data and didn’t
see what was not measurable, not in these KPI’s. → message;
• Beware of quant bias or quant addiction;
• Big data needs to be supported with Thick data. Thick Data; (emotion, context, meaning) →
data in which emotions are visible and the context where the data is collected is taken into
account. Context can relativise or give meaning to the data. These thick data needs to be
complementary to the numbers → needs techniques for like;
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, o Ethonography; describe culture by observing and talking to people, get to know
people’s way of living. Use ethnographic techniques to cover these thick data.
• So thick data improve the use of big data or analytics by seeing the whole picture of decision
making.
Caveat 2: Relate to business challenges: Many companies ended up spending a lot money without
any benefits. → There is a lot of data, so many things that you can do. In a certain data set there are
often a lot of patterns that you can identify. Off all these patterns and directions you need to find out
which ones are the most useful, help the business. So the challenge is not to just start exploring the
data but to be aware of your business goals and focus your data analytic efforts on solving these
problems and improving these decisions.
Business Strategies for Data Monetization: Deriving insights from practice: several ways of data
monetization. Based on literature review about the use of data and monetizing data, the researchers
came up with 12 categories of data monetization strategies. See sheets.
➔ There is a lot to win from using data, but you should manage it in a structured way to really
read the benefits.
Why Big Data Now?: combination of different causes:
• Availability of massive amounts of digital data
• Combination of technical developments and societal needs
• A philosophical view
o Rationalism vs empiricism
• The discovery, in science itself, of the power of data
Presents of big data; because it’s there…; companies already have digital information systems and
new technology produce a lot of extra data etc. → there is a lot of data, you want to see what you
can do with it. We have the idea that we can gain a lot, but we can only access that by trying to find
out what is there.
Why Big Data now: Technological developments:
Radical changes in: (now vs. how it was)
• The way elementary data are captured
o Sensors (automated) vs keyboard (human)
• The way data is stored → business intelligence (data warehouses, data analytics etc.)
o Main memory and cloud vs hard disk
• The way data is analyzed
o Data-driven methods vs sampling
• The way data is provided to users
o Data logistics (keeping data together) vs data integration
• The way data is presented → visualization
o Graphical interactive visualizations vs management reports
• The way knowledge (business rules, models) is created → by means of machine learning/
data mining
o Learning/mining vs (labor-intensive) knowledge acquisition
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, → these technological development enabled big data and analytics. Run in parallel with societal
demands of more real time information, processing, developing, business intelligence, insights and
action.
Information Culture: philosopher’s view: “When you two have finished arguing your opinions, I
actually have data!” → arguing about certain issues, but sometimes it’s much easier to just look at
the data which can solve the problems better and give better insights then endless arguing.
Philosophical trends; extension between rationalism (theory, thinking, abstract knowledge) vs
empiricism (rely on data, empirical data). Now we are in the empirical time.
Researchers view: discovery of the power of data. More data is more important than better
algorithms. →
Example 1: differences between the different algorithms, but the more data you use for the training
of the algorithm, the better the performance. → power of big data relative to clever algoritms.
Example 2: Google Flu trends. Could predict certain increase of the Flu, was better but also more
timely (give prediction every day) by just using data. But not without criticism, data was not always
that successful in predicting, but was still interesting to show the potential.
From Big Data to AI:
Data explosion; the available data is rising rapidity. At the
same time there is the ability to do something with the
data, which also grows but if you use the traditional
techniques it is growing rather slowly → the gap between
the data and what you can do with it becomes bigger →
problem; want to explore the data but the processing
capabilities are limited. So big data succeeded but at the
same time big data failed in the sense that you couldn’t
really use all the big data that you wanted.
Data became the problem, but the problem empowered the solution. Because of the availability of
the big data sets enabled AI machine learning techniques, which became a solution of solving these
gaps. For machine learning you need big data sets for training, if the data sets are there AI can
become an instrument to solving this gap; analysing the big data sets with on new powerful
techniques based on learning.
The new AI technologies are driven by data and analytics: use
historical data for training, new data for re-training and on the basis
of the models that you learn you can make predictions.
Machine Learning is at the fore of most of them.
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