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Summary BIA (E_IBK3_BIA) - all lectures and mandatory articles
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Lecture 1:Every minute, a huge amount of information is created online. This includes things like millions of
searches, watching hundreds of thousands of hours of videos, and more. All this data helps
understand how people behave and what trends are happening globally. This shows that our world is
changing a lot because of technology and the use of data.
Business Intelligence (BI) – is a way of collecting, studying, and sharing information from different
sources to learn about how a business is doing. It helps make decisions to reach the goals of the
organization.
With BI you can for example identify market trends, and compare your product with products of
other companies from which you can make strategic marketing decisions. You can also use BI to make
it more personal for the specific customer.
Nowadays data is really important for your company, to improve and make quick business decisions.
The reasons to use BI is to increase productivity and it also helps in achieving goals. Also by collecting
information and making forecasts, processes can be optimized which will have an impact on the ROI.
Business Intelligence (BI) is becoming more complex. There are more data sources like social media
and sensors, which means there's a lot of data to deal with (often called Big Data). Companies are
using cloud-based systems like Hadoop and Spark to store this data. Advanced analytics, which help
make decisions and improve business, are becoming really important. They're not just extras
anymore; they're crucial for planning and efficiency. This shows a big change: analytics are now
central to a business, helping it innovate and stay ahead in a world where data is key.
Business Analytics (BA) – is a structured way of using computers to analyse data or statistics. It
combines statistics, computer science, and operations research to model and solve problems in
business and industry.
Business Analytics (BA) involves using a company's data to foresee future trends and results. It
includes activities like digging into data, doing statistical analysis, and making predictions.
On the other hand, Business Intelligence (BI) is a structured process for analysing a company's
operations. A BI system gathers data from different places, assesses it, and presents it visually. The
aim is to provide a solid foundation for making business decisions.
BA and BI work well together. Ideally, they're used in tandem to not only report on current
performance indicators but also to make precise predictions about the future.
The thing they have in common is collecting, analysing, visualising data and creating reports.
There are different ways to look at data, so there are also different types of analysts. We can look at
given information from past, present and future: what has happened (reporting), what is happening
now (alerts) and what will happen (projection). You can also use the data to get more insights: how
and why did something happen (modelling), what is the best next step (recommendation) and what
will happen in the best and worst case (prediction, optimization).
Descriptive: Describing past events purely based on statistics without exploring why they happened.
Examples include tracking Key Performance Indicators (KPIs) or market shares.
Diagnostic: Analysing data to understand why certain events occurred in the past, focusing on
identifying root causes behind trends. It involves techniques like data discovery, drill-down, and
correlations, supporting hypothesis testing.
,Predictive: Forecasting likely future outcomes by extending current trends using statistical modelling
and machine learning techniques. It helps align a company with its strategic goals by anticipating
future developments.
Prescriptive: Providing recommendations for future actions based on analysing different potential
scenarios. It aims to optimize decision-making and doesn't just predict what will happen, but suggests
what should happen to achieve the best outcome.
Every business uses analytics on their on way, only a little or a lot; aspirational, experienced and
transformed. You can see that aspirational companies do use data but only on a small scale, while
transformed companies use that data in such a way that it is really helpful for the company.
When an organization wants to implement business analytics, they need to take a few things into
account:
- Start with Questions: Instead of diving straight into data, begin by asking clear questions
related to business needs.
- Focus on Important Opportunities: Concentrate on the most significant and valuable
opportunities for improvement.
- Gain Support: Be vocal about supporting analytics initiatives and ensure strong sponsorship
from committed leaders. Also, connect incentives and rewards to desired behaviours.
- Turn Insights into Actions: Make sure that insights from analytics are used to guide actions
that deliver value. Align business and IT strategies, and ask to see the analytics behind
decisions.
- Build the Right Infrastructure: Establish a robust data infrastructure and use appropriate
analytical tools.
- Evolve Without Losing What Works: Recognize that some people may struggle with new
approaches, but emphasize the importance of leaving outdated methods behind. Also,
ensure there are skilled analytical personnel in a suitable organizational setup.
- Plan Ahead with an Information Agenda: Develop a plan to guide future efforts and
investments in analytics.
Data Analyst: Analyses data to provide insights for decision-making.
Data Scientist: Uses advanced analytics to build predictive models from data.
Data Architect: Designs data storage and organization systems.
Data Engineer: Builds and maintains data processing pipelines and infrastructure.
The use of big data is characterized by the "Four Vs":
Volume: Dealing with large amounts of data.
Velocity: Processing data quickly.
Variety: Handling diverse types of data.
Veracity: Ensuring data accuracy and reliability.
Information systems are now using big data to shape how companies work and make money. Instead
of just helping with day-to-day tasks, they're:
Making Business Better: Finding answers to tough questions using new sources of data like sensors
and social media.
Doing New Things: Reacting quickly to changes in what customers want.
Coming Up with New Ideas: Making money from data by adding it to products, selling it, or trading it.
They're also exploring new industries.
Big data is all about handling lots of information fast and making sure it's accurate.
,Articles:
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A. (2014). Big data for big business? A taxonomy of
data-driven business models used by start-up firms. Cambridge Service Alliance. Working Paper.
The article explores how small, new companies, also known as start-ups, are using big data to run
their businesses. It's like they're using big data as a tool to help them succeed. The authors studied
many of these start-ups to see how they use big data in different ways. They found that there are
several common ways start-ups are using big data, and they came up with a system to organize these
different methods.
One way start-ups use big data is by collecting lots of information about their customers and their
business operations. They then analyze this data to help them make better decisions. For example,
they might look at what products customers are buying the most and use that information to decide
what to sell more of.
Another way start-ups use big data is by personalizing their products or services based on what their
customers like. For instance, if a customer always buys red shirts, the start-up might show them more
red shirts when they visit their website.
Some start-ups use big data to predict what might happen in the future. They do this by looking at
patterns in the data and making educated guesses about what might happen next. This can help them
prepare for changes in the market or anticipate what their customers might want.
Others use big data to target their advertising and marketing efforts. They use data to figure out who
their customers are and what they like, so they can show them ads or messages that are more likely
to catch their attention.
Additionally, start-ups use big data to make their operations work better. They look at data to find
ways to be more efficient and save money, like optimizing their supply chain or improving their
customer service.
Lastly, some start-ups make money by selling or sharing their data with other companies. For
example, a start-up might collect data about how people use their mobile app and then sell that data
to advertisers who want to target those users.
By understanding how start-ups use big data in these different ways, we can see how it's helping
them grow and succeed in today's digital world.
LaValle, S., Hopkins, M., Lesser, E., Shockley, R., & Kruschwitz, N. (2010). Analytics: The New Path to
Value
The article "Analytics: The New Path to Value" discusses how businesses are increasingly using
analytics, which is like using data to make smart decisions. Here's a bit more detail:
Growing Trend: More and more businesses are recognizing the value of analytics as a strategic tool.
They're using it to improve their operations and stay competitive.
Benefits of Analytics: Using analytics helps businesses in many ways. It helps them make better
decisions by giving them insights from data. It also helps them work more efficiently and come up
with new ideas for products or services.
Challenges: Even though analytics is helpful, there are some challenges. For example, businesses
need to make sure the data they're using is accurate and reliable. They also need to get everyone in
the company on board with using analytics, which can sometimes be tricky.
Stages of Analytics: Businesses go through different stages of using analytics. At first, they might start
with simple reports or dashboards to track basic information. As they get more comfortable with
analytics, they can move on to more advanced things like predicting future trends or outcomes.
Overall, the article shows that analytics is becoming really important for businesses to succeed in
today's world. It's not just about collecting data—it's about using that data to make better decisions
and drive business growth.
, Parmar, R., Mackenzie, I., Cohn, D., & Gann, D. (2014). The New Patterns of Innovation. Harvard
Business Review, 92(1,2), 86-95.
The article "The New Patterns of Innovation" from the Harvard Business Review talks about how the
way companies innovate is changing. Traditionally, innovation meant following a linear process:
companies would invest a lot of time and money in research and development to come up with new
products or services. However, the authors argue that this approach is becoming less effective in
today's rapidly changing world.
Instead, they suggest that innovation is now more about embracing new patterns or ways of doing
things. These new patterns include:
Collaboration: Companies are finding that they can innovate better by working together with others.
This might mean teaming up with customers to understand their needs better or partnering with
other businesses to share resources and expertise.
Experimentation: Rather than trying to plan everything perfectly from the start, companies are
encouraged to experiment and try out new ideas. This means being willing to take risks and learn
from failures along the way.
Ecosystems: Innovation doesn't happen in isolation—it's part of a bigger ecosystem of companies,
industries, and technologies. By being part of these ecosystems, companies can access new ideas,
markets, and opportunities for innovation.
Platforms: Some companies are finding success by creating platforms that allow others to build on
their ideas or technology. This might involve opening up their data or software to developers,
allowing them to create new products or services.
Adaptive Strategies: In today's fast-changing world, companies need to be flexible and able to adapt
quickly to new challenges and opportunities. This means constantly monitoring the market and being
willing to adjust their innovation strategies as needed.
Overall, the article suggests that companies need to rethink how they approach innovation. By
embracing these new patterns—collaboration, experimentation, ecosystems, platforms, and adaptive
strategies—they can stay competitive and continue to thrive in a rapidly changing business landscape.
Woerner, S. L., & Wixom, B. H. (2015). Big Data: Extending the Business Strategy Toolbox. Journal of
Information Technology, 30(1), 60-62. doi:10.1057/jit.2014.31
The article "Big Data: Extending the Business Strategy Toolbox" by Woerner and Wixom (2015)
discusses the integration of big data into business strategies. Here's a summary:
The authors highlight the importance of big data in shaping business strategies. They argue that big
data offers valuable insights that can extend the traditional toolbox of business strategy. By leveraging
big data analytics, organizations can gain a deeper understanding of their customers, market trends,
and operational performance, leading to more informed decision-making and competitive advantage.
Furthermore, the article emphasizes the need for organizations to develop the capabilities to
effectively collect, analyze, and interpret big data. This requires investment in technology
infrastructure, data management processes, and analytical skills. However, the potential benefits of
harnessing big data for strategic purposes are significant, offering opportunities for innovation,
growth, and enhanced performance.
In conclusion, the article underscores the transformative impact of big data on business strategy,
urging organizations to embrace this emerging trend and integrate big data analytics into their
strategic planning processes to stay competitive in today's data-driven landscape.
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