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Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support 57

data analyst is just another term for professionals who were doing business intelligence in
the form of data compilation, cleaning, reporting, and perhaps some visualization. Their
skill sets included Excel, some SQL knowledge, and reporting. A reader of Section 1.8
would recognize that as descriptive or reporting analytics. In contrast, a data scientist is
responsible for predictive analysis, statistical analysis, and more advanced analytical tools
and algorithms. They may have a deeper knowledge of algorithms and may recognize
them under various labels—data mining, knowledge discovery, machine learning, and
so forth. Some of these professionals may also need deeper programming knowledge to
be able to write code for data cleaning and analysis in current Web-oriented languages
such as Java and Python. Again, our readers should recognize these as falling under the
predictive and prescriptive analytics umbrella. Our view is that the distinction between
analytics and data science is more of a degree of technical knowledge and skill sets than
the functions. It may also be more of a distinction across disciplines. Computer science,
statistics, and applied mathematics programs appear to prefer the data science label,
reserving the analytics label for more business-oriented professionals. As another example
of this, applied physics professionals have proposed using network science as the term
for describing analytics that relate to a group of people—social networks, supply chain
networks, and so forth. See barabasilab.neu.edu/networksciencebook/downlPdf.
html for an evolving textbook on this topic.
Aside from a clear difference in the skill sets of professionals who only have to do
descriptive/reporting analytics versus those who engage in all three types of analytics, the
distinction is fuzzy between the two labels, at best. We observe that graduates of our
analytics programs tend to be responsible for tasks more in line with data science profes-
sionals (as defined by some circles) than just reporting analytics. This book is clearly aimed
at introducing the capabilities and functionality of all analytics (which includes data sci-
ence), not just reporting analytics. From now on, we will use these terms interchangeably.

sectiOn 1.8 revieW QuestiOns
1. Define analytics.
2. What is descriptive analytics? What various tools are employed in descriptive analytics?
3. How is descriptive analytics different from traditional reporting?
4. What is a data warehouse? How can data warehousing technology help in ena-
bling analytics?
5. What is predictive analytics? How can organizations employ predictive analytics?
6. What is prescriptive analytics? What kinds of problems can be solved by prescrip-
tive analytics?
7. Define modeling from the analytics perspective.
8. Is it a good idea to follow a hierarchy of descriptive and predictive analytics before
applying prescriptive analytics?
9. How can analytics aid in objective decision making?

1.9 BrieF introduCtion to Big data analytiCs
What is Big data?
Our brains work extremely quickly and are efficient and versatile in processing large
amounts of all kinds of data: images, text, sounds, smells, and video. We process all
different forms of data relatively easily. Computers, on the other hand, are still finding it
hard to keep up with the pace at which data is generated—let alone analyze it quickly.
We have the problem of Big Data. So what is Big Data? Simply put, it is data that cannot

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