2. Business analytics (BA)
n Business analytics (BA) is the practice of
iterative, methodical exploration of an
organization's data, with an emphasis on
n Business analytics is used by companies
committed to data-driven decision-making.
n BA is used to gain insights that inform business
decisions and can be used to automate and
optimize business processes.
3. Business analytics examples
n Business analytics techniques break down into
two main areas.
n The first is basic business intelligence.
n The second area of business analytics involves
deeper statistical analysis.
n True data science involves more custom coding
and more open-ended questions. Data scientists
generally don't set out to solve a specific
question, as most business analysts do.
4. The Differences Between Business Intelligence and
What is Business Intelligence?
Business Intelligence is analyzing
organizational raw data to support
What is Business Analytics?
Business Analytics uses quantitative
data for predictive modeling and
Ø Business Intelligence involves the
process of collecting data from all
sources and preparing it for
Ø Business Analytics, on the other hand,
is the analysis of the answers
provided by Business Intelligence.
Ø I t i s m o r e o f a f i r s t s t e p f o r
companies to take when they need
the ability to make data-driven
Ø Business Analytics, in contrast,
includes statistical and quantitative
analysis, data mining, predictive
modeling, and multivariate testing.
Business Intelligence (BI) and Business Analytics (BA) are similar, though
they are not exactly the same.
5. Business Intelligence and Advance Analytics
Business Intelligence Advanced Analytics
• What happened?
• How many?
• Why did it happen?
• Will it happen again?
• What will happen if we change X?
• What else does the data tell us
that we never thought to ask?
Includes: • Reporting (KPIs, metrics)
• Automated monitoring and
• Ad hoc query
• Operational and real-time BI
• Statictical or quantitative
• Data mining
• Predictive modelling
• Multivariate Testing
• Big data analytics
• Text analytics
6. Challenges with Business Analytics
n Executive Ownership – Business Analytics requires buy-in from senior
leadership and a clear corporate strategy for integrating predictive models
n IT Involvement – Technology infrastructure and tools must be able to handle
the data and Business Analytics processes
n Available Production Data vs. Cleansed Modeling Data –
Watch for technology infrastructure that restrict available data for historical
modeling, and know the difference between historical data for model development
and real-time data in production
n Project Management Office (PMO) – The correct project
management structure must be in place in order to implement predictive models
and adopt an agile approach
n End user Involvement and Buy-In – End users should be involved in
adopting Business Analytics and have a stake in the predictive model
n Change Management – Organizations should be prepared for the
changes that Business Analytics bring to current business and technology
n Explainability vs. the “Perfect Lift” – Balance building precise
statistical models with being able to explain the model and how it will produce
7. Business Analytics Best Practices
Ø Know the objective for using Business Analytics.
Define your business use case and the goal ahead
Ø Define your criteria for success and failure.
Ø Select your methodology and be sure you know the
data and relevant internal and external factors
Ø Validate models using your predefined success and
Data Analysis and
Business Intelligence (BI) is:
“The processes, technologies and tools needed to turn data
into information and information into knowledge and
knowledge into plans that drive profitable business action. BI
encompasses data warehousing, business analytics and
The Data Warehouse Institute, Q4/2002
Business Intelligence is defined as "knowledge gained about
a business through the use of various hardware/software
technologies which enable organizations to turn data into
Data Management Review
What is Business Intelligence?
15. Information Delivery
Ad hoc report/query
Microsoft Office integration
Search-based data discovery
Geospatial and location intelligence
Embedded advanced analytics
Online analytical processing (OLAP)
BI infrastructure and administration
Business user data mashup and modeling
Development toolsEmbeddable analytics
Support for big data sources
BI and analytics
17. Social Media and Big Data
Big data is all the huge unstructured
data generated from social networks
from a wide array of sources ranging
from Social media ‘Likes’, ‘Tweets,
blog posts, comments, videos and
forum messages etc.
• Just to give you some information, Google in any given day
processes about 24 petabytes of data.
• Social networks have a geometric growth pattern.
• Big data technologies and applications should have the ability to
scale and analyze this large unstructured data.
• Social media conversations generate lot of context to the
• For your information, most of the data is not arranged in rows
18. Applications of Big Data in Social Media
n The use of big data in social media can be a game changer if you have the
ability to design consumer preferences that will attract clients and lead to sales.
n There are several ways you can apply big data in social media to achieve
Personality Insights : Using big data in social media can help analyze personality
attributes from posts like emails and social posts so that you can obtain the right
insights about people.
Promotion :Targeting your customers that are most likely to purchase your product
can be another useful outcome through the use of big data.
Placement : Another use of big data is to find right channel for your products. From
there you can set the right supply changes and even change placement if
Product : When you need the right insights of your product, it is advisable to use
big data in social media. You will conduct a qualitative and quantitative online
market research about your product.
19. The challenges
n The challenges are very many. They can be divided into two types.
n Firstly, the very notion and traditional thinking processes on data capture,
processing and storage needs to change. This is incremental and a gradual
process. Mining such huge data requires data mining technologies such as data
mining grid and Map reduce infrastructure such as Hadoop. The technology might
not be cost effective and the learning curve is steep. It also requires a non-linear,
non-deterministic software architecture.
n Secondly, the well-known adage ‘What we measure is what we manage’
stands quite tall here. That means, we need to know as an organization what we
want to measure for the day. It is important that we understand clearly what we are
looking for. If we want to identify trend patterns for the day and predict a path
where a social conversation might lead to, then need to know ‘when to ask the
question’. This is quite difficult as events are dynamic.
20. Social Media analytics
n Social media analytics is the practice of
gathering data from social media websites
and analyzing that data using social media
analytics tools to make business decisions.
n The most common use of social media
analytics is to mine customer sentiment to
support marketing and customer service
21. The Amazing Ways Instagram Uses Big
Data And Artificial Intelligence
n Instagram, the social networking app for sharing photos and
videos, launched in 2010. Today, it boasts 800 million monthly
active users and is owned by Facebook. There are 70 million
photos uploaded to Instagram every day.
n Here are some ways Instagram uses big data and artificial
n Explore Page and Search Function : Via the use of tags and
trending information, Instagram users are able to find photos for a
particular activity, topic or event or discover experiences, restaurants and
places around the world that are trending.
n Target Advertising : In order to make the data that Instagram collects
valuable, it must extract customer insights from it. Instagram can sell
advertising to companies who want to reach that particular customer
profile and who might be most interested in receiving a particular
22. n Enhance the User Experience: In order to ensure users find value in
the platform, it’s important for Instagram to show them what they will like.
As the amount of content grows, finding content that each user will find
relevant becomes exponentially more challenging.
n Filter Spam: Instagram uses artificial intelligence to fight spam. The
spam filter is able to remove fake messages from accounts written in nine
languages including English, Chinese, Russian, Arabic and more. Once
messages are detected they are automatically removed.
n Fight Cyberbullying and Delete Offensive Comments: In a
survey conducted by Ditch the Label, 42% of more than 10,000 UK youth
between ages 12 and 25 reported Instagram was the platform where they
were most bullied. With this unfortunate distinction of having the biggest
cyberbullying problem of any social media site, they became the first to
use machine learning to automatically remove offensive posts, whereas
Facebook and Twitter rely on users to report abusive language.
n Study the Human Condition: In one study, 100 million Instagram
photos were used to learn global clothing patterns. This work showed the
potential for machine learning to help extract insights when studying
humans and social, economic and cultural factors around the world.