This content provides a deep dive into Business Intelligence (BI), explaining its evolution, core concepts, distinction from Business Analytics (BA), industry applications, advantages, disadvantages, and strategic implementation. It emphasizes the growing importance of data-driven decision-making in the modern business landscape.
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uh welcome everybody good morning and
good afternoon good evening I am uh
Vladimir barov and I'm thrilled to guide
you through this course Deep dive into
the vital subjects in adus MBA program
business intelligence and analytics to
start a little bit about myself I'm a
three times founder with two exits under
my belt I've directed software
technology and data services Ventures
through the growth and delivery of
successful software products for over a
decade I was a co-founder in CTO of
advisor engine through eventual
acquisition of the company and a whole
Global Executive mbas from Columbia and
London Business School let's get started
with the
subject in today's rapidly
changing business landscape
organizations have faced with influx of
data and the challenge of transforming
the data into meaningful
insights questions for all of you have
you used any tools lately
that you found useful for dealing with
any kind of data and I'll give you a few
chat and please don't be shy going to
possible but it's also okay if uh people
are still waking up or about to go to
sleep so that's definitely
understandable we can uh move on to the
slide
so business intelligence commonly
referred to as bi is a technology juring
process that involves the collection
integration analysis and presentation of business
business
information the primary objective of bi
is to Aid organizations in making
informed Decisions by providing
actionable insights derived from a vast
amount of data business intelligent
tools access and analyze data sets and
present analytical findings in reports
summaries dashboards graphs s charts and
maps to provide users with detailed
intelligence about the state of the
business in this webinar we'll go over
the following anticipating business
intelligence Technologies of the future
adapting to the involving landscape of
data analysis and strategies for
Effective decision making in the era of
intelligence another question for you
all if you have woken up uh anyone can
tell me when business intellig
was first
seconds all right um the roots of
business intelligence traced back to
1960s though the term it help with
coined much later in
1989 but even before the term was
popularized the core Ence of business
intelligence existed sharing and
utilizing information for better
decision- making across organizations
in the early days business intelligence
was about improving operational
efficiency and understanding market
dynamics it entailed systems that could
assist in cating centralizing and
interpreting large volumes of data this
rudimentary systems were the precursors
to Advanced business intelligence
platforms we see today with the Advent
of the computer models in the late 20th
century particularly for decision making
the discipline began to evolve
exponentially early computer VI systems
were mostly mainframes that executed
specific tasks for large Enterprises the
primary objective was to convert vast
amounts of data into comprehensible
insights which were presented in a form
of reports by the 1990s as personal
Computing began to take root this models
further evolved becoming more
userfriendly offering Dynamic reporting
and providing users the ability to
create custom
queries initially the I tools and
process es were heavily reliant on it
departments making it a specialized
domain within
organizations with data mostly resiling
in stos databases it reli Service
Solutions were the norm but as
businesses recognize the potential of
informed decision making and the value
of data driven insights there was a
pressing demand for democratizing data
taes this led to the emergence of
self-service bi tools in the 2000s
enabling end users to derive insights
without being overly dependent on it
indeed business intelligence has been a
buzzword sometimes misused or
misunderstood however at its core bi has
always been about harnessing the power
of data for strategic and operational
advantage while a traditional bi was
more reactive often offering a
postmortem view of business events modm
bi especially post20 is proactive
predictive and in some cases descriptive
tools today not only tell businesses
what happened but also provide insights
into what might happen next or even
recommend actions Based on data
patterns bi in its contemporary form is
far more than mere data reporting it
encompasses a suite of tools
Technologies and methodologies that
allow businesses to access analyze and
visualize data and then convert this
insights into actionable
strategies This Modern iteration of bi
integrates seamlessly with Technologies
such as machine learning artificial
intelligence and big data
analytics understanding bi's reach
history and evolution helps in
appreciating its current form and
potential as the landscape of business
continues to transform the age of
digitalization bi remains a pivotal tool
that empowers businesses to stay ahead
of the curve making data driven
decisions the Cornerstone of modern Enterprises
and another question for everybody what
is the difference between business
intelligence and business
analytics I'll give you a couple minute
that all right so at first clance
business intelligence and business
analytics might seem
interchangeable both revolve around the
use of data in the business real
enhanced decision making however when
you de dive deeper into these
disciplines distinct differences
emerge let's start with business
intelligence the what and the how
business intelligence involves
leveraging software and services to
transform raw data into actionable
insights it is primarily concerned with
measuring and understanding past and present
present
performances business intelligence aims
to provide a snapshot of the current
state of affairs in the business using
various tools to collate manage and
visualize data traditional bi tools
include data warehousing data
visualization tools and
dashboards T powerbi and click view are
the quick examples of those
tools business intelligence is typically
retrospective in nature focusing on
descriptive analytics which paint a
picture of what happened and how it
happen what about business
Analytics a on the other hand is the
practice of iterative methodical
exploration of organizational data
focusing on statistical analysis it's
more about understanding the why behind
the data and forecasting future
Trends business analytics aims to
identify business Trends and patterns to
forecast future scenarios and in some
cases provide prescriptive
measures business analytics often employ
statistical tools data mining predictive
modeling and multivari testing amongst
others SAS analytics
ibmspss and python skykit learn are the
examples of that kind of
software while a business intelligent
answers what happened business analytics
dels deeper into why did it happen What
could happen in the future and how can
we make it
happen the key differences between
approaches while business intelligence
is typically backward looking assessing
past and present Data Business and
analytics is forward-looking emphasizing
prediction and
prescription business intelligence often
stops at the level of reporting and
dashboarding whereas business analytics
delves into deeper statistical and quantitative
quantitative
analysis business intelligence focuses
on structured data from specific sources
like a CRM or Erp systems and business
analytics often deals with the both
structured and unstructured data
incorporating data from external sources
social media or iot
devices in morm businesses the line
between business intelligence and
business analytics is blurring many
organizations integrate both business an
intelligence and business analytics
capabilities to create a comprehensive data
data
strategy several platforms now offer
both bi and ba functionalities allowing
businesses to seamlessly transition from
understanding current situations to
predicting future ones this combined
approach provides a holistic view from
historical data analysis to predictive
modeling ensuring more informed
decisions across the board although bi
and ba come with their unique focuses
and tools there are two sides of the
same coin in today's data driven
business landscape understanding both
and their interplay is crucial for
sustained success the key is not to pit
the against ba but to leverage both in
tend them to navigate the intricate Maze
so here's a question for you who can use
business intelligent tools what are the
individuals business intelligence is
applied differently from business to
business and across a range of sectors
Finance retail consumer goods energy
technology government education
Healthcare manufacturing and
Professional Service
here's how intelligence is being used by
different Industries to achieve
success in finance it is being used for
risk management bi tools analyze market
trends and fluctuations to predict
potential risks also for fraud detection
real-time data analysis helps in
identifying unusual transactions
reducing instances of fraud also in
portfolio analysis helps in
understanding investment TR Trends and
optimizing portfolio managements and
strategies retail and consumer
goods are helped by Inventory management
where a bi predicts inventory needs
based on sales Trends and historical
data customer insights where it analyzes
purchasing Behavior leading to better
products placements and marketing
strategies or supply chain optimization
streamlines supply chain processes by
identifying bottleneck and
efficiencies how does it help an energy
sector it helps with demand forecasting
by predicting energy demand spikes
helping an efficient energy
distribution by providing operational
efficiency monitoring equipment and
infrastructure Health in real time
reducing down times also supporting
Regulatory Compliance ensuring that
energy production and distribution
adhere to the environmental and governmental
governmental
standards in technology sector these
tools help with product development
business intelligence insights guide
Tech firms and developing product based
on market demand and
helps in enhancing software and
application usability by understanding
user pattern performance monitoring
tracks the performance of digital
Solutions in real time leading to timely
optimization Mohamed thank you very much
for helping with the education
comment how about government in
government public service enhancement bi
AIDS in optimizing Public Services like
transport waste management and water
supply budgeting provides a clear
understanding of where public funds are
required the most and in crime analysis
also helps in predicting crime hotspots
and improving Public Safety and here's
education as Muhammad
mentioned helps with student performance
analysis helps in identifying which
student might be at risk and which one
needs a specific
interventions helps operational
efficiency AIDS in optimizing resource
allocation from classroom to faculty
assignment incor clar design to
analyzing student feedback and
performance to shape course content and teaching
teaching
methodologies in healthcare it helps a
lot with patient care bi tools track
patient Health in real time leading to timely
timely
interventions research development
providing insights into disease Trends
guiding research efforts into resource
allocation ensuring optimal allocation
of resources from medical staff to
equipment in manufacturing it helps with
Supply Chain management business
intelligent tools offers detailed view
of supply chain from raw material
procurement to product delivery quality
control analyzing product data to ensure
product quality and standards predictive
maintenance predicts when Machinery
might fail or require maintenance
reducing down
times across different sectors business
intelligence plays a pivotal role in
driving efficiency optimizing resources
and ensuring better decision- making
while the core principles of bi remain
consistent its applications vary
ensuring that each industry benefits
from tailored insights and strategies as
the business landscape continues to
evolve so will the innovative ways in
sectors all right let's talk about
advantages and
disadvantages we've covered a lot of
pros of bi but as with any major
business decision implementing bi comes
with some difficulties and disadvantages
disadvantages of business
intelligence some could be complex
implementation deploying business
intelligence systems especially larger
organizations can be complex and timec
consuming it may disrupt existing
workflows and require substantial change
management efforts it could be costly
and resource intensive initial setup
licensing and training cost can be
significant ongoing maintenance and
necessary upgrades also add to the
cost it also could be a potential for Mis
Mis
representation business intelligent
tools provide data but interpretation
relies on the user incorrect
interpretation can lead to misguided
decisions it also comes with data
privacy concerns storing and analyzing
sense of business data especially
customer data introduces privacy
concerns and potential regulatory
implications it has Reliance on quality
data input a business elligence system
is only as good as the data fed into it
if the input data is flawed or biased
the insights derived can be
misleading it also has potential for
overreliance there is a risk the
business might over rely on BI tools and
undervalue human intuition and
experience there are some integration
challenges too integrating bi tools with
existing systems like CRM or ARP can
sometimes poose challenges leading to
data stylus and compatibility issues
training and skill requirements ments
employees need training to use bi tools
effectively without proper skills the
underutilized business intelligence
helps business decision makers to get
information they need to make informed
decisions but the benefit of business
intelligence extend beyond business
decision making according to data
visualization vendor Tableau including
the following helps with data to driven
business decisions the ability to drive
business decisions with data in central
benefit of bi as trbi strategy can
deliver accurate data and Reporting
capabilities faster to business users to
help them make better decision making in
more time fashion it helps with faster
analysis and intuitive dashboards
business intelligence improves reporting
efficiency by condensing reports into
dashboards that are easy for
non-technical users to analyze saving
them time than seeking to glean insights from
from
data it helps with increased
organizational efficiency business
intell elligence can help provide
holistic views of business operations
giving leaders the ability to Benchmark
results against larger organizational
goals and identify areas of
opportunity has improved customer
experience ready access to data can help
employees charged with customer
satisfaction provide better experiences
it helps with improved employee
satisfaction providing business users
access to data without having to contact
analysts or it can reduce friction
increase productivity and facilitate faster
faster
results trusted and govern data modern
bi platforms can combine internal
databases with external data sources
into single data warehouse allowing
departments across an organization to
access the same data at one
time it also creates increased
competitive Advantage a sound business
intelligence strategy can help
businesses monitor their changing market
and anticipate customer needs business
analytics and business intelligence
serve similar purpose and are often used
as interchangeable terms but bi should
be considered a subset of business
analytics business intelligence is
descriptive telling you what's happening
now and what happened in the past to get
your organization to that
state where are sales prospects in the
pipeline today how many members have you
lost or gained by this month business
analytics on the other hand is
predictive what's going to happen in the
future and prescriptive what should the
organization be doing to create better
outcomes this gets to the heart of the
question of who business intelligence is
for bi aims to deliver straightforward
snapshots of current state of affairs to
business managers while the predictions
and advice derived from a business
analytics require data science
professionals to analyze and interpret
one of the goost FBI is that it should
be easy for the nontechnical users to
understand and even to dive into the
data and create new reports while a
business intelligence offers
transformative potential for the
businesses it's it's essential to
approach its adoption and usage with
clear understanding of both its
advantages and disadvantages this
balanced perspective ensures that
businesses can maximize the benefits of
business intelligence while mitigating potential
pitfalls let's talk about the strategy
and how can you implement this in
today's Dynamic business landscape
datadriven decisions is crucial a well
defined business intelligence strategy
not only provides Clarity on data usage
but also ensures alignment of data
initiatives with broa business
context let's understand the business
context first what are the organization
objectives Begin by understanding the
company's overarching goals business
intelligence initiatives could be
directly supporting this objectives what
are the stakeholder needs engage with
the key stakeholders to GA requirements
and understand the specific data needs
of different
departments then you have to conduct the
data assessment create a data inventory
catalog existing data sources identify
what data is available where is it
coming from and its
relevance conduct data quality
assessment scrutinize the data sources
for consistency accuracy and
completeness determine which data sets
can be trusted and which need cleaning and
and
enhancing next step is technology and Tool
Tool
evaluation what is car
infrastructure assess current bi tools
and Technologies in use are they
adequate or are the gaps do some future
proofing one considering new bi tools
ensure that they are scalable and can
needs and Next Step develop a data governments
governments
framework develop a framework for data
ownership Define who within the
organization owns different data sets
this streamlines responsibility and
accountability analyze data access and
security determine who has access to
what data Implement security measures to
information then perform training and
culture integration you have to do
skills assessment identify if there a
skills Gap in organization regarding VI
tools utilization are there any training
programs should there some be set up
Implement training sessions to ensure
staff can use bi tools
effectively is there a need for a
cultural shift promote a datadriven
culture encourage departments to base
intuition and then we proceed with
implementation and deployment it should
start with pilot testing before a full
scale deployment test a bi Solution on a
smaller scale to identify potential
issues create a feedback loop regularly
gather feedback from users to refine an
OP optimize the bi
apologies Implement continuous review
and evolution there should be
performance metrics determine kpis to
measure and success of the bi strategy
this could include metrics like data
accuracy user adaption rates and
decision-making speed adapt an iterative
approach as the business evolves the AI
strategy should too periodically review
and update the strategy to ensure
alignment with business objectives
stakeholder Communications make sure to
do regular reporting keep stakeholders
informed with regular reports showcasing
bi insights and
impacts change management if changes are
made to the bi strategy or tools ensure
smooth communication transition
processes minimize
disruptions question for audience and
maybe Mohamed can help me again
have you had it to create a bi strategy
for your company and just yes or no qu
fine all right guys uh still getting
that coffee okay uh developing a robust
vs strategy is not a one-time task but
an ongoing process a depend a blend of
technical Acumen or organizational
understanding and strategic foresight
with the right strategy in place
businesses can harness the full power of
their data leading to informed decision-
making enhanced efficiency and
Advantage all right let's jump into some
examples here um business intelligence
systems and tools play a central role in
modern organizations turning row data
into actionable insights the spectrum of
bi tools is fast evolving in response to
increasing data complexities and the
need for deeper analysis let's take a
look at the systems and
tools for data
warehouse which are Central repositories
that store integrated data from one or
more disparate sources
sources
usage facilitates efficiency quering and
Reporting provides a unified and
consistent data source for bi activities
some of this examples logos mentioned
here so it's Amazon R shift Terra data
Google big
query and Muhammad thank you again for
participation appreciate your
answer and next slide all right what
about business intelligence systems and
tools these are the tools that convert
complex data into visual
graphs charts and maps and examples
again are here are Tableau click View
powerbi some other examples here we have
uh Crystal Reports IBM cognos SSRS which
Services all a little bit more so data
Discovery tools uh platforms that enable
users to dive into data sets and extract
meaningful insights using quering data
prep and
visualization and us used for
identifying patterns correlations and
anomalies often user friendly and Design
for those without a deep statistical
background an example of those are Alx
fire uh we get some data mining tools uh
these tools are used to analyze vast
data sets and extract patterns
correlations and other relevant
information useful for Predictive
Analytics helps in identifying Trends
anomalies and opportunities and some
examples here are IBM SPSS modeler rapid
Miner SAS data
data mining
mining
predictive Predictive Analytics tools
these are systems that use statistical
algorithms and machine learning
techniques to identify the likelihood of
future outcomes provide Foreside by
forecasting Trends and behaviors aiding
proactive decision making and some of
the examples here are SE Predictive
Analytics SAS Predictive Analytics and
Analytics then we have online analytical processing
processing
tools these are tools allow
multi-dimensional data from multiple
perspectives and available
online suitable for complex queries and
calculations support realtime business
modeling we have Oracle olap Microsoft
tm1 some of the example of ETL tools uh
it's extract Transformer load solutions
that facilitate the three stage process
of extracting data from Source systems
transforming it to fit operational needs
and loading into Data warehouses an
examples here are Talent Informatica and SQL
SQL
Server all right we got to close to the
end of this presentation and we'll be
delighted to jump into Q&A just shortly
I wanted to thank everybody here for
your active participation especially
Muhammad and engaging insights and I
hope that you found this chords Deep
dive both informative and inspiring and
if there are any questions and thoughts
please feel free to share them as we
session and we have a com and
foremost we like to extend our deepest
gratitude for taking the time to share
your uh insights and expert with us
today Vladimir thank you again for your
time your knowledge and perspective
added the immersive value to the
discussion and thank you to all the
adenes uh thank you for being engaging
and your participation and hopefully the
questions you have for our guest speaker
uh will enrich this session uh we we
understand how precious time is and we
are honored that you've chose to spend a
part of your day with us we can move on
I think with the Q&A next I
believe yep that is correct go to the
next slide
um okay uh before we start with Q&A as
you think of any questions for our guest
speaker uh just a reminder this webinar
will be available on our social media
channels on Instagram LinkedIn Twitter
uh every platform out there
you will find this webinar recording
within a week from today so stay
connected with us uh to uh to view this
webinar as well as any upcoming event we
have um okay I think now we can wait a
second or a minute or so to see if have any
see there's a question um from from your
experience where do you see business
intelligence heading in the next five
years that's a that's a really good
question um we have a lot of tools in
the Horizon especially the artificial
intelligence tools which give enormous
power to business owners to analyze
incredible volumes of data and produce
something close akin to human
intuition based on that data um a lot of
the tools that we mentioning here at
some level are supported by Machine
learning and artificial intelligence but
many of them have not yet
Incorporated uh llm tools um or anything
from chpt or power of being
uh that allows us to look much more
ahead using all of human knowledge and
not just our local
business so my my vision for the
evolvement of the tools is a greater
integration of the AI techniques LM
techniques specifically to analyze the
data and give much more accurate and
human friendly human rable projections
that could be incorporated into
businesses I hope that answers your question
question thank
thank
questions um so what do you feel about
companies that avoid using artificial
intelligence in general like AI adapting
the future I see um that's that's a very
interesting question um I think some of
the companies are still cautious uh with
the rich of the companies that share
their algorithms for greater public um
it's the concern of data privacy um it's
the concerns of how that data is going
to be used later on whether models are
going to be developed based of that data
a lot of businesses are cautious about
revealing their
own proprietary information to third
parties uh in order to uh for the third
par is to gain advantage that's one of the
the
reasons U number two reasons uh it's
also comes at a huge cost if you decide
to build your own internal models not
only you require a lot of data but you
also have to have a lot of supporting
staff and Hardware in order to process
that data and generate
insight and number three even though it
seems like almost a
magic that we when we look at results
produced by artificial intelligence it's
hard yet to understand when that magic
fails and we don't have tools right now
to uh verify the the completeness and
validity and logic of the answer um and
I think a lot of businesses who want to
maintain their reputation uh vary about
stills
thank you very much uh I think a
followup question is how can we
Implement bi or AI uh and companies that
change as I think a proverb says that uh
even water can change Stone but over a
very long time um I would recommend uh
setting up uh very time sensitive budget
when that project is undertaken but also
to analyze what are the components of
the culture uh in that organization
because it's not absolutely necessary
that the whole organization is not
interested to do the shift it could be
just specific individuals and either
assisting those individuals to cross the
Gap the knowledge Gap uh the risk Gap
the cost Gap uh will enable the whole
organization to follow along and as one
of the slides mentioned uh some
additional training might be required um
in order for the rest of the
organization in order to jump on board
approaches great thank
you think we will wait another minute so
Yeah question um what are the
implications of not having a
comprehensive bi strategy in today's
business world and like the potential
impact on the organizational
success absolutely that's a that's a
great question um I think the only
businesses that can be allowed these
days not to have IBI strategies lemonade
STS with a very uh small uh Revenue uh
with a very small audience uh and client
base but as soon as you start getting to
real level businesses with millions and
billions dollars of Revenue it's
impossible to make Next Level decisions
without thoroughly analyzing your data
for what you have now and then applying
those forecast models that you can
create through uh business um analytics
to understand where it can take you and
create scenario planning tools and if
all those tools are not being used the
business is probably going to be not a
going concern very shortly as other
businesses which do will have a serious competitive
Advantage thank you very much
if
uh we have a question what are the steps
to Implement Company bi
analytics that's that's a good question
um first of
all there has to be some initial
analysis of what
is what are you trying to accomplish
with implementation of that strategy
strategy
and after those goals have been defined
then you can map the tools and
approaches from the slides that I've
mentioned here or other resources on the
internet where you can select approaches
to solve those goals so for example if
you have a business analysis uh need for
a large data set then you go again
through the slid and identify tools like
um the IBM tools assess tools provided
that can go inside the data set and
understand what the current state of the
data is or current state of the client's
markets or
revenues uh and then if let's say your
problem is not just going through the
data set but creating analytics on top
of that and you have a need to provide a
regular report to the board of what we
anticipate things to be in the next
couple months couple quarters couple
years this is when the business
analytics tools come in and where you
can use those on the data set provided
by business intelligence tools to
outcomes and I see the next question is
uh is bi similar to Ai and how can they
both implemented in the healthc care system
system
um there are various definitions of what
AI is um a simple regressive algorithm
perhaps will not be but it's we're still
talking about a certain model which has
inputs and outputs and you have an
algorithm in between and either that
algorithm is known like in the case of
uh General business intelligence or
sometimes unknown because AI arrives to
it through the data training process uh
and those are the key differences um
both of them can be implemented
depending again for the use case
depending on amount of data depending on
how accurate your output has to be um
those would be the consideration and
all right now wake wak waking up okay
multiple questions coming in so we have
a question from sham
sham
Dr sham um what are the positive and
negative impacts of Chad GPT and related
technology on BI will Chad GPT minimize
the career opportunities of bi
bi
um I have my opinions but uh I don't
know how far I can predict things
because Chad dut just came out in March
it obviously a very strong product with
a lot of potential but as I mentioned
before there's still a lot of unresolved
questions when it comes to data privacy
and accuracy of the answers um I think
we need to look at chat GPT as a
supplementary tool and not necessarily
the primary tool driving our business
intelligence um uh analysis because in
the end we still need individuals on the
other side to certify the answers that
the tool provides that they're actually
accurate um as I mentioned before tread
Gutt is a model which has inputs and has
outputs but the algorithm in the middle
is still a bit of a dark mystery for a
lot of practitioners uh who cannot
certify with certainty for all the types
of outputs what inputs what kind of
outputs will be so I think a lot of
classical bi tools will be around with
us for a while uh to double verify the
answers of chat GPT so I would say for
the near future uh there probably not a
lot of impactful uh results that will
jobs all right next question how can
organizations from Sicilia
uh how can organizations manage and
protect data used in bi um I think we're
talking about now about data strategies
uh which is a little bit uh outside of
the topic of this uh presentation but
I'll still dive into it um some of
things that could be used uh is
encryption it's a backup multi- Reginal
um it could be used limited uh technique
of limited access to the data um and
also analizing all the data which uh is
used in business intelligent analysis
because in the end you're only looking
for statistics and not necessarily the
private data um some of the other
techniques to verify the stability of
the data through cyber security incident
response training could be performed to
analyze the gaps um in the systems that
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