Generative AI Transforming Business Intelligence Adoption
Key Points
- Business Intelligence (BI) transforms raw data into actionable insights through a workflow that includes data collection, preparation, analysis, and presentation.
- The three core BI personas are data engineers (who clean and ready data), BI analysts (who build reports, dashboards, and answer ad‑hoc questions), and business users (who consume and interact with those visualizations).
- Despite massive investments in data tools, only about 35% of business users regularly use analytics for decision‑making, a stagnant figure over the past seven years.
- Adoption is hampered by three main challenges: complex and manual data preparation bottlenecks, limited self‑serve capabilities that still require deep knowledge of business logic and metrics, and the steep learning curve for non‑technical users.
Full Transcript
# Generative AI Transforming Business Intelligence Adoption **Source:** [https://www.youtube.com/watch?v=io6JqPG80WU](https://www.youtube.com/watch?v=io6JqPG80WU) **Duration:** 00:08:34 ## Summary - Business Intelligence (BI) transforms raw data into actionable insights through a workflow that includes data collection, preparation, analysis, and presentation. - The three core BI personas are data engineers (who clean and ready data), BI analysts (who build reports, dashboards, and answer ad‑hoc questions), and business users (who consume and interact with those visualizations). - Despite massive investments in data tools, only about 35% of business users regularly use analytics for decision‑making, a stagnant figure over the past seven years. - Adoption is hampered by three main challenges: complex and manual data preparation bottlenecks, limited self‑serve capabilities that still require deep knowledge of business logic and metrics, and the steep learning curve for non‑technical users. ## Sections - [00:00:00](https://www.youtube.com/watch?v=io6JqPG80WU&t=0s) **Core Personas in Business Intelligence** - The speaker defines business intelligence and outlines the three primary roles—data engineer, BI analyst, and business user—that handle data preparation, analysis, and consumption before addressing generative AI’s impact. ## Full Transcript
all right so let's talk about the impact
of generative AI on business
intelligence or bi but first let's
understand what bi is bi typically
refers to the practices and the
processes that organizations use to
collect prepare analyze and present data
and insights to facilitate decision
making
now the entire point of business
intelligence is to take your raw data
and to convert that into an actionable
piece of insight now organizations may
use one or multiple bi tools to
accomplish this task now from a roles
and personas perspective all the way
from data integration to making a
decision there are multiple roles and
personas involved but for the purpose of
this discussion let's keep it focused on
three key or core personas
so let's talk about
bi today
and start with
the data stored
or the data engineer what they typically
do is they they they clean collect
transform and prepare the data for
analytics
and once the data is clean and prepared
next comes your bi n list
they would typically take that clean and
prepared data they would analyze it
and then build reports and dashboards
with that information they would also
answer various ad hoc questions from the
line of business and the bi analyst
would typically work closely with the
line of business users to understand
their wants and needs and then build the
reports and dashboards accordingly
and then you have your lineup business
user
and they typically
consume these reports and dashboards
prepared by the bi analyst and while
consuming those reports and dashboards
they may interact with it by slicing and
dicing the data Drilling in drilling
through adding filters and so on but for
the most part they are just consuming
the information that's contained in
these reports or dashboards
now what's important to note is over the
last few years a lot of ba venders have
been including lots of no code self- Ser
capabilities that allows line of
business users to build reports and
dashboards and visualizations themselves
having said that despite all of this
Innovation though
we have an adoption problem despite 97%
of companies investing over $32 billion
by 2027 in data and a only 35% of lineup
business users use data and analytics
for decision- making
now this 35% adoption number hasn't
moved in over 7 years and it hasn't
moved mainly for three reasons first is
that data prep is complex it's tedious
it's manual requires specialized skills
and as a result creates a major
bottleneck
and the second reason is the self- serve
today is quite limited right so I
mentioned previously that line of
business users could through no code
capabilities build their own reports and
dashboards today but to be successful at
this you still have to understand the
underlying business logic metric
definitions kpi definitions this is
really hard right and the learning curve
is steep for a LED business user to come
in and use one of these no code self-
serve tools and the other important fact
here is a lot of lineup business users
aren't really interested in the
analytics themselves and what I mean by
that is although they're interested in
the final recommendation or the final
piece of insight they're not necessarily
interested in creating reports
dashboards working with data manually
interpreting those reports and
dashboards they want none of that right
they just want to abstract away the
noise of the analytics and get to the
final piece of insight or the
recommendation
and the third Factor here is a Chasm
that exists between data and insights
even if you were to receive the best
report or dashboard prepared by a bi
analyst as a line of business user I
still have to manually interpret that
report to understand what happened then
understand why something happened what
will happen and what I can do about it
so lots of manual steps with lots of
humans in the loop lots of
inefficiencies and again drives that
adoption Gap
however we are at an inflection point
thanks to generative AI for the first
time in decades we have an opportunity
to move this 35% adoption number upwards
of 50%
and we're going to do that by optimizing
or augmenting the experiences for the
three roles that we spoke of previously
so let's start with the line of business
user and understand how gen is going to
augment their experience
first geni is going to allow lineup
business users to
talk to their data
what I mean by this is that as a line of
business user I could come in ask a
question in my everyday language to the
system and then generative AI would take
that question understand the intent
behind it understand the right data
sources I need to bring in understand
the right data query I need to perform
bring in the right statistical analysis
and then finally relay back an answer in
a format that easily digestible so in
natural language and visualizations
now as a result the Reliance on
predefined reports and dashboards is
going to diminish for lineup business
users and there's going to be a shift in
analytic power from the VA analyst to
the line of business users now as a
result again 90%
of data consumers will now become
content cre creators
so that is the impact of gen on line and
business users
for bi analysts
gen is going to help you
optimize
report
authoring so web generative AI you'll be
able to do things like automatically
generate code generate SQL automatically
build reports or dashboards build
visualizations edit them all through
natural language and the other important
piece here is that since line of
business users are going to rely Less on
the bi analysts they're going to have a
lot of free of time to focus on
higher value tasks
an example of this could be a bi analyst
now taking that X time to document the
nuances or the knowledge of the business
into the semantic layer or the data
layer or focusing on more complex pieces
of insight or analysis
and then finally you have
your data stored
we a data engineer and there's a similar
story here as well you'll be able to
optimize
various data engineering tasks so You'
be able to do things like automated code
generation optimize data pipelines um do
perform automated data profiling data
cleaning and semantic enrichment and so
and this is essentially what
bi plus gen AI
will bring to the table now of course
when the line of business user is better
self- serving themselves asking
questions from their data getting
insights from the system
that frees up a lot of time for the data
stored and the bi analysts to focus on
higher value tasks like documenting the
knowledge of the business into the
semantic layer or the data layer this
creates a virtuous cycle which will
ultimately move that adoption number
from the 35% that it has stagnated for a
long time at to upwards of 50% Thank you
very much
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