Using the AIMS Grid for Project Planning and Data Discovery in a Data Analytics Initiative

Name: Power BI Project For Beginners | Sales Insights Data Analysis Project - 2 - Data Discovery
Uploaded: 2026-01-25T18:25:35.579195+00:00
Channel: codebasics
Description: Summary and key takeaways on Using the AIMS Grid for Project Planning and Data Discovery in a Data Analytics Initiative, covering Introduction In the video

codebasics

Jan 25, 2026

•

3 min read

YouTube video ID: 9QiZ0-HZG_A

Source: YouTube video by codebasics — Watch original video

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Introduction

In the video, Bhavin Patel, a senior director at AtliQ Hardware, walks through how to plan a data analytics project using the AIMS Grid and outlines the steps for data discovery, integration, and dashboard creation.

1. The AIMS Grid Framework

The AIMS Grid is a four‑component project‑management tool: - Purpose – Identify the exact pain point (e.g., high manual effort merging Excel files). - Stakeholders – List all parties involved: Marketing, Sales, IT (the "Falcons" software team), and the Data Analytics team (the "Data Masters"). - End Result – Define a concrete deliverable, such as a Power BI dashboard that provides real‑time sales metrics for regional managers. - Success Criteria – Set measurable outcomes, e.g., 10 % cost reduction, elimination of manual Excel merges, and a five‑person productivity gain.

2. Assembling the Team

IT Team (Falcons): Owns the sales‑management system and its MySQL database.
Data Analytics Team (Data Masters): Requests access to the MySQL source, performs transformations, and builds the Power BI visualisation.
Business Users: Marketing and Sales teams who will consume the dashboard.

3. Data Source Considerations

The sales‑management system records every transaction in a MySQL OLTP database.
Direct querying from Power BI can degrade the OLTP system, risking sales operations.

4. Introducing a Data Warehouse (Optional but Recommended)

Extract‑Transform‑Load (ETL)
Pull data from MySQL.
Transform it (currency conversion, column pruning, data cleaning).
Load it into a dedicated warehouse (e.g., Snowflake, Teradata).
Benefits
Keeps the production database untouched.
Provides a schema optimized for analytical queries.
Allows use of ETL tools like Apache NiFi, Informatica, or Python‑pandas.

In the example, the team skips the warehouse for simplicity and connects Power BI directly to a clean SQL database.

5. Building the Power BI Dashboard

Connect Power BI to the chosen SQL source.
Perform data wrangling (cleaning, merging) inside Power BI – the next video will cover this in detail.
Design visualisations that answer the business questions defined in the AIMS Grid.

6. Measuring Success

After deployment, verify that: - Operational costs have dropped by at least 10 %. - Manual Excel‑merge effort is eliminated. - The dashboard is actively used for at least three months and meets the productivity targets.

7. Next Steps

The upcoming video will dive into data cleaning, merging, and wrangling within Power BI, showing that even users with no prior Power BI experience can quickly become proficient.

Key Takeaways

The AIMS Grid forces clarity on purpose, participants, deliverables, and metrics.
Protect the OLTP system by using a data warehouse or a read‑only replica for analytics.
Power BI can serve as both an ETL engine and a visualisation layer, making the whole pipeline accessible to non‑technical business users.

By applying the AIMS Grid, clearly defining stakeholders, success metrics, and a safe data architecture, organizations can turn messy Excel‑based reporting into automated, real‑time Power BI dashboards that cut costs and boost productivity.

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Hardware, walks through how to plan

data analytics project using the AIMS Grid and outlines the steps for data discovery, integration, and dashboard creation.

Helpful resources related to this video

If you want to practice or explore the concepts discussed in the video, these commonly used tools may help.

Power Bi For Data Analysts Book Recommended

Provides step‑by‑step guidance on building Power BI dashboards, helping teams implement the visualisation part of the AIMS Grid quickly

Amazon →

Snowflake Data Warehouse Guide Book

Explains how to set up and use Snowflake as a modern data warehouse, supporting the ETL and performance considerations discussed

Amazon →

Data Warehouse Design Book

Covers best practices for designing scalable warehouses, ensuring analytics workloads don’t impact production systems

Amazon →

Links may be affiliate links. We only include resources that are genuinely relevant to the topic.

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Full Transcript YouTube

in last video we looked at problem
statement in this video we are going to 
look at project planning using AIMS grid
and data discovery after bhavin patel 
who is a sense director of at lake
hardware realizes the pains and the 
possible solution
he will call a couple of people like an 
IT head or a head of data department and
he will should live a meeting on how 
exactly we should do this data analytics
project and after those people are in 
the room together they will brainstorm
on project planning using AIMS grid now 
what is AIMS grid AIMS grid is a project
management tool and it has four 
components to it the first component is
purpose so first we want to determine 
what is our pain point what do we want
to do exactly the second component is 
the stakeholders so in this project who
all will be involved so here of course 
the marketing team the sales team who is
facing these issues will be involved the 
IT team will be involved so the IT team
is basically the team of software 
engineers that AtliQ hardware has and
the data analytics teams will also be 
involved so we are assuming that AtliQ
hardware or already has a data analysis 
team in-house they also have an in-house
team of software engineers usually some 
companies don't keep this team in-house
in in that case they will outsource 
their work to some service based
companies such as Accenture enforces TCS 
etcetera so if you want to assume that
this project is exported to those 
companies then those service based
companies will manage the whole thing 
but here for our project we are assuming
that all these teams are available 
within AtliQ hardware the third
component to Ames grid is the end result 
after the project is over what do you
want to achieve so we need to define 
that very clearly it cannot be a vague
statement that I want to generate sales 
inside in our case the end product
be a power bi dashboard something that 
our sales director or even Regional
Managers can go and look into and it 
gives you a real-time information on
your sales numbers the fourth component 
would be the success criteria after the
project is over unless that you have 
spent few million dollars how do you
define that the project was successful 
AIMS grid allows you to do that by
exactly specifying your success criteria 
so in this case our criteria would be we
want the cost to go down by 10% also 
until now the sales team and Regional
Managers have been giving this 
information in Excel file sometimes if
Regional Manager wants the information 
in more precise way there will be couple
of people who will be manually working 
on merging these Excel files you know so
there is lot of time that is being 
wasted today so we want to save our
business time and cost on those 
operations there are many BPO companies
today that exist and all those companies 
do is they will get couple of Excel
files they will do merging they will do 
some Excel formulas you know they do lot
of manual work so many organizations end 
up spending a lot of money on these
manual works and AtliQ hardware is one 
of them so we want to reduce cost on
that front and one other success 
criteria could be we want to bump up
ourselves by let's say five person so 
after we do all this hard work build a
dashboard we want to use it for three 
months and we want to make sure that we
have achieved all these results defined 
by success criteria so the AIMS grid is
an extremely useful tool for any project 
managers and leaders which can help you
Mon manage the project in an effective 
way for AIMS grid I have this nice
youtube video where this person explains 
the sames grid in a very simple manner
so I would suggest that you watch this 
video it will be
helpful in terms of improving your 
project management skills so now you
have defined AIMS grid you know exactly 
what needs to happen the then self
director Wilke will call the data 
analyst team and he will start
discussing that how we can solve this 
problem he will also call the IT team
now just to be fancy I have given this 
fancy name to our IT team let's call
them Falcons so Falcons here is a team 
of software engineers who own the sales
management system so AtliQ hardware 
has this sales management system which
is just a simple software that is a 
keeping track of all the sales number so
whenever they sell any computer or any 
hard days in any local region this
software is printing the invoice so it 
has all the records stored in a MySQL
database and this database and this 
application is or owned by this Falcons
team over data analyst team is called 
data masters so the data masters will
reach out to Falcons and say hey we want 
to use your SQL database because this is
the database which has all the records 
that we need for our analytics and what
we'll do is we'll integrate MySQL 
basically we will use MySQL as a source
in our power bi tool and we will build 
dashboard on top of it and Falcone's
don't mind if I can say yeah go ahead 
and this is the approach we are going to
use now in majority of the time when the 
data volume is high you want to make
sure that your MySQL database is not 
affected by the queries that you are
doing in your power bi so sometimes what 
people do is not sometimes actually many
many times they will design a data 
warehouse okay so what is data warehouse
so data 
warehouse is basically you take the data
from MySQL which is also known as OLTP 
which is online transaction processing
system it is a very critical system you 
cannot afford for that system to go down
otherwise your regular sales operations 
and gets hampered right for you that
means business is very important 
analytics is important but then it's a
secondary thing so what people do is 
they pull the data from OLTP which is
MySQL in our case they do all the 
transformation which is also called ETL
which means extract transform and load 
and after doing that transformation they
store the data in a data warehouse 
there are many data warehouse such as
Tara data or snowflake and so on and for 
doing this transformation people use
tools such as Apache knife I tell end or 
even Python and pandas by doing this
transformation you are reformatting the 
data in a way that is best for
performing analytical analytical queries 
because if you perform your query
directly on MySQL database there are two 
problems one is if you cause that
database to slow down then your 
mainstream business is affected
second problem is the data stored in the 
OLTP is not often in a format that you
want so you want to do lot of 
transformation such as currency
conversation you want to remove the 
columns which are not needed there could
be n number of transformation that you 
want to do and all of that happens in
the ETL tools such as informatica or 
apache ni fi and for doing this they
have the steam of data ingenious so data 
ingenious job is to do this
transformation and maintain the data 
warehouse infrastructure and after that
our data masters will come in play and 
data masters will pull the data from
data warehouse and they will build 
the power bi dashboard for simplicity we
are not going to build any data 
warehouse will have an SQL database and
then we will directly plug power bi to 
that SQL database and build our
dashboard now sometimes the data that 
you need for analytics might not be
available in your organization at all so 
you might have to work on capturing that
information so many times data analysts 
will spend their time and they will
capture the required information that is 
needed to perform the data analysis so
that's all I have for this video in the 
next video we are going to look at data
cleaning and data merging this is also 
known as data wrangling or data munging
whatever you want to call it we will do 
all of this in power bi itself now data
munging data wrangling these are all 
sensitives do not worry actually it's
very easy you already know it's just 
that people use these fancy terms and
confuse you unnecessarily we are going 
to do all of this in power bi and trust
me you don't need any prior background 
for this project even if you don't know
anything about power bi it's perfectly 
fine power bi is very similar to excel
actually it is literally excel on 
steroids and we will look into it it's
it's very very easy even if you don't 
have any background you will be able to
learn it very quickly