1.
Transformation of Business Analytics from Business Intelligence
The primary benefit you will be getting from having Spend Visibility (the by-product of the
data transformation process) is that it will facilitate better decision-making. This applies
not just to you, the business leader, but to all the levels of the organization, which is what
makes data-driven Spend Analysis such a highly sought-after service these days. The
question is, though, how do Business Analysts generate the spend data that changes your
business almost overnight?
Below, we’ll be exploring the main steps of the Spend Analysis Process to get a better idea
of how it’s all done, starting with a small introduction to what Spend Analysis is in the first
place. You know, in case you forgot.
What is Spend Analysis?
Spend Analysis is the name given to several collective steps of gathering and processing
the spend data of an organization to identify their spend trends. In most cases, this service
is provided with the larger aim of identifying saving opportunities, streamlining operations,
and improving the organization’s overall Spend Visibility.
In easy-to-understand words, it lets you save money and optimize the efficiency of your
business operations.

2.
What Are the Processes Involved in Spend Analysis?
1. Data Extraction (Identification)
Identification, or Data Extraction, is the first step of the
data transformation process and involves extracting
spent data from both internal sources (departments
and such) of the organization and external sources
(vendors, suppliers) related to the organization.
Typically, this extends to every department, plant, and
business unit since Spend Analysis requires real-time,
comprehensive spend data, leaving no sources out.
Some of the prevalent sources of spend analysis data
you’ll find are:
● Enterprise Resource Planning Tools
● The organization’s general financial data
● Purchase orders of goods and services
2. Data Consolidation (Gathering)
Data Consolidation is the process of
consolidating all of the different data you’ve
gathered into one central database. It might
sound as simple as normal Data Entry
operations, but in reality, Data Consolidation
comes with many challenges. You’ll see
problems like the following when consolidating
your data:
●Data is in different languages, formats, and
currencies.
● A particular receipt or piece of spend data might not have all the details.
● Columns may not match across sets of data.
These problems are some of the biggest reasons project leads prefer to use “hand coding”
in more minor cases, where Data Engineers sort this data into neat datasets. For larger
datasets, you’ll want to use ETL tools made for this specific purpose.

3.
3. Data Cleansing (Cleaning)
Data Cleansing is at once one of the essential steps and one of the most frequently
mishandled parts. In practice, it’s removing inaccuracies and redundant or corrupt records
from the central database. Professionals in data transformation services find and remove
errors in the datasets (transactions,
descriptions, etc.) to ensure data
quality.
There are three main steps of Data
Cleansing:
● Identifying and correcting
unstructured or jumbled data
● Filling in missing values and
fields in datasets, along with removing
errors
● Finding, fixing, or deleting irrelevant, corrupted, inaccurate, duplicate, or incorrectly
formatted data
Like you might have guessed, this step aims to produce a clean database that reflects the
entire company spend.
4. Data Clustering (Clustering)
Data Clustering refers to a technique used by organizations to identify purchases made from
the same supplier or vendor using a
different name. For example, a
supplier’s name in one receipt might be
in acronyms but fully spelled out in
another. You might even see the same
supplier but with a different location
after their name.
To prevent datasets from becoming
confusing, Data Clustering creates
supplier grouping for better supplier management. Data Clustering is a massive help in
creating reports and insights later down the line.
5. Data Classification (Categorization)
Classification is the technique businesses use to categorize the gathered, cleaned, and
clustered data into separate categories of information and identify a consolidated spend.

4.
Spend data for similar goods and services is
grouped into predefined, clear categories to
make company spend easier to address and
manage at all levels of the organization.
If all that was a little complicated, here’s a
simple definition of this step: Data
Classification allows leaders to gain Spend
Visibility and make better sourcing decisions
by sorting spend data into clearly defined
categories.
6. Data Insights (Analyzation)
Once the company spend data is categorized into clear categories through Classification,
procurement professionals analyze this
central database to identify opportunities
to save on expenses and streamline
operations. These insights are leveraged
in the right places by data
transformation services experts to
reduce costs, such as reducing
procurement costs by having all
purchasing happen from preferred
suppliers that provide discounts.
The last and final step, Data Analysis,
generates the spend insights, reports, and KPIs resulting from the Spend Analysis process.
7. Data Refresh
Doing it once, though, is never enough. Spend
Analysis needs to be a repeated process with fresh
data sets to keep identifying saving opportunities.
Wipe the data and keep on going with the latest data
transformation tools!
Conclusion
At the end of the day, though, analyzing company spend is far easier said than done. The
data transformation process used for Spend Analysis holds excellent potential for
identifying savings opportunities, but you need both the latest technology and consulting
services to ensure a good job is done. Our opinion, though? The risk management,
compliance, and profit maximization benefits of Spend Analysis are definitely worth the pain!

5.
Our methodologies:
Please contact us if you'd like to learn more about in2in global automated data analytics
solution, which can provide data - driven analysis from your data and enable it to be used
for its intended purposes.