In recent years, be it any industry you look into, you must have seen or read about digital transformation
and how “data” is changing the operational dynamics of the industry. From weather forecast based on
historical data, calculating the shortest route based on historical traffic data, analytics of specific ads to
calculate spend vs. conversion ratio, to boosting the revenue of the business, data plays a major role. Data
can be of any form such as transactions, data from sensors, data from the internet, and so on. The process
of evaluating the data using specific tools and coming up with specific information that will help in business
decision making is known as Data Analysis.
Before stepping into the context of what data should be analysed, let’s get a brief understanding of why
data analysis is important for businesses. Any business, be it old or new, needs to analyse the data as it
gives a clear understanding of their customer trends and behaviour, insight on how much more promotion
is required to attain a consistent growth rate, increase the business productivity and drives better decision
With this knowledge of data analysis, let’s identify what data we must analyse and the reason for it. We
will take a look at certain examples to explain the reasoning –
Fashion and retail industries are the ones with lots of data. Data such as “Outfit of the day”, trending
clothes design and style, spending habits of people of different types of clothes, influencer data, social
media statistics and trends play a major role in the success of the industry.
One of the most important sources of data can be from the Wi-Fi signals of customers who visit a
fashion/retail store. This will give a great context of the time they spend at the store, the sections they visit
and the amount of time spent in each section. With these data, fashion stores can derive data sets based
on which they can arrange the different dress collections according to user behaviour. Specific fashion
items and makeup items can be placed near the billing counter as they stand a higher chance to be picked
up by customers. Another aspect of the retail industry is to calculate the price and offer special discounts
to users based on their shopping behaviour. For example, data from the current store can be mixed and
matched with data from competing stores/brands and an average price set can be arrived at that will help
the customer to make the buying decision.
The other way data analysis helps the fashion industry is to make the shift from an offer based demand to
demand based offers that will increase the sales, both through direct channel and online channel.
The travel industry is dynamically changing, thanks to the changes in the consumer behaviour. Travel
companies are now forced to predict the customer behaviour and provide them with offers at the right
time. To better serve customers, travel companies must take advantage of data that they have access to.
Amongst all the data that is available to them, requests data play a major role for travel companies.
Requests data is nothing but the data that is generated from customer requests. Request data helps travel
companies to better understand the offers they provide versus the actual demand for the offer. This also
helps travel agencies to identify unique patterns from the data that helps them predict their customer
behaviour. Based on the prediction, they can provide a more personalized and customized offers for
customers. This will help in gaining increased traction and enhanced customer experience.
For example, travel companies can identify the days on which bookings are higher on their website and
generate special offers/discount.
Did you know that the business can retrieve a lot of insights from the payroll? With the modern tools that
are available, organizations are realizing the importance of payroll data. Surveys reveal that almost 88% of
the organizations see payroll as an important source of data. Why is payroll important? What data should
In an organization, payroll is a combination of people and their costs for the company. In simple terms,
payroll is a mix of HR (people) and Finance (workforce cost). Payroll plays a vital role with respect to the
cost incurred, rewards paid to employees and how much it impacts the level of retaining the workforce and
their skills. From payroll data, the best data that can be extracted are the employee leave history and
overtime data. Data sets can be analysed to understand the reason for overtime work. The business can
even see how best the employee overtime can be made into regular work hours.
Let’s take a look at some very generic examples of places where data plays a major role and what data
must be analysed.
1. Event Data
There is always a vast amount of data that is generated from events. From participant details, their choice
of events, event surveys, speaker details and so on, every data plays a major role. For event organizers, the
data of relevance will be the participant’s preference of topics, their interest levels for a topic, which days
they prefer attending an event (weekdays, working Saturdays, non-working Saturdays, Sundays), what type
of event they prefer (workshops, hands-on labs, full day or half day sessions) and so on. Another important
source of data is the event’ social media handle or specific hashtag. With hashtags, event organizers can
The other aspect of event data that must be analysed is the speaker data. There are a lot of communities
that organize and conduct meetups, events, roadshows, conferences and so on (such as Headstart, Startup
India, ProductGeeks, etc). With the help of data analytics tools and software, a database with the list of
speakers, their bio details and contact information can be made available so that it becomes easy for other
communities to organize events with these speakers. It avoids the overhead for event organizers to go and
search for speaker information on LinkedIn and other pages on the internet.
2. Fashion Clothes Brands Collection
As explained earlier, the fashion industry is one where there is endless data available for analysis. One of
the possible data that can be analysed is the dressing style from fashion shows and events. Based on the
styles that are showcased, fashion brands can arrive at specific customer profile statistics of which brands
were sold more post the event and the brand that was preferred most by the customers. Similarly, specific
data can be collected from TV shows and movies with respect to clothes and brands that generate a lot of
3. Taxi service Companies
In recent years, taxi companies like Ola and Uber have become very popular amongst users. With large
amount of data that is available, taxi companies can analyse lots of data patterns such as the most
common destinations travelled by the customer (e.g., airport, bus station, etc.,), the frequency of their
travel, how surge pricing affects the customer’s behaviour and choice, what type of vehicle the customer
chooses, on what basis people rate their drivers, and so on.
For example, with Uber, you can create a Bayesian model to understand the different Uber destinations
people wanted to go. This calculation would tell which destination people wanted to go 3 out of 4 times
during their trips.
With technology changing the way businesses operate, data analysis will play a major role in deciding how
to use the technology for your business. Data analysis will help in making predictions of customer
behaviour, increase the business profits and help businesses to make better decisions. Data management
solutions play a major role in helping businesses to arrive at data patterns and build actionable insights
that will improve the business process and generate positive results.