Page 27 - ITLN May - June 2022 issue

what was collected. Accuracy ensures
the data collected is correct, relevant,
and accurate. Timeliness ensures the
data is received at the expected time for
the information to be utilised efficiently.
Consistency: ensures the data is aligned
or uninformed with another dataset.
Integrity ensures that all data can be
traced and connected to other data.”
As an industry, air cargo is very
disparate in technology adoption and
is not connected to the entire cargo
community, according to More. So, data
is re-entered 14 times in the value chain
manually leading to duplication, errors
and incomplete data.
“Cargo Community Systems (CCS)
essentially form a network on all
the logistics stakeholders (shipper,
consignee, transporter, freight
forwarder, customs, cargo handler,
airport, airlines) who are connected on
before they even perceive that need,” as a common portal to exchange all trade
he puts it. related data which is useful the next in
“The ML algorithms would line stakeholder. The CCS also checks
understand human behaviour with for data discrepancy with its in-built
large-scale automation and data intelligence and prompts the users to
integration, and predict exactly what correct any incorrect data. This thereby
customer needs,” he adds. ensures data accuracy, authenticity,
For Ramnath, it is not just about speed of operations and transparency of
building dashboards with lots of charts trade,” he said.
and filters. Instead, data visualisation Meanwhile, Rajan notes that there
means representing insights from are three key steps towards developing
the data. a data-driven and analytics strategy
And for him, data visualisation could for any business.
be one or a mixture of these two types A well thought-through As he puts it, “One, you need to pin
of dashboards. “Operational dashboards data-driven analytics down the outcomes that you are driving
display real-time metrics. E.g., a strategy can bring towards, which should then define
dashboard to display the performance better yields, become your data needs. This is more complex
of the shipments and alert operational more responsive, than it looks as good data is the most
teams to anomalies. Strategic critical resource in any analytics
dashboards present key performance improve efficiencies and project. The second step is to evolve
metrics to senior management and aim deliver better customer business practices around analytics
to tell the 'big-picture' story behind the satisfaction. and start thinking about how such
data. E.g., a sales dashboard to display Ashok Rajan services are going to be consumed
the cargo sales insights to the cargo IBS Software by your business. Thirdly, you need to
sales heads or head of cargo to make start thinking about the right platforms
the decision,” he says. and IT environment that can transform
The algorithms would seem cool but in So, if the data is incorrect, the entire the business data into insights that
the absence of the right and quality data, model is incorrect. Hence, ensuring the help to make decisions to eliminate the
it is impossible to achieve those results. quality of the data is paramount. “Chinese Whisper” problem and ensure
Ramnath explains that there that all stakeholders are consuming
Collecting the right data are four components which will the same data.”
We cannot build an AI strategy if we ensure data quality – called CATCI Indeed, there is room for improvement
don't have the right data. The AI/ML (Completeness, Accuracy, Timeliness, and technologies like AI ML and data
models are built and learned from data. Consistency, and Integrity). visualisations can solve them. However,
The first step of creating a model is He says, “Completeness ensures more than anything we would need good
data collection or data preparation — there are no gaps in the data between leaders with the right vision to help use
extracting inputs to the model from data. what was supposed to be collected and technology for the right reason. 
25
www.itln.in May - June 2022