The need of big data testing for digital customer experience

1

The need of big data testing for digital customer
experience
2
The need of big data testing for digital customer
experience
In todays world of digitalization, the top
motive for enterprises is to ensure a robust
digital customer experience. However, tracking
this experience has been a challenge for
eons. The advent of emerging technologies such
as Artificial Intelligence (AI)/Machine Learning
(ML), 5G, Internet of Things (IoT), and Big Data
Analytics, has made measuring customer
experience possible now. While AI/ML, 5G, and
IoT can indirectly help gauge the customer
experience, Big Data Analytics is going to play
a big role in measuring the profundity of
customer experience. Measuring Customer
Experience Gartner in its recent study emphasized
on 5 types of metrics such as Customer
satisfaction (CSAT), customer loyalty/retention/ch
urn, advocacy/reputation/brand,
Quality/operations, and employee engagement to
measure customer experience. While all these
metrics quantify the measurement of customer
experience, it is imperative to understand that
only those metrics need to be taken into
consideration that are relevant to the business.
Recent studies by industry experts suggest that
it is only the correct analysis, meaningful use
of data collected, and holistic approach that can
help improve the digital customer
experience.
3
The need of big data testing for digital customer
experience
Big Data Testing for better data
quality According to Evans Data Corporation,
19.2 of big data app developers say quality of
data is the biggest problem they consistently
face. Data quality is a vital characteristic
that determines the trustworthiness of
decision-making. It helps you manage and cleanse
data while making it available across the
enterprise. With Big Data comes bad data. So, it
is imperative that the processed data adheres to
the highest standards. Big Data testing is
completely different when compared to the typical
testing around traditional data warehouses or
databases that revolve around structured data and
use Structured Query Language (SQL) to accomplish
the testing. Big Data deals with not only
structured data, but also semi-structured and
unstructured data and typically relies on
Hibernate Query Language (HQL). Data Validation,
Process Validation, and Outcome Validation are
the key components of Big Data Testing. While
validating the data, the collected data is
ensured to be accurate and not corrupted. This
collected data is sent to Hadoop Distributed File
System (HDFS) for validation where it is
partitioned and thoroughly checked.

4
The need of big data testing for digital customer
experience

It is imperative to identify core business
priorities first to ensure that business
opportunities are driving your analytics
investments. While many firms become more
data-driven with business models that depend on
data, deprived quality data will gradually become
a systemic problem. Cignitis Big Data Testing
framework helps clients measure and improve
digital customer experience using Big Data
Analytics. Cigniti leverages its experience of
having tested large scale data warehousing and
business intelligence applications to offer a
host of Big Data testing services and solutions
such as BI application Usability Testing. Need
help? Talk to our Big Data Testing and Customer
Experience experts to know more about Big Data
Testing for superior digital customer
experience. Read Full Blog at
https//www.cigniti.com/blog/big-data-testing-dig
ital-customer-experience
5