Articles
Application of Data Engineering in Your Business
by Tech Geekk writer
Digital
transformation has led to the continuous generation of a huge quantum of data,
which has created a need for data
engineering services to organize and convert data into usable formats.
Designing and building data pipelines, to
transport and transform raw data into usable formats can be termed data
engineering. Data from various sources is collected in a data warehouse or
a data lake and represented uniformly. Data engineering services assist in designing, managing,
and optimizing the data flow.
There has
been a visible shift in how businesses have started viewing data as an asset
and the use of this data has been a game changer for business operations. Data
is tracked, recorded, leveraged & analyzed to make informed decisions,
unlock new business opportunities and also increase business growth. Digital
data offers huge possibilities for organizations to innovate and also serve
customers.
Organizations
that adapted to this trend early on have benefited from it and are ahead of the
competition. These organizations can redefine their strategies to connect and
engage with customers across digital channels and capture market share and
customers.
Importance
of data engineering -
A decade
back, data engineering consisted of data warehousing, business intelligence &
ETL (Extract, Transform, Load) (tooling to move the data). In the current
scenario, data engineering focuses on
Best
practices - Data engineering
focuses on best practices in the software development cycle, which includes
appropriate use of version control, release management & automated DevOps pipelines.
Security - Data hacking and data breaches pose a serious
threat to businesses and customers. Data collected by businesses have to be
compliant with System and Organizational Controls (SOC 2) or Personally
Identifiable Information (PII) that the business is legally required to
protect. Data engineers are expected to grasp the security issues related to
this issue and take appropriate action in managing risks involved with data in
transit and at rest.
Business
domain knowledge - It is now essential for data engineers to have
domain expertise to draw insights from the data.
The purpose behind data engineering is to offer
an orderly and uniform flow of data that can enable data-driven models like
machine learning and data analysis.
The
Benefits of data engineering for businesses are -
· Agility - Using data engineering, businesses can be
agile in taking decisions based on the insights that the data analytics
provides. Businesses that are data-driven and invest in processes that can take
advantage of the data streams and their subsequent analysis, can spend more
time fine-tuning their companies.
Enhanced
efficiency -
Integration of data analytics can help enhance an organization’s competencies
to become more effective and find better opportunities. Data analytics can
provide awareness to take insightful decisions and drive the business forward.
· Revenue generation - Data analysis can be termed as the new
revenue stream. Improvements in data collection and subsequent predictions can
help in decision-making. A data-driven organization can innovate, improve and
generate more revenue streams.
· Digital marketing, product development, and better
decisions - Companies
leverage data to get better insights into their businesses to make better
decisions which can result in better and improved results. To market products
and boost sales businesses can run campaigns that are targeted to specific
audiences which makes marketing and advertising activities more effective.
Companies can also use sales data to measure the effectiveness of such targeted
campaigns and take further decisions based on the results. Marketing and ad
campaigns are targeted based on different geographical locations, beliefs &
preferences.
In
2017, Coca-Cola revealed that its new flavor Cherry Sprite was inspired by the
results of data collected from a self-service drink fountain. where customers
can mix and make their drinks. Coca-Cola was tagging which flavors were mixed
the most in different areas of the world. The analytics were done and the Cherry
flavored sprite combination was made into a purchasable item.
· Decision outcomes - Data-driven decisions generate additional data
that can be used to evaluate the impact of decisions taken based on previous
data streams and their analytics.
· Improving user experience - Data analytics and data engineering assist in
the development of new products and make it easier for a product management and
development team to spot patterns that can drive the strategic roadmap for new
products, services & innovations. Businesses can use machine learning
algorithms to forecast future market behavior based on the data. Machine
learning can spot patterns and can be used for market predictions based on
historical data, ensuring that businesses stay competitive and anticipate
market demand to deliver products.
Data
engineering makes it simpler and easier to solve complex business issues. A data engineering service
provider can help with digital automation and artificial intelligence to
interpret complex datasets helping the stakeholders to make informed decisions
for the betterment of the organization. Such a team of expert data engineers
and developers can help business owners generate better data analytics and
harness the business potential and generate better revenue streams.
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Joined APSense since, March 9th, 2016, From San Jose, United States.
Created on Nov 3rd 2022 03:58. Viewed 107 times.
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