The Challenges of Real-Time
Data Analytics
Becoming a data-driven company can be a difficult journey --
fraught with frustration and uncertainty. Businesses continue to
struggle to capture, manage and leverage the value of their data.
And when it comes to analytics -- any kind of analytics -- there is
nothing more important than the data being used in analytics. And
bad data can lead to bad decision-making.
Data Strategy
To make sure data is truly used as a company asset, a data
strategy needs to be put in place. This framework provides a
common set of goals and objectives across projects to make sure
data is used effectively and efficiently and supports the overall
goals and needs of the organization.
The bad news: In a 2021 survey by Cognopia, a data transformation
and consulting firm, reported that more than two-thirds of firms
still did not have a data strategy in place, and a third had no
dedicated budget to improve their data.
The good news: 53% of the survey participants reported they were
in the process of drafting a data strategy now."
Neil Burge, CEO of Cognopia, offers a pragmatic solution. "Align
your data strategy against business strategic goals," he says. "This
enables you to justify investments in data that directly support
critical business objectives. Link the dollar values together to get
executives interested."
Data Governance
Data governance helps set the foundation for how a company's
data is used, accessed, managed, and protected. Effective data
governance drives better data analytics which helps drive better
decision making and operational support across the entire
organization.
According to McKinsey, the challenge starts with management.
THE NEED FOR SPEED | 9
Analytics 3.0
Advanced Analytics -
The first two decades of the
21st century observed the rise
of artificial intelligence (AI),
machine learning (ML) and
robotic process automation
(RPA). These newer, more
agile, and advanced analytic
tools could produce insights
at a much faster rate than
ever before. During this time,
data analytics comes to the
forefront of business
initiatives, with bigger
budgets and bigger roles --
including the advent of the
Chief Data Officer and Chief
Analytics Officer. More
importantly, technology and
business leaders are now
working hand in hand to drive
greater value in the
company's operations.
Analytics 4.0:
Automated & Embedded -
This phase of analytics is still
in its infancy. It is largely
defined by increased process
automation and data
analytics that are now
embedded in individual
business tasks. You will now
find data analytics integrated
within the operational and
transactional processes of
key departments in the
organization -- including sales
and marketing, human
resources, finance, and
claims.
