There's no debate: Data is the
most powerful resource for businesses today.
While many companies build their
business models around data, others garner, store, analyze, and use complex,
bi-directional data in order to draw up precise, irrefutable patterns, decipher
actionable insights, predict business outcomes, monitor and track consumer
behaviours, and improve customer lifetime value.
As per a study by Gartner, businesses
prefer data-driven decision-making over
intuitive decision-making. Intuitive decision-making that involves harnessing
the gut feeling to make decisions is, more than often, incomplete and
inaccurate.
This probably accounts for why the
data analytics landscape is increasing at a rapid rate of nearly 30
percent.
With these factors in mind, let's
identify the six macro technological trends that will likely revolutionize data
analytics in 2024.
1. Automation will be an organization-wide initiative
IT leaders have relied on
automation for data analytics for some time now. Deloitte report indicates that 53% of
organizations have already begun implementing robotic process automation (RPA),
and that's expected to rise to 72% over the next two years. However, studies
have found that many automation use cases serve a limited purpose and remain
isolated, creating governance issues and limiting scale.
2024 will be the year when
top-notch organizations will shift from leveraging isolated pockets of
automation to more strategic, enterprise-wide automation. In other words, they
will drive hyper-automation initiatives to analyze and use data to make quality
decisions and deliver value.
What's more, hyper-automation will
lower costs. According to Gartner, by 2024, hyper-automation will help organizations reduce operational costs by 30
percent. Amid growing economic uncertainty, hyper-automation will transform
data analytics, speed digital transformation, and accelerate growth to help
organizations manage disruption and deliver value.
2. Composability will be a core business pillar
Data silos have been a consistent
roadblock to connectivity. Using analytics that is pervasive, more
democratized, and composable will be key in 2024 and beyond.
Gartner predicts that 60 percent of
organizations will use analytics solutions
that are composable. In other words, organizations will become more composable
and fuse different components from a variety of analytics solutions to gain a
complete, richer view of their data. So, composable analytics adoption will be
on the rise.
It's obvious that businesses will
prioritize composability. This will enable teams to reuse existing capabilities
for integration and automation, thus improving time to value. As a result,
organizations will have the flexibility they need to adapt with agility to
emerging market demands in order to increase customer loyalty and drive growth
in 2024 in a cost-effective and strategic manner.
3. More businesses will shift from big data to big AI
Many organizations fail to analyze
the large streams of data they collect. This is primarily because the majority
of data (almost 90 percent) is either unstructured or has no defined schema.
With the help of AI and machine
learning technological solutions, organizations will be able to analyze
unstructured data much more smartly and quickly. These solutions will also
identify patterns and trends present in structured data that aren't visible. By
leveraging AI and ML solutions with data analytics and business intelligence
capabilities, organizations will handle complex, bi-directional data types and
unravel the value of unstructured data at scale. Today, AI/ML-powered solutions
can locate and garner data from highly unstructured files or documents with
nearly 95 percent accuracy. It's not difficult to predict artificial
intelligence will continue to transform and mature and gain popularity in
2024.
4. More businesses will adopt meta-data-driven data fabric
architecture
Now, organizations are integrating
and automating different data systems and relying on AI/ML technologies to
analyze the ocean of data. And for that, they need to combine conventional data
sources and modern capabilities. Here the concept of data fabric comes in.
Data fabric empowers organizations
to process and analyze complex, bi-directional data streams from systems that
are both logically different and physically different, including multiple
clouds, social media, on-premises, IoT devices, etc.
However, the data has to be in the
right context. By enriching the data fabric with metadata, analysts can garner
deeper insights into data. By adding context to data, the meaning can be
collected easily. Also, it helps analysts understand its relationship with
different kinds of data, which can help organizations get holistic insights,
and finally make informed decisions.
5. Analytics will facilitate adaptive and real-time
decision-making
Clearly, analytics has become more
contextual and continuous. To manage data better, analytics should become
more adaptive, all thanks to artificial intelligence and machine learning
technological solutions.
Analytics, therefore, cannot
afford to just focus on historical data. Instead, it will analyze and process
data in real time, comprehend the meaning, and adapt its approach
accordingly.
The fundamental benefit of
adaptive analytics is that organizations will feel empowered to make proactive
business decisions based on real-time data with accuracy. Because data is
analyzed in real time, the system shouldn't turn obsolete.
6. Edge computing will transform data analytics
There is a steep rise in
machine-generated data from the internet of things (IoT) and industrial
internet of things (IIoT) devices.
The volume of this data is so big
that traditional computing models, where everything is controlled and analyzed
centrally, fail to perform and deliver. Both accuracy and speed suffer.
To manage such large volumes of
data, organizations are opting decentralized computing model (a.k.a. edge computing). In this method, analytics, AI, and decision intelligence are
used in edge applications. It enables organizations to analyze data in real
time and deliver actionable insights for decision-makers - quickly, securely,
and accurately.
Edge computing helps organizations
deliver faster insights by performing analytics where data is created, rather
than transmitting it back to a centralized database for processing. However,
this will lead to increased security risk. Organizations will need to mitigate
the risks by adopting cybersecurity mesh approaches, underpinned by API
management.
Conclusion
Simply put, data is a new
resource, but one needs a powerful engine to harness its true potential.
Organizations that take advantage of these trends to build a strong analytics
foundation and a robust analytics-competent culture will be able to turbocharge
their innovation strategies and decision-making.
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ABOUT THE AUTHOR
Chandra Shekhar is a
digital marketer at Adeptia Inc. As an active participant in the IT industry, he talks
about data integration and how technology is helping businesses realize their
potential.