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Data centricity, predictive analytics,
and decision-making
Data plays a critical role in modern
businesses (e.g., Marr, 2017, McKinsey 2022,
2023). Organizations must adopt a data-
centric approach to thrive in today’s
competitive landscape, particularly with
the rise of big data, the Internet of Things
(IoT), advanced analytics, and now AI. Data
can be leveraged to improve decision-
making, optimize business operations,
and even monetize data as a valuable
asset. With a rapid and accelerating digital
transformation, businesses are increasingly
dependent on predictive analytics
to forecast trends, automate decision-
making, and enhance their efciency.
To effectively manage AI and generative
AI journeys, the sector is on the way to
radically improving its data management,
putting the data at the forefront of its
digital strategy. Access to high-quality
digital data generated and maintained in
modern systems and applications is critical
to enabling the collection and analysis
of it to provide real-time insights, reports,
and dashboards. This capability enables
more accurate and proactive business
decisions, allowing GBS to play a greater
role in making strategic business decisions
and to develop digital capabilities and
analytics skills more powerfully. The vast
amounts of data require ever-increasing
storage potential and computing power
as well as investment in modern, well-
designed, and efcient data architecture
that supports the quality and integrity of
datasets produced. These will be key to
providing tailored and hyper-personalized
services soon. At the same time, in the
generative AI era, the quality of data upon
which the LLM algorithms are trained has
even greater signicance. Enhanced data
analytics capabilities allow businesses to
gain deeper insights into their operations,
customer behaviors, and market trends.
They drive better decision-making,
enabling companies to tailor their services
more effectively to meet client needs. They
call for developing smart transformation
strategies and investing in modern systems,
applications, and solutions, enabling
real-time access to large amounts of
high-quality data. Reorientation from
siloed functions to end-to-end business
capabilities and decision-making through
a unied data architecture and cross-
functional teams enables enterprises
to unlock opportunities within the value
chain and open new value pools. Building
an accessible and democratized data
foundation (Accenture, 2024) is one of the
components of an effective digital core. It
enables data mesh and data-as-products
and provides high-quality, curated,
and diverse inputs for AI ambitions. As
Carruthers & Jackson (2019) show, data can
be the source of dynamic and successful
business transformation.
The predictive aspect of data reading
allows businesses to anticipate future
trends not only in external markets, such
as customer behaviors and consumer
demands but also internally by analyzing
KPIs and OKRs to forecast operational
performance and resource needs. In the
business services sector, this capability is
essential for proactive decision-making,
enabling companies to optimize processes,
mitigate risks, and align strategic goals with
real-time insights.