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Introduction
In today's fast-paced digital world, the strategic value of big data cannot be overstated. As businesses increasingly rely on data to drive
decisions, having a comprehensive assessment of IT operations and maximizing
the value IT delivers has never been more critical. IT infrastructure forms the
backbone of modern organizations, and those prioritizing governance and
integration stand to gain significant business advantages. When approached
strategically, big data provides an unprecedented opportunity to improve
operational outcomes, strengthen competitive positioning, and support
transformative innovation.
Overview: The Power and Pitfalls of Big Data
Businesses are eager to harness the power of big data — the
rapid, diverse, and voluminous data streams generated every second. Leading
organizations derive game-changing insights by capturing and analyzing this
data in real time. However, the trustworthiness of these insights hinges on
robust data governance and rapid integration strategies implemented from the
outset.
As the data landscape expands, ensuring the accuracy,
consistency, and security of data becomes increasingly challenging. Without
trust in the data, organizations risk making misguided decisions or missing out
on valuable opportunities. Automating governance processes and enabling
integration at the point of data creation builds confidence and ensures that
decisions are grounded in reliable insights.
To fully leverage big data, organizations must adopt agile
integration and governance frameworks that support the discovery, profiling,
and contextualization of diverse datasets. These frameworks must seamlessly
integrate with varied technologies—from data marketplaces to Hadoop platforms—supporting decision-makers with real-time, actionable intelligence.
The Big Data Opportunity
In recent years, the proliferation of big data technologies
has transformed how businesses approach analytics, especially within
heterogeneous environments. No longer can organizations rely on siloed business
intelligence tools tailored to a single platform, such as Hadoop. Instead,
today’s successful companies require platforms that are data- and
source-agnostic, capable of integrating and analyzing data across multiple
systems.
The decline of vendors focused solely on Hadoop, such as
Platfora, signals a shift toward more flexible, inclusive analytics solutions.
Meanwhile, traditional RDBMS technologies, like Microsoft SQL Server, have
evolved to support big data workloads, incorporating features like JSON support
to manage unstructured data.
Data volumes are exploding. Cisco’s Global Cloud Index
(2015–2020) predicted that stored data in global data centers would grow
from 171 EB in 2015 to 915 EB in 2020, with big data accounting for 27% of that
total. Simultaneously, data generated on devices is expected to reach 5.3 ZB.
Fueled by mobile devices and IoT sensors, the growth is exponential.
But unlike a tsunami, big data is not destructive—it is a
valuable asset. Organizations can use big data to gain insights and make
timely, impactful decisions, provided they have the tools and strategies in
place to harness it:
- Financial
institutions use real-time analytics to detect and prevent fraud.
- Retailers
monitor social media trends to offer targeted promotions.
- Content
providers personalize offerings based on user behavior.
- Utilities
manage energy grids in real time to optimize power distribution.
Overcoming Big Data Challenges
Despite its promise, big data brings several inherent
challenges:
- Beyond
Traditional Boundaries: Big data extends beyond structured, on-premise
data systems, encompassing social media, emails, PDFs, and sensor
outputs—all of which must be collected and analyzed cohesively.
- Volume
and Velocity: The sheer scale and speed of data creation—often in
real-time—make it difficult to derive actionable insights without
advanced analytics tools and infrastructure.
- Master
Data Management (MDM): To avoid creating new information silos,
organizations must align unstructured data with existing structured data
frameworks. MDM plays a crucial role in providing a consistent view of
entities like customers or products across systems.
By integrating MDM into big data environments, organizations
can generate more relevant, high-quality insights. MDM defines the "golden
record" of business entities, and when connected with analytics engines
like IBM Watson or InfoSphere, it enhances data trust and usability.
Data Lakes: A Strategic Foundation
Organizations are increasingly turning to data lakes—central
repositories that store unstructured and structured data at scale. While early
implementations focused on accumulating data, the current trend is shifting
toward extracting value through repeatable, agile usage of the lake.
The analogy holds: once a lake (data repository) is filled,
its value lies in its use. In 2017 and beyond, organizations demand
justification before investing in infrastructure, focusing on clear business
outcomes. This shift fosters closer alignment between IT and business
stakeholders and increases the relevance of self-service business
intelligence (BI) tools that allow non-technical users to access and
analyze data directly.
Understanding Big Data: At Rest vs. In Motion
Big data can be divided into two broad categories, each with
unique infrastructure and analytical needs:
- Big
Data at Rest
Refers to stored data awaiting analysis. Examples include historical logs, reports, or customer records. Batch processing is typically used here to uncover patterns and optimize long-term strategies. For instance, analyzing millions of leads to identify high-conversion segments. - Big
Data in Motion
Involves real-time data streams that must be analyzed on-the-fly. Examples include health sensor outputs, credit card transactions, or traffic feeds. Latency is critical—any delay can result in missed opportunities or risks. Tools like IBM InfoSphere Streams enable low-latency analytics, allowing for immediate action (e.g., flagging a fraudulent transaction before it’s processed).
The success of Big Data in-motion analytics depends on processing power and robust, low-latency network infrastructure to
maintain availability and performance.
Conclusion
To truly unlock the value of big data, organizations must
implement well-defined strategies for governance, integration, storage, and
analysis. The goal is not just to collect data, but to trust it, act
on it, and drive business value from it. With the right
infrastructure, methodologies, and tools in place, organizations can harness
big data to enhance customer experiences, improve operational efficiency, and
create new sources of competitive advantage.
As data volumes grow and technology evolves, the winners
will be those who invest not just in big data tools but in the governance,
agility, and integration required to use them wisely.