ERA OF INTELLIGENCE SERIES

Embracing the eraof intelligence for adata-driven future
Unlocking business value through effective useof data

Becoming data driven is not just a
strategic move but a foundational
necessity for businesses acrossthe globe.
The transformation involves leveraging datacomprehensively to inform decision-making processes,
enhance operational efficiencies, and tailor products
and services to meet customer needs. By properly
utilising and analysing data, businesses can identify
trends, predict customer behaviours, optimise theiroperations, and innovate with agility.
This transition, however, is accompanied by a host of
initial challenges that customers face, which can range
from technological hurdles to cultural shifts. This article
highlights our strategic response to the evolving market
landscape and customer expectations and emphasise
key service areas and capabilities that empower ourcustomers to embrace a data-driven approach.

Empowering Change: AI Technology to drive data
driven innovation.
We have seen AI take a leading role in the data conversation withour customers over the past year, but this isn’t their only concernor focus, the results from the Immuta survey below are echoedby our own observations. As AI takes a more prominent role in theenterprise data landscape, the need to ensure that appropriate
data governance and security are more important than ever.

We have seen AI take a leadingrole in the data conversation
with our customers over thepast year.

Key Enterprise Initiatives and Priorities

35%

22%

20%

13%

4%

Implementing stronger
data governance andsecurity controls

Modernising dataarchitecture

AI integration intobusiness process

Enabling data
self service

Improved data
privacy

Source: https://www.immuta.com/2024-state-of-data-security-reportWe examine the obstacles faced by businesses and demonstrate
how Computacenter assists in surmounting the barriers that
impede the realisation of the full potential of our clients’ data.
We continually discuss with our customers the primarychallenges in becoming data-driven, from:
Data management and integration of disparate data sourcesmany of our customers find themselves grappling with siloeddata spread across different departments and systems, making
it difficult to achieve a unified view of the information. Thisfragmentation not only hampers the efficiency of data analysis
but also leads to inconsistent insights, undermining decisionmaking processes.
Data quality and governance
Ensuring the accuracy, completeness, and reliability of data is
critical for deriving meaningful insights. However, our customers
often struggle with maintaining high-quality data due to issues
such as duplication, inaccuracies, and outdated information.
The scarcity of skilled data professionals
The demand for data analysts, and engineers far exceeds thesupply, making it challenging for companies to build or expand
their data capabilities. This talent gap is often seen to delay theadoption of data-driven practices and limit the potential benefits.

Organisations realise that
adaptation through data isunavoidable.
Cultural transformation
A data-driven mindset poses its own set of challenges, Shifting
the business culture to value and prioritise data over intuition or
past practices requires a fundamental change in attitudes andbehaviours.
Organisations realise that adaptation through data isunavoidable. Having a strong data-driven culture means that theworkforce is data literate. Executive leaders are having to change
behavioural patterns to drive talent and business results.
To promote successful adoption of new tools, companies are
beginning to instil data literacy into their organisational culture.
By introducing a steady, change management programme,
customers are starting to see benefits, whilst safeguarding
an organised and co-ordinated transition. This ensures little
disruption to procedures, maintains high optimism, and fosters
acceptance within the workforce.

Data Management

Data Quality and Governance

The volume of data generated daily is staggering. According to
Statista approximately 329 million terabytes, or 0.33 zettabytes,of data are created each day. This amounts to roughly 2.31
zettabytes per week and 120 zettabytes annually, illustrating
the immense scale of data production and the challenge thatbusinesses are looking to undertake to manage it.

Ensuring the accuracy, completeness, and reliability of data is
critical for deriving meaningful insights. However, businesses
often struggle with maintaining high-quality data.

Navigating through this vast data-driven landscape highlights theimportance of addressing practical concerns such as enhancing
data security, ensuring privacy, and efficiently managing diverse
data sources. At Computacenter we are seeing this fundamental
data challenge halt or at the very least limit customers’ ability
to really show the value that their data can bring to the wider
business. Siloed data and aging or incompatible data platforms orsources being fundamental issues in the ability to truly visualise
what they have available to them.
We are working with many clients across all verticals to unpick
how best to tackle this challenge, whether it is best to replatform
and consolidate onto a new technology either on premises or in
the cloud or look to leverage a data virtualisation capability to givea unified view of the estate without having to migrate as readily.
There are many solutions to this challenge but maintaining
whatever is put in place will be just as important as making thedecision in the first place.

Due to the numerous sources from which data can be harvested,
all with different data formats and controls, data can often beduplicated, unreliable and incomplete. What’s more, poor data
quality often doesn’t reveal itself immediately. It is only when anunderlying service fails or analytics delivers something clearlywrong that organisations realise that the data, they have been
relying on isn’t fit for purpose.

Navigating through this vast
data-driven landscape highlights
the importance of addressing
practical concerns such asenhancing data security.
To qualify what is “good” data often entails understanding not justthe reliability of the source but putting the data in context of its
use and impact. Our customers often have a good understandingof specific data, for example around user or financialtransactions. But as AI tools surface information from more widelydispersed and less controlled data sources, combining it withthese original data sets to use it in different ways (for example topredict outcomes) can result in issues and anomalies.

To ensure data is fit for purpose businesses need to have a robust
set of data quality rules that identify the key features and uses ofthe data itself, which if adhered to, give comfort the data is fit forpurpose. These need to be carefully written and tested to ensurethat they support the system or processes the data underpins, notsimply about the data itself.
Data retention is also key. Whereas before it was enough to knowwhat data that was being stored (for example, personal data
for GDPR) now organisations need to know how and where data
is being stored for security and resiliency purposes. In financial
services for example, regulators are expecting to see audit trailsback from the activity the data drives to the data source itself. Our
customers therefore need to document and control these audit
trails.
These requirements call out for a governance function. This data
governance needs to cover not just the data itself, but also thetooling, infrastructure and processes surrounding the data use.
This can help avoid data sprawl, tooling duplication and ensuring
designs make the most of existing data lakes and controls.
Data quality and governance often gets lost in the excitement ofuse cases and the potential benefits. It is only when a process
is unavailable, or conclusions clearly wrong that people try toaddress it. However, this can come at a cost as by then you’redealing with process failings that can have a much larger
business impact.

High Performance Architectural Considerations
The data market is also seeing a shift in how enterprises arestarting to consume their data, with technology focused on
fulfilling edge use cases really coming to the fore, processing
data closer to its source. This shift in moving the processing
closer to the source is critical for handling the amounts of data
generated by contemporary digital activities and IoT devices,
reducing network traffic and transportation bottlenecks
wherever possible to give the performance that the business sodesperately needs. If we then layer on the core data center, public
cloud, and the desire for multi-cloud data mobility, then this adds
yet more governance and performance concerns that need to bemitigated through architecting the correct data platform to caterfor your specific enterprise needs. The data architecture shouldbe aligned to the use case that is being driven, enabling the data
to be consumed as close to the source of its generation, this is anarea that is often overlooked when looking to show value quickly.

AI Adoption
Leveraging data through business focussed AI driven endeavours
is where many are looking, but to achieve that effectively wefirst need to understand the underpinning data assets, after all,there is “no good AI without good data”. In our dialogues withour customers, we aim to present our advice in this domain,
illustrating how companies can demonstrate initial wort whileand simultaneously identifiying which data holds genuine value
and the most effective ways to utilise it.
While the journey towards becoming data-driven is fraught withchallenges, the benefits it offers in terms of enhanced decisionmaking, operational efficiency, and customer satisfaction
are unparalleled. By addressing these challenges head-on,
businesses can unlock the full potential of data and position
themselves for sustained success.

Technology and data challenges such as the ones mentioned
through this article are typical in preventing customers fromshowing the value of AI within their businesses. Uncovering
the data that is needed for a particular use case or businesschallenge, and more importantly ensuring its quality, lineage
and ongoing lifecycle management is a fundamental need forits sustainable use and the overall accuracy of the outcome. We
spend significant time with customers in this space, ensuringthat the data is “good” and it is “right” for the use case that it isbeing leveraged for.

As can be seen from the graphic below, incremental value
increases through the adoption of AI over more traditional
techniques across industries are, or can be significant, but it cantake time, and relies on the above areas to ensure that the results
are truly achievable.
The positive impact that well governed AI can bring to businesses
are clear, both internally facing, through operational efficiency
measures as well as externally facing, through its ability to growrevenue reduce time to market.

Potential Incremental Value from AI over other Analytical Techniques
Travel128%

Transport & Logistics

89%

Retail87%

Automotive

85%

High Tech85%

Oil & Gas

79%

Media & Entertainment

57%

Agriculture

55%

Banking

50%

Healthcare

44%

Public Sector

44%

Telcomms

44%
39%

PharmaInsurance

38%

Source: McKinsey Global Institute Analysis

Conclusion
AI and its use are coming under the microscope with thedevelopment of the EU Artificial Intelligence Act and thecloser to home UK AI Regulation Framework, so whether
our customers operate within the single EU market or in
the UK they will be expected to abide by the guidelines andframeworks that are being put in place to ensure correct
ethical standards are met through accountability, fairness,transparency and safety.
We are yet to see the full impact of these impending actsand how our customers are likely to be affected, but weneed to be aware of the implications in not adhering to thestandards being set, and the data, and use cases being
used to drive these endeavours forward.

EU Artificial Intelligence Act
UK AI Regulation Framework

At Computacenter, we look to take a holistic view of the data
driven landscape and the activities that our customers arelooking to achieve in this area. Business value through AI
cannot be fully realised without the underpinning capabilities
that we have spoken about here. Good data management is afoundational requirement to drive data strategy and feed into
AI management, governance, and standards. Ensuring thatthe architecture across the enterprise, from edge to core tocloud, allows for the evolution of the business in the long termand includes an optimised operational management and datadelivery capability is fundamental for ongoing success and theability to deliver against wider business goals. Any technology
and software choices should be made by ensuring that they caterfor future needs by driving simplicity, standardisation and above
all performance to the users that need to leverage the data thatflows through it.
Our data assemsnet and distribution solutions serve as anessential foundation for our customers, providing them withthe assurance that any data-centric initative is poised for
greater success.

Office of the CTO
The Office of the CTO (OCTO) team leads in the exploration and application of technology
products and delivery methodology to aid the digital transformation of our customers.
As a team of cross-functional technologists with extensive industry and IT experience wedeliver thought leadership, advice and real-world implementation experience to help ourcustomers achieve their goals.

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