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Building a Data-Driven Business with Advanced Analytics

Organisations are increasingly reliant on unlocking the data held within IT systems to gain insight into how to better connect with customers, drive innovation and improve efficiency.

Technologies such as cloud, mobile and the Internet of Things are creating huge amounts of structured and unstructured data—but many organisations are held back by data silos. Building a data-driven business depends on developing analytics competencies that can convert data into valuable information to drive real-time decision-making.

“Data is the new oil,” says Intel’s Heiner Genzken. “On its own, data is boring. What you do with data is what makes it interesting.” Using advanced analytics for data, organisations are beginning to understand the potential.

Economies of scale enable AI
Artificial intelligence (AI) is attracting attention as advances in processor technologies and resulting economies of scale mean that AI is no longer limited to high-performance computing environments.

But AI is not new, as Genzken points out: “Thirty years ago, I created a neural network that could differentiate between a capital A and a capital B, which was considered a success at the time.”

Now, with advances such as Intel’s Xeon Scalable processors that allow organisations to analyse data sets with ease, the opportunities are expanding rapidly.

“Everything that is possible will be made available,” says Genzken. “The compute power today means that AI has become affordable and is available to do real-time analytics for a relatively small budget. Now real-time video analytics can compute data and detect a moving picture, which was impossible 30 years ago. The possibilities on a given IT infrastructure based on Intel processors are huge.”

Data-driven digital transformation is transforming how organisations do business, with analytics and AI tools driving innovation across industries.

Genzken highlights how the automotive industry is using AI to enable autonomous driving, while in the healthcare sector, it is enabling real-time analysis of health conditions.

“A person’s skin can be scanned in nanoseconds or real time because of AI, and they can be given therapy specific to their genome in a cost-effective way,” he says.

However, some sectors are moving faster than others.

“Some industries have not yet deployed everything that is possible, and this is because of a number of factors, including legacy infrastructure, rules and regulations, and cost,” says Genzken.

For example, in healthcare, people are still using paper-based processes that other people must pick up and read, as well as information that is stuck in a system. This can lead to well-publicised problems such as an accident and emergency department being unaware that beds are available for patients.

“There is no real-time information exchange from one department to another, but this sort of thing is a worldwide phenomenon,” says Genzken.

Two key challenges
Unlocking valuable information and accelerating analytics deployment are two key challenges organisations must overcome to address bottlenecks. Understanding where you are on the analytics journey is the first step, followed by coming to grips with business drivers and transformation outcomes.

Existing practices and infrastructure need to be considered and the fundamentals put in place—not for overnight transformation, but for a successful analytics journey. The building blocks must be laid with a data-centric architecture and the business and IT working collaboratively toward business transformation.

Research firm IDC predicts that by 2020, 40% of enterprises’ net new investment in analytics will be in predictive and prescriptive analytics. However, for many organisations, this prospect is some way off.

Understanding data is essential before any jump to AI. Looking at three areas—people, tools and infrastructure—must be done holistically, as all parts are interrelated, says Genzken.

“To extract value from data, all three must play together,” he says. “Ideas of what to do with data come from people, and the tools can help realise the ideas. Organisations need to pick the right tools to put change and transformation in place. AI is a tool, but tools are based on infrastructure.”

Legacy infrastructure will continue to have its role.

“You can’t deny legacy—if it’s mission critical, it must survive as part of the new infrastructure,” says Genzken. “But if it is not needed, then you might have to let it die. Those decisions are made by human beings.”

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AI and security
AI is also important to the evolution of security. It is being used to identify hacks and breaches by analysing unusual behaviour on network traffic.

“More than 50% of hacks or breaches are committed internally, but advanced analytics can determine hacks in real time,” Genzken notes.

Sectors not traditionally thought of as intelligence-led are also harnessing AI. For example, companies in agriculture are using AI to innovate.

“The core business has not changed. However, today’s seeding machines are not stupid mechanical systems. They are highly intelligent robots analysing soil in real time,” says Genzken.

Although there is no such thing as an overnight transformation and advanced analytics is a continuous evolution, it is a good idea to explore what’s possible and ensure that processes, IT infrastructure and culture are focused on being data-driven for future innovation and success.

If you would like to find out how big data analytics can help your organisation’s journey toward unleashing the value of information, please visit www.intel.co.uk/analytics.

Find out more about:

Intel Xeon:
https://www.intel.co.uk/content/www/uk/en/processors/xeon/scalable/xeon-scalable-platform.html

Intel Select: https://www.intel.co.uk/content/www/uk/en/architecture-and-technology/intel-select-solutions-overview.html

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