How emerging technologies are creating a race for insights

IN AN INCREASINGLY digital world, a common denominator for successful organizations is the ability to empower its workforce and customers with the right information at breakneck speeds. With IDC forecasting that at least 50% of global GDP will be digitized by 2021, putting data to good use is no longer merely important, but a genuine prerequisite in order to compete.

This represents both a challenge and a (significant) opportunity: how many companies under financial pressure today are actually sitting on an information gold mine?

Up until now, deriving actionable insights from data has not been as easy as we would all like. According to Forrester, 74% of businesses aim to be data-driven but only 29% are successful — and there’s a good reason for this. Unstructured data — information which is not organized in a predefined manner, and which may therefore be difficult to extract value from — make up 80% of all data, according to Gartner. There are limits to using data analytics to derive insights. With the volume of data being created growing exponentially each year, managing and organizing an exploding store of information in order to derive value continues to grow in complexity and cost — and the more data there is, the harder it is to make sense of it all.

There is light at the end of the tunnel, though. Through emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML), we’re actually finding new ways of turning data into actionable insights, adding structure, automating the processing of data and reducing the need for time-consuming manual analysis. Our ability to interpret data is improving with the application of Big Data Analytics, Data Science, and Artificial Intelligence techniques.

A good example of extracting value from data is how Google Maps and Waze interpret realtime traffic data through users’ smartphones and provide optimized navigation suggestions. This is made possible by the ability to process massive IoT data-sets combined with highly detailed and constantly changing map information. Ride-hailing apps such as Grab, on the other hand, utilize data from the GPS-enabled mobile devices to match drivers and riders in the same area.

Or, take football: instead of manually labelling a World Cup goal with specific information such as which teams were playing, who scored, who provided the assist — plus the time, date and venue — AI processes such as object and facial recognition, logo detection, and video sequence labelling can run these functions pretty much unsupervised.

Businesses are racing to invest in AI technology to improve their ability to obtain valuable insights — quicker. Dell Technologies’ Realizing 2030 study found that 81% of businesses in APJ are investing or planning to invest in advanced AI technology, and that 75% of APJ leaders plan to appoint a Chief AI Officer to further accelerate that growth.

There are a few industries already on a path to success by investing in AI and ML, as they seek to accelerate time-to-insights. According to IDC, the retail and finance industries were the biggest spenders on AI systems in 2017. Health care will soon use AI to comb through vast genome databases and calculate the probabilities of contracting specific diseases or conditions. Meanwhile, the insurance industry is set to be transformed by AI, using sensors to track activity and driving styles and calculating tailored premiums based on an accurate and real-time analysis of individuals’ behaviors and lifestyles.

Investing in emerging technology right now doesn’t mean the challenges of processing data will disappear. Success with AI and ML is predicated on providing the right platform with the right data to generate reliable insights, which often involves a diverse range of information from multiple systems. As data continues to grow and evolve in ways that businesses cannot always fully anticipate, it will require new strategies to ensure infrastructure is able to anticipate and accommodate this.

Another challenge will be to maximize value creation while simultaneously ensuring compliance with an increasing number of regulations, policies and laws governing data. For example, very few organizations will be building their own AI functions from scratch, and will instead leverage technologies and applications provided via a third-party service-provider’s API. It’s important to understand how these APIs are being used, what they cost, and — for instance — how much data of varying sensitivity is transiting the public internet and being temporarily stored in public cloud data centres along the way.

Businesses need to build a strategic and holistic approach in order to navigate this environment of unprecedented change — and to harness the full value of these exciting emerging technologies. That approach has to be built first and foremost on an effective audit of what data is available as well as what new data might become available as a result of new products or services. In addition, collaboration across the business is essential, to accurately establish what value is needed or possible. This factfinding mission will provide a strong foundation, enabling the extraction of meaningful data with the potential to genuinely accelerate the business.

From a technology perspective, the primary principles of effective data management are to modernize, automate and transform core IT processes. A modern, scale-out architecture that can provide costeffective tiered storage, with inplace data protection and analytics, is the core of the modern data centre. With these factors at play, and a high level of automation and data governance, organizations can effectively harness the value — and significant power — of data capital.