Machine learning garners enterprise thrust

Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. In addition, Machine Learning systems apply algorithms to data to glean insights into that data without explicit programming: It’s about using data to answer questions. As such, enterprises are applying Machine Learning to a wide array of issues, from customer purchasing patterns to predictive maintenance.

Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. In addition, Machine Learning systems apply algorithms to data to glean insights into that data without explicit programming: It’s about using data to answer questions. As such, enterprises are applying Machine Learning to a wide array of issues, from customer purchasing patterns to predictive maintenance.

According to research and consulting firm International Data Corporation (IDC), spending on Artificial Intelligence (AI) systems in the Middle East and Africa (MEA) is expected to maintain its strong growth trajectory as businesses continue to invest in projects that utilise the capabilities of AI software and platforms.

Referencing its latest Worldwide Artificial Intelligence Systems Spending Guide, IDC stated that spending on AI systems in MEA is expected to reach US$ 374.2 million in 2020, up from US$ 310.3 million in 2019.

“The AI software applications and AI platforms markets continue to show steady growth in the MEA region and we expect this momentum to continue over the forecast period,” said Manish Ranjan, Programme Manager, Software and Cloud, IDC Middle East, Africa and Turkey (META).

“The use of AI and Machine Learning is on the rise in a wide variety of business applications from ERP and CRM to analytics, content management and collaboration solutions. Many global vendors have started embedding AI, Machine Learning and cognitive applications to provide ultimate business benefits to their users.”

Spending on AI systems in the region will be led by the banking and retail industries. Together, these verticals will account for more than 33% of spending in 2020, followed by federal/central governments and telecommunication industry. “With the growing adoption of various use cases across all industries, organisations are continuing to invest significantly in optimising their business processes, automating their operations and enhancing their products and services offerings in order to maximise the overall customer experience,” said Ranjan.

Looking at individual countries, IDC’s forecast shows South Africa accounting for 20.5% of AI spending in MEA during 2020, followed by the UAE on 19.7%. Saudi Arabia will be the region’s third-biggest spender next year with 15.7% share. Turkey will rank fourth, accounting for 11.1% of regional AI spending.

Andrea Tucker, Head of Research and Development, e4, said Machine Learning has grown to have a significant impact on people’s daily lives, often without even knowing about it.

“Using Machine Learning, enterprises are now able to better understand and process their data much faster using modern tools with established algorithms. Potential outputs of a successful Machine Learning strategy can be powerful and measurable marketing campaigns or more efficient operations or logistics,” she noted.

According to Tucker, firms want to be able to utilise their valuable data, but not at a cost that is greater than its inherent value and Machine Learning allows for this, with the added benefit of more consistent decision making and streamlined operations.

Garnering enterprise momentum

With Machine Learning seeing an increase in enterprise wide adoption in the MEA, pundits say CIOs and C-level executives need to be flexible when deploying new technology.

Adam Pantanowitz, Co-Founder and Chief of Innovation at AURA, said there is no doubt that there is greater accessibility to and democratisation of Machine Learning, an upsurge and excitement around the technology.

Pantanowitz added that the big cloud players have embraced it and made their tooling more accessible.

“The open source community are very active and are providing accessible toolkits. In addition, the scientific community have made some great advancements which allows one to leverage knowledge gained in another domain by another researcher and their resources and move it to your problem domain.”

He said that computation has certainly advanced, enabling more cost-effective access to the requisite processing power (GPUs and CPUs) to enable these applications.

Pat Gelsinger, CEO, said Machine Learning is the most important new workload to emerge in the IT enterprise space in the last 10 years.

Today, said Gelsinger, Machine Learning is seen as being the way of the future by many enterprises as it is deemed an effective and automated means to provide companies with a competitive advantage, driving innovation and ultimately increase profitability.

“The main sectors utilising Machine Leaning are healthcare, travel and hospitality, retail, finance and manufacturing. By using Machine Learning, a business can prevent fraud, provide dynamic pricing, effect predictive maintenance, leverage alerts and diagnostics as well as use real-time information to drive business decision making. This may be achieved by intelligent operations management, from applications to infrastructure, all by using dynamic threshold and capacity planning,” he said.

Secret sauce wares

With vendors often claiming to have some Machine Learning ‘secret sauce’ in their wares that will revolutionise an enterprise’s business how should CIOs and their IT teams go about selecting the right tools and systems?

Dr Nicola J. Millard, Principal Innovation Partner, BT, reiterated that no amount of secret sauce will revolutionise a business unless its data is in order and of high quality.

According to Millard, Machine Learning does not work by magic as it depends on data and if that data is unstable, inconsistent and spread among multiple legacy systems, it becomes more difficult and far more expensive to do. “Aside from data, enterprises also need to step back and ask what problem they want Machine Learning to solve and whether it will actually solve it. For example, if they want to deploy a chatbot to improve customer experience, does it actually improve it or just add another level of frustration for customers if the ‘bot’ hits a dead end and abandons them if it can’t understand what they want,” she said.

Enterprises need to understand how Machine Learning will integrate with legacy systems, processes like in the contact centre, how much training it will require and what the business case is given that return on investment (ROI) may take some time.

Tony Bartlett, Director, Data Centre Compute at Dell Technologies South Africa, agreed with Millard and adding that: “Firstly as with most systems, enterprises are advised to look to vendors who can provide an end-to-end solution. Machine Learning systems are part and parcel of a broader business strategy, which includes Digital Transformation, cybersecurity, Edge Computing, automation and data analysis to name a few.”

Bartlett said enterprises should consider whether the vendor they rely on for Machine Learning solutions has the technology depth, breadth and specialisation to meet their requirements whether it be at the Edge, core or cloud.

“As the volumes of data increases and systems are tasked with processing more data, in real time, IT systems will need to depend on high performance computing to keep up with the demands and technologies such as GPU’s, FPGA’s, high speed memory and storage are essential in deriving outcomes in real-time,” he said.

Data quality

With data and AI playing a crucial role in any Machine Learning deployment, experts are urging CIOs and business line executives to ensure data quality.

Ramprakash Ramamoorthy, Product Manager, ManageEngine Labs, said your model is as good as the data you train on and spurious correlations, biases, data imbalance present in your data can adversely affect the quality of Machine Learning model’s prediction.

“Organisations should track datasets and make sure they comply with local regulations and are free from inherent biases. Sometimes labelled data availability would be a challenge. For example, there are practically no commercial grade labelled datasets for service desk sentiment analysis, but you have a wide range of datasets available for e-commerce product reviews, hotel reviews and movie reviews,” he said.

Ramamoorthy pointed out that transfer learning techniques can help bootstrap your small service desk dataset with the learning from the larger consumer datasets and whatever process used to generate and label datasets, will have to run fairness checks before deployment.

Getting business buy-in

Most CIOs and IT leaders have faced resistance from their C-level peers when securing buy-in for a Machine Learning system deployment in their organisations.

BT’s Millard said that CIOs and IT leaders need to ensure that the wider business doesn’t get carried away by the hype around AI and Machine Learning.

“They need to work with the wider business to identify where deployment of Machine Learning is appropriate, likely to deliver ROI and above all deliverable. There may be a need to completely re-engineer legacy IT infrastructure, data and processes. CIOs and IT teams need to engage with the rest of the business to gauge their appetite to do this and ensure that investment budgets are realistic,” she advised.

Rudeon Snell, Senior Director: Industries and Customer Advisory, SAP MENA, believes it is imperative to lead with business value and AI is a tool that can be used to drive business outcomes that matter to the organisation.

“By leading with business value and showcasing how potential solutions are able to impact the metrics that matter to the organisation, whether they are internal or external AI solutions, it will provide CIOs and IT leaders with the credibility among their peers for how technology can drive business differentiating capabilities that move the organisation forward,” he said.

e4’s Tucker said taking the organisation on a journey with the CIO together with the entire IT team makes these more likely, but it is never a given that everyone is going to buy into a Machine Learning solution.

Ramamoorthy said given that Machine Learning is a subset of Artificial Intelligence, introducing AI in a staggered manner would help better understanding of the advantages and pitfalls that AI has to offer.

“Most CIOs will have the challenge of retrofitting AI into business systems that have existed over decades. Having an explainable AI model would offer further easing on the transition into Machine Learning,” he said.

Michael Cade, Senior Global Technologist, Veeam, believes that demonstrating the value of these systems is paramount, as is understanding the strategic advantage of improving business performance by complementing human intelligence.

“Reliability and automation, underpinned by robust and secure software, provide a very compelling case,” he said.

Tony Nkuna, Solutions Consultant, TechSoft International, said that Machine Learning is much a technology fit as it is a business vision fit.

According to Nkuna, Machine Learning only works when it is married with the future view of the organisation.

“As a CIO, you need to look at the value of advanced analytics and how Machine Learning enables this to extend or promote capabilities in your business. Because it is a relatively new technology, I would suggest companies look at what the research giants like Gartner, IDC and Forrester have to say about who are the leaders and who have the capabilities you are looking for from a Machine Learning system,” he said.

When it comes to cybersecurity, time is a crucial element as it is important for the security measures to work faster to keep pace with the hackers and all kinds of cybersecurity threats.

Ashley Lawrence, Regional Sales Senior Manager, Africa and Israel, SonicWall, said: “This is exactly where AI and Machine Learning-based tools really excel. To deal with the cybersecurity threats of the future, businesses need to embrace AI and Machine Learning-based tools and security mechanisms. They also need to have a solid understanding of how Machine Learning-based algorithms work and how they can enhance security.”

Mohammad Jamal Tabbara, Senior Solutions Architect, Infoblox, added that Machine Learning can help enterprises in multiple aspects of the business processes, whether directly or indirectly by adding agility, precision and intelligence.

He said enterprises with Machine Learning embedded in their decision-making processes have an advantage against their competition relying on traditional technologies.

Given the impact AI technology is having as it continues to be widely deployed within organisations in Africa and globally, it can only mean that Machine Learning technology will start seeing enterprise wide momentum and will integrate with other processes, tools and applications to tackle bigger problems, introduce game changing capabilities and accelerate a wave of transformation in both consumer, public service and enterprise arenas.

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