FEATURE : MACHINE LEARNING
with any technology purchase , an outcomes-based approach is also essential in keeping things on track . Is your organisation able to benchmark these outcomes in advance from existing use cases , for example ? A focus on the long-term business impact and a direct impact on productivity are two key metrics to assess here .” need to address non-IT stakeholders ( especially members of senior management ). They can do so by telling persuasive stories that illustrate the impact that the investment in emerging technologies such as Machine Learning will have on multiplying business value , especially on profits and revenue .”
Rick Rider , VP , Applied Innovation , Infor , said endless compute resources is the reason Machine Learning is seeing wider industry acceptance now . In addition , Rider said it ’ s actually more about giving users the ability to use Machine Learning without building out or connecting technologies for years . “ Now we have platforms that allow quick experimentation and implementation so efficient ROI is quite real with the right vendor ,” he said . “ Some industries
IN ADDITION TO PROPER COLLECTION OF DATA , ENTERPRISES ALSO NEED TO MODEL AND PROCESS THE DATA TO FIT THE ALGORITHMS THAT THEY WILL BE USING .
tend to be more progressive , such as industrial manufacturing , distribution and more . However , even within certain industries it ’ s more about the companies who aggressively embrace new opportunities and technologies that succeed . Those are companies that tend to continuously find new ways to pivot and expand their business , regardless of industry .”
Getting business buy-in
Stephen Gill , Academic Head , School of Mathematical and Computer Sciences , Heriot-Watt University Dubai , said to remain relevant and competitive , a CIO must adopt two positions within their organisation : guardians of infrastructure and digital catalysts of business value . Gill said as Machine Learning and AI continue to transform businesses across a myriad of sectors , organisations are gradually starting to see their huge potential . “ As with any initiative , stakeholder support is key for its eventual success and that is why so many CIOs focus on creating solid , evidence-based business cases for the technology investments that they want the management to approve ,” he said . “ While quantitative-empirical communications could be influential for other IT colleagues , CIOs also
He explained that being a good storyteller and a salesperson might not come naturally to a CIO who has risen through the IT and engineering ranks hence , it is important for them to develop such communication skills from their counterparts in sales and marketing , in order to gain the management buy-in and support needed to deploy their digital initiatives .
Dell Technologies ’ Richmany said companies today know they need to increase their investment in new technology , however , they are hesitant of change . He said executives may not be fully aware of the benefits Machine Learning technology can provide to the business .
“ Therefore , before making a case , CIOs and IT decision-makers must analyse the organisation ’ s vision and goals , and look for the IT solutions that will support in achieving these goals . They must make the case for Digital Transformation through a mission driven and business value perspective , which will allow key decision-makers to develop a better understanding of what the business is investing in ,” he said . “ This could include a focus on revenue generation , profitability , as well as employee efficiency and productivity . CIOs must also take security into account at every step of the way and have a solid security plan in their proposal .”
Challenges enterprises face
Priyanshu Vatsha , Intelligent Automation and Pre-sales Consultant , Proven Consult , said technical challenges associated with Machine Learning systems are majorly related to data . Vatsha noted that data unavailability , noisy , redundant or inadequate data makes it difficult to achieve satisfying results . “ Problems also arise if input data is biased or encrypted . Ongoing validation is an additional challenge for the implementation of Machine Learning models in practice ,” he said .
Vatsha said coming to non-technical challenges , building user trust is a big one . “ Users need to rely on them when facing the challenge of making important decisions . Legal requirements also often pose a significant challenge for a Machine Learning project . This relates to data privacy protection as well as decisions on who is going to be accountable for false decisions based on Machine Learning models .”
Dell Technologies ’ Richmany said Machine Learning is still in its very early stages and even at such an
36 INTELLIGENTCIO AFRICA www . intelligentcio . com