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Over the recent years, emerging and disruptive technologies such as 5G, IoT, AI/ML, Blockchain, RPA, digital transformation etc., have been accelerating and are increasing in demand. It seems, especially with the current pandemic, to have reached a crucial point for the adaptability to the ‘new normal’ with increased acceptance of these new technologies. We have also become accustomed to the expression of data being the ‘new gold’, and for good reason, as the exponential growth of data is said, by 2025, to generate around 163 zettabytes of data (1 zettabyte is 1 trillion gigabytes). Technologies being ‘smart’ enabled means IoT interactions at 4800 times a day with over 100 billion IoT devices by 2025. Hence, technology enablers must be in place to understand the meaning of all this data and its knowledge use cases; especially since over 80% of the world’s data is unstructured. The term ‘big insights’ seems more applicable than big data, and knowledge empowerment is a key output of big insights.

Amidst this explosion of technology that surrounds us, it is important to stop and consider the utility of it all. On one hand, does the new technology serve a substantial purpose? Does it solve challenging business issues? Is it backed by sufficient data and research and is it significantly impacting and positively transforming business? And on the other hand, what are the risks of not adapting to emerging technologies and digital transformation? The investigation of the use cases of emerging technologies should be from a ‘business first’ point of view rather than adopting technology for the sake of technology. Some decisions are simple and are a part of evolution – if we remember dial-up internet and moving to broadband and cloud services – while others require a deep analysis of business challenges and benefits. Before jumping on the technology bandwagon, it is a good idea to do a sanity check to see if you are driving the use cases for technology or if you are being driven by it. Artificial Intelligence and Machine Learning are hot topics in today’s times, however, before adopting them in your business operations, analyze the business drivers for the technology and correlate it with data you have at hand to ensure that there is a definite need to invest in the technology.

As mentioned at the beginning of this article, the growth in data is huge with over 90% of the world’s data being created in recent years, and 10 times that data is going to be created over the next 5 years. It is becoming clear that we need more insights, visibility, discoverability and intelligence to monitor and allow the business to pivot and be agile by understanding this. There is also a clear definition as to what businesses want and how to better differentiate themselves in the market. Since data sets are vast and contain structured and unstructured data, technology is required to assist its enablement and delivery. At Impelsys, our purpose is ‘spreading knowledge through technology’, and to that extent, we are investing in AI and ML technologies to ensure our platforms have services that make content more discoverable through metadata taggers, creating automated summarized content (abstraction) and serve personalized/recommended content to users. These are just some examples of the services being developed in the Impelsys Innovation Lab.

Deploying new tools and technologies come with its own set of complications. Implementation and maintaining these technologies can be solved by partnering with organizations that are institutionalized with good practices such as building tech with privacy and security by design, using cyber security measures and agile methodologies.  Data privacy and transparency are current hot topics and that is why blockchain is being considered and undertaken by utilising certain attributes of immutability, security, audit trail, and developing hybrid models of permissioned/consortium blockchain rather than just the original cryptocurrency use cases. Ethical implications of new technologies should be carefully examined before adopting them, as algorithm bias and AI ethics are a global debate, a set of industry standards need to be established for the same and is currently a work in progress. Information about how the technology collects data, what it does with it and steps taken to maintain data integrity must be specified to the users. A Forbes report (2019) revealed that just the first half of last year recorded 3800 data breaches, exposing 4.1 billion compromised records! And these are just the ones that were publicly disclosed. So, data security and privacy are of utmost importance and is as much the responsibility of each individual as it is of the technology provider. We are growing more sensitive to where we give our data and what it is being used for without our knowledge. There is so much behavioral data being generated about us, without us being aware, and this is set to grow substantially with the growth of ‘smart societies’ and IoT.

One thing we know for certain – as has been demonstrated with the pandemic – technology is going to accelerate even more. It is going to be all about evaluating what set of tools, methodologies and challenges are best met for each business by suitable emerging technologies. We will need to consider the extent of digital transformation that is required, and then develop a roadmap of activity that can be phased in and be agile enough to potentially change and adapt according to how the business and market conditions evolve.

– Stefan Kendzierskyj, Head of EMEA and APAC, Impelsys Inc.