Machine learning (ML) enables artificial intelligence (AI). ML is simply a way of achieving AI. Programmers from the past figured out a way to make the machines train themselves by not explicitly coding them. But the machines weren’t as fast as we would have liked them to be. Now of course, due to the benefits that tech advancements in big data processing, computational powers, faster algorithms, etc. have brought in a way to harness the power of ML at a supreme level.
Machine learning has created masterpieces, earliest of which I can recall is IBM’s Deep Blue that beat the then-chess-world-champion in the 90s’. Within a period of a little more than a decade, IBM developed Watson (which defeated Jeopardy champions). And anybody who is up-to-date with the tech world is definitely be aware of Google’s AlphaGo that beat the world champion in the ancient board game of Go. But is it all play and no work? I dare say No. Machine learning has an innate capability to vastly revamp every enterprise there is out there.
ML For Your Business
1. Cognitive Storage
At a storage level, data can be difficult and expensive to manage. Big data storage is associated with the EPL process – Extraction, Processing and Loading. Companies want a more efficient and cheaper way of doing this. What if we have a system that understands what data should be stored and where based on its relevance and importance? And is ML the way to achieve this? Why should we limit ML to only processing and performing operations? Why not store data using ML’s exceptional features. IBM is already studying a way to achieve this. Creating a dynamic, efficient and most importantly an intelligent storage system that stores and transfers data between storage tiers (based on its importance) is the way to achieve this novelty. Let’s term this as ‘Cluster Based Cognitive Big Data Storage’.
Let’s consider ‘time’ as a major constraint. The figure shows the value of data in different categories over time – The value of Business Data varies over time. Data with an Expiration Date has value only until it is considered valid. The final category of Must Keep data has a constant value regardless of time. So for instance, we can store this data at a tier with high security and encryption capabilities but not necessarily on the tier with the highest processing power. Enterprises can benefit from this logic with ML at their expense.
2. Intelligent Business Processes
Human intervention is required for most of our business operations. But with Machine Learning, this can be avoided. Repetitive tasks such as going through expenses to match tally, scanning hundreds of emails and applications for hiring, etc., all of this can be managed by machines now. ML has features of pattern recognition and decision-making to achieve better efficiency than even humans. If IBM Watson can figure out ways and patterns to beat a world champion in a complex board game, a machine with similar understanding can help your enterprise save time while your employees work on more productive and innovative ideas. Imagine the level of intelligence your business can achieve with such high functioning smart processes. No doubt this shift might cause an impact on an immediate understanding (with employees and clients) and even the finance of the company. But investing in ML processes right now will prove to be a success in the long run. It is the same game with just new rules.
3. Predictive Reasoning
A system that provides insights to a company is currently a theme that most companies are adopting. ‘Business Forecasting’ is a more appropriate term for it. Businesses have leveraged on their past large volumes of data to figure out ways of predicting and then prescribing what step to do next. An enterprise driven with insights is characterised by prediction, optimization, automation and continuous learning – All of which can be achieved with Machine Learning.
Accenture has already ventured into this insights-driven work culture. And it is making the most of it. Accenture has eliminated 90% of their repetitive, time-consuming steps previously needed to create new analytics and AI models. They claim to providing newer intelligence in days / hours rather than months. This is a brilliant feat for an MNC. Their goal is to outpace the change that is occurring at this very moment in the world of analytics.
Entrepreneurs have a certain vision for their company. Creating an intelligent ML-Driven enterprise is in most of their minds. This can be achieved only with a collaborative employee support. And people responsible for these changes in their enterprise need to make sure the right support is given to all departments for a smooth ride to creating an Intelligent and Smart Enterprise.