The entire journey of the digital renaissance in business hinges on data and agility leveraged out of it. Enterprises – specially those that have been successful in their transformation projects – have embraced the power of insights radically in the last few years. They have realised the significance of using data for business advantage. They have found compelling uses possible out of even the most latent or unstructured or forgotten data all across their environments. This is where the role of analytics and data tools becomes profound. It is through the right approach to data gathering, storage and processing that one comes to the next frontier of digital advantage – the one we better know as analytics. And analytics is a different lever altogether – it is equipping enterprises to achieve customer affinity, engagement, innovation, personalisation, and efficiency – with clever and creative solutions, all made possible through smart use of data.

That’s why business intelligence and analytics products are truly empowering our decision-making. They have been evolving in an interesting way all these years – as descriptive analytics and predictive analytics.

How AI and ML can boost Predictive Analytics

When we think of predictive analytics – we know we are talking of something that will take us from ‘what is’ to ‘what should be’. It is a stripe of analytics that can crunch and chew historical data; apply statistics; and offer predictions for likely future outcomes through smart modelling and analysis. Do we know what actually powers this special quotient?

Using intelligence and machines that learn and keep learning – that actually adds a lot of weight to the word ‘predictive’ in predictive analytics. When AI and ML take charge of the landscape of data, they stand out immediately because:

  1. They are adaptive – and I mean, quite adaptive. Throw them inside any pool of data with some basic parameters and models- and they will be completely at home
  2. They are versatile – machines can read everything, from unstructured data to images to faces to sentiments
  3. AI and ML are designed to read anomalies too – that’s where they pick up the best insights
  4. AI and ML are amazingly-comfortable with complexity. The scale or mess of any data pile does not boggle these models. They can survive, and thrive, in this maze and come out with unexpected answers
  5. They can adjust, and respond to, the ever-changing nature of data and give real-time action insights
  6. Machines can keep re-orienting their models and parameters to the needs of actual-user scenarios and data-flux
  7. They offer better accuracy. They are good at categorising data in the right buckets, understanding causal relationships, picking the complex dance between various dependent and independent variables, and spotting patterns that evade the humans easily
  8. AI and ML are fluent in speaking well in all kinds of data, verticals, queries, legacy-burden levels and quantum of data
  9. Easy to scale up and down, in and out – that makes AI and ML good investment bets
  10. And of course, they pack a staggering compute-muscle. They can take in as much data that comes in and can still work like a fine-toothed comb

Also Read: What is Image Analytics + 41 Use cases in Marketing, Oil & Refineries, Infra, Manufacturing, Agriculture, and BFSI

That’s why a lot of enterprises have started finding quicker, and more precise, answers to questions like – Will this product’s demand show an uptick in the next week? Are my customers satisfied – from what their sentiments are telling? Are my employees motivated and happy – from what their thoughts and facial communication is indicating? Should I be ready for a certain machine to break down in some corner of my factory – from what that sensor just beeped? Should I phrase my social media message in a new way – based on what the emotion-analytics is hinting?

Yes, from refineries to insurance players to inventory managers – I can see a lot of my peers using AI and ML to turn data into predictions, and to convert predictions into positive action. Be it customer analytics or language analytics, risk analytics or sentiment-analytics – AI and ML have started delivering strong value of prediction to every stripe of data.

Future of Predictive Analytics with AI and ML

The beauty of AI and ML is not just that their analysis is fast, contextual, deep, actionable and reinforcement-ready. Their brilliance lies in the way we finally let machines help us with what they are best at – a good algorithm can dive faster and deeper into the chaotic ocean of data without getting lost. It wanders like a true explorer. It picks out patterns and correlations and variable-dynamics and all that. Machines can also learn from what they were good at – and from the mistakes they made. The swift and reinforcement-based nature of learning here is impressive and usable.

AI and ML have barely started scratching the vast ocean that boggles the human eye and mind. There’s more to come. The more these algorithms and machines learn, the better they will get and that will help us leverage the absolute power of predictive analytics and bolster the on-ground potential of prescriptive analytics.

So far we have explored this realm with neural networks, Natural Language Processing (NLP) and Natural Language Generation (NLG). In fact, a new field of MLaaS (Machine Learning as a Service) is emerging strongly where enterprises can provide data to APIs and get the models they want. AI and ML would be available on Cloud too in easily usable formats and economics. Also, as data continues to expand and explode, AI and ML will get even more sophisticated and diverse. They will have to get ready for a new data-sprawl with IoT and sensors getting bigger and reaching wider than before.

Experts and providers are working hard to iron out the opaque nature of many ML models, too. Soon, we will have models that will stand the test of interpretability, bias-removal and explainability with equal confidence. I can already see new spoons arriving in this cup – as AI is expected to get more democratised, responsible and accessible. Words like literacy, trust, governance, and proficiency will add themselves to the word ‘data’ with a stronger force and reach too.

In other words, we will get rid of corrupt, biased, or stale data and as we do, we will amplify the accuracy, usability, and ethical usage of AI and ML for predictive analytics. The year 2020 was a great glimpse of the use of AI in precise healthcare, drug discovery acceleration, and automated customer-service. This wave will grow bigger and in the right directions – and I feel that strongly.

The future is going to be quite fresh and exciting. Enterprises would be liberated from the chains of chaos, uncertainty, and their own ignorance. They would be able to look back, reflect, anticipate, and work in a pre-emptive mode – all due to the swathe of multi-faceted tools and intelligence solutions in their ambit. It is a future about data, driven by data and with data. Let’s get there soon.

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