The march of the machines is upon society: the vast power of machine learning has been freed from the confines of IT laboratories and is disrupting businesses across the world, delivering deep insight, accurate predictions and detailed learnings that have the potential to make huge transformation.
But there’s an issue: these immense, invaluable insights are wasted unless business leaders actually apply them. All too often – in fact, 90% of the time, according to one data scientist – these insights are uncovered but not applied across organisations. Businesses neither understand nor trust the outcomes, and may not have the budget or the skillset needed to make real changes.
>See also: What is machine learning?
The findings of machine learning have the power to transform our behaviours, society, healthcare, economy – even our understanding of our planet and its climate. But if machine learning is to realise its full potential, the world of commerce has some catching up to do.
AI and machine learning – the difference
AI and machine learning are not the same. AI is technology designed to perform tasks usually reserved for humans. Machine learning falls within AI, but uses simple sets of instructions – algorithms – to generate learnings from structured and unstructured data.
It sifts through large data sets which might include images, text, voice, video, location and even facial recognition data. Machine learning is a way to identify correlations, patterns and trends. From this, it can perform actions or make predictions. So far, so amazing. But why now?
Machine learning thrives on large data sets
Currently across the globe, businesses and consumers collectively produce 2.5 quintillion bytes of data each day – that’s enough to fill 100 million blu-ray discs1.
Analysing this data brings businesses closer to understanding their customers. Enter machine learning. It flicks the booster switch on analytics, and reveals hidden forecasts, predictions and insights using algorithms. It thrives on large data sets – structured and unstructured – and analyses complex data quickly and accurately.
Chances are you’ve come across machine learning without realising. Those ‘other customers bought this…’ recommendations on Amazon are generated by machine learning.
Facebook uses machine learning algorithms to power facial recognition software – that’s how it tags your friends in an image. Touch ID smartphone unlocking and retinal scans in airports are based on machine learning. Autonomous vehicles such as the Google self-driving car use machine learning algorithms.
If you join a gym and never go, don’t expect results – it’s the same with machine learning
Analytics help organisations deliver a superior customer experience, support product and service innovation, and optimise business processes. The evolution of analytics into predictive modelling generated by machine learning can result in a valuable and transformative business strategy.
That is, however, if you let it. Your business must act on the outcomes, not just pay lip service. Just as joining a gym to get fit won’t work unless you actually go, embarking on machine learning and not acting on the results will leave you out of pocket and back where you started.
Most businesses are still in the ‘early adoption’ stage of machine learning. Their barriers to take-up include:
• Organisational culture: perhaps there is a nervousness around machine learning: surely computers can’t reveal insights that humans can’t see? For some, adopting a new, open mind set isn’t easy. It involves thinking differently and acting differently, and requires a major culture shift across a business, including assurances of the positive impact of machine learning.
• Businesses think they know something, then the data challenges this: similar to the above, they just aren’t ready to challenge the status quo.
• Businesses have neither the ecosystem nor the resource within which to integrate the insights they have gleaned: perhaps they operate in silos, lack collaborative tools or lack the skillset to apply learnings.
• Leaders within the business don’t fully understand the outcomes, or their potential impact.
• There is an attitude of ‘That’s nice to know, we’ll leave it there for now’.
• Budget constraints: perhaps budget was allocated on the actual generation of analytics, and no budget reserved for applying it across the business.
Now is the time for businesses to overcome their fear of the unknown, and accept the value of machine learning. Here’s how:
1. Integrate machine learning into your digital transformation journey at the planning stage, giving you longer in the planning cycle to understand and appreciate likely outcomes.
2. Make a firm commitment to understand, embrace, adopt and integrate the intelligence – this might require upskilling teams or recruiting data scientists. Accept the skill gaps you need to fill.
3. Incorporate analytics into your strategic vision from the very top, which will help create an ‘analytics culture’ so that the entire business is accepting of new data-driven learnings.
4. Empower teams: employees must be empowered to adjust practices based on machine learning outcomes.
5. Make sure a ‘data-friendly’ infrastructure is in place: can teams collaborate and share data easily, or do they operate in silos? Putting the tools in place here will make it easier for teams to work with, and benefit from, machine-learning generated behavioural and predictive analytics.
6. Stay focused: identify what you want to achieve from your outcomes, even if you don’t know precisely what those outcomes are yet. You might want to drive changes in your customer experience strategy, for example, or reduce time-to-market for a particular set of products.
7. Ask for help: “Help me understand this, and what it means for my clients/business/team”.
8. Roll out small-scale projects initially, acting on the information you’re given in a low-risk environment.
An ‘intelligence inertia’ towards machine learning outcomes will be the equivalent of the comet that wiped out the dinosaurs. Businesses must take time to understand its immense value to the business.
Source: statistics from Vcloudnews
Sourced from Andy Berry, VP EMEA Software Solutions, Pitney Bowes