Have you ever come across someone who uses the words machine learning and predictive analytics in a way that they sound similar or confusing? Have you pondered over the small, but significant, difference that can exist between machine learning and predictive analytics.

Chances are that you have. Many people in the industry have blurred the lines between the two because they are names that come up in the same set of conversations. They are part of the same team. And yet, they have different roles.

Machine Learning (ML) is a broad basket. It is strong in delivering sharp, and strong, predictive analytics. But that’s just one thing -among many- that it can generate. It has many other end-points and applications. And predictive analytics is just one among them.

Wait, before it gets even more confusing; let’s pick a whiteboard and draw a quick chart.

10 Differences between ML and Predictive Tools

Here are the key difference points between machine learning and predictive analytics.

  1. Predictive analytics is mostly designed to work on historical data and current data. It uses predictive modeling and automation of forecasting to spell a future outcome. Machine learning ‘helps’ to do that. It is ‘one more tool’ to achieve better predictions. It is more of modeling than a report-card or a dashboard. It is where machines learn – sometimes based on data that they are fed and sometimes on their own
  2. Predictive analytics constitutes many tools – statistical tools, data mining, and machine learning. So machine learning can be used for predictive analytics, but it is a broader field of computer science in itself. The algorithms here work on their own, swimming through data, wandering and finding patterns that can help make predictions. Eventually, machines can outpace the instructions that set the direction somewhere because they keep learning and improving
  3. Predictive analytics has a watertight use-case and problem to solve. Machine learning is more of a playground depending on what it churns out. It is exploratory in its DNA
  4. The level of interpretability and human role is higher in case of predictive analytics
  5. Machine learning works on self-defined nodes because the machines can learn on their own. The ability to compute at scale, and in vague environments, makes it a good choice for designing goals of predictive analytics, per se
  6. Machine learning can use regression, correlation, outlier recognition, Naïve Bayes, decision trees, time-series, and neural networks to help with predictions. SPSS, linear models, and Excel are, on the other hand, primary tools employed for predictive analytics
  7. Predictive analytics is the domain of statistics. Machine Learning expands to Mathematics, Data Science, and Computer Science
  8. Predictive analytics needs minimal, or negligible, coding. Machine learning works on programming and Artificial Intelligence (AI) models. It is a form of artificial intelligence
  9. Predictive analytics is designed around ’cause’ and ‘change’. Machines learn and improve based on recalibration and real-time data
  10. Machine learning is more adaptive and complexity-friendly than a statistical tool

The most important difference between ML and Predictive Analytics

Now that we are a tad less confused about the two concepts, here is quick reminder that matters a lot specially when your enterprise wishes to pick a specific path. First, remember that Machine Learning is quick, it can go deep, it is self-driven, it can throw up many combinations and patterns to play with, and it brings accuracy – no matter how slippery and huge the data barrels look like. It can help an organization distill even the most complex varieties of data. It can deliver great answers not just with stable data but with ever-changing and hot-plus-warm data as well – and with equal poise. It will unshackle the predictive analytics part from rigid models and allows one to explore new questions and problems too.

In fact, as per a recent prognosis from ResearchandMarkets that I read, the global predictive analytics market size can jump from USD 7.2 billion in 2020 to USD 21.5 billion by 2025. And a major factor contributing to this is the rise in adoption of big data and artificial intelligence and machine learning technologies.

We can keep doing predictions the old way – with sheets, and calculators and graphs. Or we can do it the new way; the smart and intelligent way – with intelligent machines. The key difference would be that this time we would rely on something that is not afraid to say it knows nothing to begin with. We would have a tool that will keep learning with every new byte of data that goes in. All that ultimately translates into predictions that are precise, well-timed, usable, and contextual. But there’s one more thing worth considering. Machines help us think of better questions and of new possibilities that were never on our minds to begin with. This is so much more than prediction. This is a compass. This is direction.

I am definitely excited about this main difference above everything else.

Getting Started with ML-enabled Predictive Analytics

Let’s be pragmatic about our constraints and our possibilities. There would be some decisions where human learning will always outshine machine learning. Intuition vs. Insights. Context vs. Speed.

But do not gloss over the fact that your competitors may already have adopted Machine Learning for acing their game in predictive analytics. So make it quick. And remember two things as you go ahead. Invest in good architecture, tools, partners, data preparation and quality. Also consider well that most executives (37%) do not understand how cognitive technologies work (A Deloitte survey ‘AI for the Real World’) and some (18%) think that technologies are oversold in the marketplace. They also face integration issues and scaling-up obstacles when they think of investing in AI.

Having clarity on what you want and what your ultimate goals are – this is critical in choosing the right approaches for your business and IT investments. It is a great time to be in this space because this industry is buzzing with new breakthroughs, innovations and ideas every day. You have a lot of choice. Just go ahead with a clear map and some courage to support this journey. You would be able to write your own future, instead of just predicting it.

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