The Magic Triangle of Data: Machine Learning, Automation and the Human Force Driving Data Science
It is no secret that technology is constantly changing. Although industries face an escalating tide of technological disruption, they all too often focus on disruption challenges. Instead, they need to embrace the opportunities that disruption brings.
Data has become a real game-changer. According to IBM, 90 percent of all data has been created inside the last two years. The ways we share, analyse and absorb information through technology have now exploded to the point where Big Data’s usage is far more commonplace. For many businesses, it has become the crux of their model.
Against this backdrop of constant change, a power trio of technologies has emerged: machine learning, automation and self-service human-lead data science. All three have their benefits and their place and understanding how each works individually will help construct a broader picture of how abundant data can help industries as well as enable companies to benefit from the big data boom.
Machine learning is, in essence, exactly what it says on the tin. It is a system that is taught to learn from experience. It consists of three primary components:
- Model: The system that makes predictions or identifications
- Parameters: The signals or factors used by the model to form decisions
- Learner: The system that adjusts the parameters — and in turn the model — by looking at differences in predictions versus actual outcome
With these three mechanisms working together, machine learning systems are able to output reliable, valuable, and consistent analysis.
It has become incredibly valuable because organisations are dealing with vast and rapidly growing volumes of data. Some estimates suggest that the amount of data globally doubles every two years. Attempting to find insights and value from data can be very difficult. Therefore, one of the main appeals of machine learning is that the complex algorithms do the hard work for you.
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Automation within the context of data is defined by the need for machine learning models to have repeatability, reproducibility and refreshed models to provide predictions. This automation starts with fixing pain points for workers that are spending large proportions of their time in Excel and many hours each week simply rebuilding spreadsheets once new data arrives. In these common situations, automation allows for a lengthy manual task to be condensed and automated into a few seconds. This, in turn, enables anyone handling large amounts of data to spend more time running analyses and focusing on critical business outcomes.
Human-Driven Data Science
Automation and machine learning, however, have one primary limitation: context. While automated analysis is extremely effective, it is hollow without knowing how and where to apply these learnings most efficiently, as well as understanding what may have caused the trends found. This is where people capable of both analytics and critical thought come into their own. The people who wield this power come in two distinct forms: firstly, data scientists, who are simply defined as those who work in the field of data science. Interestingly, there is an emerging group of people who have become known as ‘citizen data scientists’, i.e. those who spend time crunching data in a non-data focused role. These people have been empowered to partake in data analytics by making use of accessible (generally ‘code free’) self-service analytics platforms.
Traditionally data scientists have worked in self-sufficient teams, but this is beginning to change as data has a larger and more central role throughout the business. Many of the tasks required are long and repetitive. The emergence of self-service, code-free data platforms have elevated people’s abilities at all levels of analytical awareness. The ever-increasing number of people working in data science has meant that business and lead data insights are becoming more common within short and long-term strategy that ML cannot entirely conduct.
The Magic Triangle
When all three fields of data analytics are combined, there can be an unrivalled added-value for business. The problem of using only one of these big three tools is that you will be limited in your progress. Citizen and specialist data scientists are unable to perform thousands of rules per second, while automated systems struggle with joining the dots.
Getting ahead of the competition has always been a primary focus in business but now that big data is a major player, companies are drawing on elements of this ‘magic triangle’ and beginning to adapt in a very rapid evolution; gaining valuable insight and uncovering deeper understandings of customers, markets, and the wider world.
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