Machine learning represents an exciting new opportunity for business owners and entrepreneurs to streamline operations, optimise customer experience and free up their human workforce for more profitable uses of their time. Unfortunately, the intricacies of the technology mean that many CEOs, CIOs and COOs are left scratching their heads about how to implement a functional, successful system in the first place.
In order for a machine learning system to operate at its peak capacity and offer the best insights, it needs premium raw data directly from the client base. However, that data often remains inaccessible until the system itself is up and running. Which comes first? The algorithms inside a machine learning platform which analyse, automate and provide predictions? Or the invaluable data which drives the learning curve? Although confusing at first glance, the answer may be simpler than imagined.
Know your goals
First thing’s first – before beginning any implementation of a machine learning system, it’s important to establish exactly what its intended aims are. The objectives will certainly have an impact on the types of algorithms needed to stimulate the system, which in turn may have a knock-on effect on the kind of data required.
For example, a travel agency which craves the dates, days and times when its clientele is most susceptible to promotions will welcome reams of information regarding social media activity, spending habits and TV schedules, but what customers are eating for lunch and where they get their hair cut may have little value. In this way, knowing which outcomes you seek before setting out is all-important.
After establishing these objectives, consider whether machine learning is really necessary to achieve them. By definition, machine learning involves autonomous and ongoing evolution of a computer model as it predicts and mimics human behaviour through the analysis of data.
This sort of sophisticated structure is not always necessary for every task and the time, effort and expense of implementing machine learning where it’s not needed (and simply because it’s the Next Big Thing) can be a step backwards. For example, linear regression uses artificial intelligence in a much less demanding way by evaluating trends and creating estimates based upon the observable relationship between two variables. For many applications, this might be a sufficient solution.
Clean up your data
It’s a common misconception that the amount of data a company holds is the be-all and end-all of their success. Of course, data is undoubtedly one of the most valuable assets in any business’s portfolio, but it all comes down to the quality of data being stored. Is it new? Is it clean? Is it labelled? Is it organised?
Depending on the industry concerned, data can quickly become obsolete – meaning it is virtually worthless, regardless of the volume stored. For instance, the same travel agency mentioned above might have decades’ worth of records on preferred destinations from years gone by, but if it doesn’t have access to up-to-date information on emerging economies and the latest tourism trends (not to mention regions of political instability), it will fail to make accurate judgements.
It’s extremely rare for a company to suffer from such a dearth of data that they can’t make a start on a workable machine learning system, since even a small amount can be enough to get things up and running. Over time, the self-perpetuating nature of the beast will ensure that the model improves as the data does, so that before long both have improved beyond all recognition just from virtue of being in operation.
One of the biggest obstacles to taking that first step can be segregation of the data beforehand; with many companies storing their data in isolated silos, the effort needed to integrate it can seem insurmountable. However, this should be viewed as an incentive to undertake machine learning, not a deterrent, since the drawing together of different data sources is almost an end in itself. Now, with the added benefits offered by machine learning, there is real motivation for driving that change.
Coming out of its shell
The role that technology in general and artificial intelligence in particular continues to have on business is one that develops every day. In this sense, machine learning can be viewed as a microcosm of that relationship: always evolving, always improving, always learning. The benefits it can bestow on the productivity, efficiency and, above all, bottom of line of a company should not be underestimated – but neither should the effort involved in its implementation. Given the right treatment, machine learning can completely transform your business, but it’s essential to ensure it is the appropriate move for you.