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Machine learning – what is it and how do we move?

We’re at the dawn of what could be a golden age for machine learning. Data science isn’t the premium product it once was; the tools of the trade are more open and accessible than ever, and costs are a fraction of what they were yesterday.

The time is right for firms of all sizes to take advantage. And when deployed correctly, machine learning can be a self-feeding process that powers innovation and business efficiency.

However, preparing your business for the future begins in the present – and the first move is a root-and-branch analysis of your existing data infrastructure.

What is machine learning?

Machine learning means computers expanding their own horizons: they’re programmed to absorb new information and adapt, to “think”. The aim is to have machines looking in new directions on behalf of your businesses, identifying new opportunities to save time, effort and to boost the bottom line.

When set up and maintained correctly, the machine learning process feeds itself: data powers machines which, in turn, learn and understand more. And round and round it goes. Under this dynamic, the innovation cycle can grow exponentially as firms hardcode ever-slicker efficiency and performance into their strategy.

That’s the theory. And it’s not a leap to say that, today or tomorrow, most organisations will want a piece of it.

Yet while there’s a lot of stargazing around what’s possible via machine learning algorithms, there’s comparatively little on how to enable and activate them: how to prepare and maintain pitch-perfect data foundations on which machine learning tools can thrive.

So before diving headfirst into a brave new world, the first step is getting to grips with your organisation’s data culture.

Finding data maturity

Having a level of data maturity is an essential starting point in gearing up to deploy machine learning tools.

A data-mature enterprise knows what data it has and what it all means. A data-mature business holds data that is consistent, with little-to-know duplication, and that is omni-accessible to all in the organisation.

Crucially, a data-mature business has the governance and the infrastructure in place to ensure the mass of live operational data – which flows moment-by-moment through active systems such as POS, social media, customer dashboards and analytics engines – is parsed from the verified and cleansed data on which analysis, strategy and business decisioning is based.

A common mistake in assessing data maturity is the assumption that new data tech equals a mature data infrastructure. That’s not always the case. Evolution in the IT world has massively outpaced that of the business world and consequently some firms work with a patchwork of old and new systems that speak different languages. Such issues and gaps need to be resolved.

Laying these foundations may feel expensive. But upgrading and even migrating to newer platforms is a necessary step – organisations that are too attached to their legacy systems are simply delaying the inevitable while losing competitive ground. To implement machine learning correctly, algorithms need to run on fresh data that accurately reflects business today, as opposed to harking back to days gone by.

Many facets define data-maturity, which can be a relative term. But the key takeaway is that a business with a foundation of organised data can start to build machine learning into its future.

Master Data Management (MDM)

Getting to a stage of data maturity may require a root-and-branch exploration under a Master Data Management project. Such a process aims to, if necessary, reshape, reroute and reorganise existing functions so data can flow and filter with more purpose.

To ensure the longevity of a firm’s data processes, MDM will almost certainly define a set of data governance principles in line with a tightly defined data strategy. Progressive businesses in the digital age know that a data strategy should link to the wider business plan, gone are the days when these were separate entities.

It’s also worth noting that if there’s disharmony between the data and business plan – whether it’s caused by machine or human factors – that needs to be addressed.

An MDM project will likely define efficient workflows and a data warehousing strategy. The data warehouse is the final resting place for a firm’s gold standard data. It is where the cleansed, structured, sorted and validated data rests so it can inform vital business forecasts, trends and decisions.

Data warehousing

For machine learning to be effective, enterprises must utilise data from the greatest possible variety of sources. This includes data which is neatly structured, and that which is unstructured  – such as e-mail messages, word processing documents, videos, photos and audio files.

In days gone by, warehousing ever-increasing volumes of data pushed up storage and server costs dramatically. But with the correct workflows and technologies, filtering can be a constant and dirge data can be efficiently extracted, leaving only organised and business-relevant data to fill the warehouse.

Once firms can get eyes on their gold-standard data – from as far ranging a variety of sources as possible – they are able to assess efficiency and performance in every nook and cranny, exposing opportunities that machine learning tools may be best placed to exploit.

Machine learning as a means to machine learning

The effort to become machine learning-ready sounds heavy, expensive and targid. But really, setting up so you can tap into automated wisdom is now a process that’s actually built on machine learning.

The key starting point is knowing where you are now. Then we can started looking at strategy and platforms, and discussing whether the likes of Talend, RapidMiner and Cloudera are most suitable in getting you from A to B efficiently, in a way that benefits every corner of business now and in future.

The process harbours much potential. But as ever, it must be planned and executed on a foundation of best-practice in data. From start to finish, we can help.

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