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Companies Pigeonhole Productivity and Profitability Potential with Machine Learning

In this special guest feature, Yvonne Cook, General Manager at DataRobot UK, takes a look at how companies are applying machine learning technology to give themselves a substantial leap forward in their quest for digital transformation. Yvonne joined DataRobot in 2015, taking responsibility for the development of the business in EMEA. She has always demonstrated great professionalism and brings excellent levels of knowledge and understanding of the IT industry to any boardroom. So much so that she is considered by clients to be a genuine ‘partner’ rather than purely a service provider. Before joining DataRobot she held senior roles at SAP, Netezza (acquired by IBM) and Teradata, amongst others.

Previous attempts at predicting future events from historic data met with limited success.  Such efforts were expensive to undertake and slow to produce results, and programs proved fragile as the rules needed to be re-coded as the real world changed. Today, companies across all industry sectors are adopting machine learning to make predictions from their data.

Machine learning represents a significant technology breakthrough for predictive analytics, driven by advances in computing and a growing resource of open source algorithms and software libraries. More importantly, machine learning dramatically lowers the cost of making such predictions.

The emergence of machine learning as a technologically and economically feasible strand of artificial intelligence is timely. Analysis by Accenture of data sourced from Oxford Economics shows GDP has slowed in many large economies. In the 1980s, real GDP growth of developed nations averaged about three percent. Today that average figure has dropped to just 1.1 percent. According to Accenture, AI and machine learning will boost productivity “by enabling people to make more efficient use of their time,” culminating in a 4.6 percent growth rate by 2035.

Have, or have-more?

The rate of productivity improvement is not uniform across the economy. In the Harvard Business Review, Manyika, Pinkus and Ramaswamy discuss a digitisation index that reveals disparities exist between sectors and between companies within sectors. On the USA’s digital progress, the authors note “there are hardly any ‘have nots’ anymore. But a widening gap exists between the ‘haves’ and a group we call the ‘have-mores’: companies and sectors that are using their digital capabilities far more than the rest to innovate and transform how they operate”.

Sectors surging ahead include technology, media, financial services and professional services. Divides exist between companies within each sector, with the ‘have mores’ enjoying greater success than the ‘haves’.

And to make things more difficult for both the ‘haves’ and ‘have-mores,’ the pace of change is increasing. Companies wanting to stay ahead of competitors don’t have time to sit back and learn lessons from others in their sector on how best to put machine learning to work.

Be an AI-driven enterprise

The transition road map starts by first understanding where to apply machine learning. Begin by investigating the internal business processes in order to identify points within individual tasks where staff currently make decisions, and where these could be replaced by predictions generated by machine learning. Once this work gathers momentum, companies can then broaden their focus to investigate opportunities to embed machine learning in new products and services.

Some companies view machine learning as a technical skill requiring the services of specialist data scientists. Such pigeonholing limits productivity and profitability opportunities of this transformative technology. Rather than a technical specialism, set your company a goal to make working in machine learning ‘business as usual’, a technical skill available to all business people.

Our prediction is that attitudes to the value of machine learning will now start to change rapidly.  We have already seen hundreds of executives, managers and business analysts complete our training courses to help them find opportunities for machine learning within their companies. Once they identify these opportunities, they go to work and build machine learning models capable of making highly accurate predictions on their data and embed these in their operations to lift productivity. Machine Learning Automation can apply the expertise of many of the world’s highest performing data scientists to the process of creating predictive models, leaving business people to focus on the work critical to the continued success of their company.

Such automation creates powerful and profound impacts on the business. Embracing machine learning technology will give companies a substantial leap forward in their quest for digital transformation. Now that automated machine learning is so readily available, time really is of the essence. Established companies that move fastest to become AI-driven will take full advantage as they defend market shares against digital disrupters.

Another long-term gain from this approach is that you put your company’s transition to becoming an AI-driven enterprise into the hands of the people with the deepest understanding of your operations, of your products and services, and of your data.

 

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