Machine Learning capabilities hold great potential for new revenue streams and tremendous cost savings for enterprises.

SwissCognitiveIncreasingly, businesses are using ML to strengthen their competitive advantage and drive innovation. Is your organisation embracing this shift or are you falling behind? If you are on the “bias-for-action” side of the scale and have already started steering your organisation towards digital & ML transformation, are you confident you are doing so in the right way?

Over the past decade, data has become increasingly important and has even been described as the “new oil”. Organisations with extensive user data can leverage data to increase sales and customer retention. Data of machinery can be leveraged to improve machines utilisation of manufacturers. Computer Tomography images can be used to identify cancerous tumours. There is literally no industry segment which can’t leverage data to improve and create new business models. Meanwhile, data has never been easier and less expensive to collect, store, analyse, and share. Many enterprises are building their data lakes today precisely for this reason. But, is your organisation taking full advantage of its data? Are you satisfied with the value you generate from your data? Do you struggle with building smart applications on top of your data lake? Big Data but not enough insights? Too much talking, not enough walking?

If so, consider the following tips:
Be business driven and customer focused: What are your organisation’s biggest challenges? Start from a focused business challenge and work backward towards a solution. Too many companies try to apply “self-driving cars” or “genome-sequencing” algorithms to a sales funnel optimisation challenge just because they hired an expert in this field, while often there are models that better fit the task and bring higher value at lower costs. Don’t keep your data science team in the IT department alone. Rather, giving ownership of the data science team to a business stakeholder can invigorate your organisation, and unlock new revenue streams and tremendous cost savings.

Iterate fast and simple: Be quick and decisive about bringing your ML system into production. Conducting small iterations through tests, proof of concepts and pilots will help your team to bring ML workloads into production faster, and in a higher quality. Plan to have a production-ready prototype in 3 weeks, and a fully operational version in under 90 days. Even if your system is not using the state-of-the-art model, you will learn far more by iterating quickly than you would from an overly-long development cycle. ML transformations happen by building knowledge and experience through small, fast, and simple steps, rather than by multiple year planning. A redesign is inevitable. Only by experimentation, experience, and adaptation, can you realise the full potential of your ML product. Fail fast and improve often.

Centralise or Decentralise ML teams? Centralise ML teams when necessary, but aim to decentralise when possible. ML applications, like any other piece of software, require maintenance, updates, and support.[…]

read more – copyright by