Linked to my article is a simple Question and Answer article with some valuable insights into Machine Learning. It looks at the advantages together with potential dangers in not taking it as a strategy.
The commonality to the journey of any data led business, which is the same as with that of good Business Intelligence (BI) and Big Data /Analytics strategies is that the collection of data for data's sake is a not a good one, without developing a strategy to have a clean data policy.
The drive of any businesses to become more competitive has to be on the basis of putting data as king. Customer data and all other data that follows. This is no longer a luxury and needs to be the focus of all good business strategies. Ignoring the rules of a good data management policy can land companies into massive fines. The new GDPR rules starting next year with fines of 4% of global revenues or 20M Euros is a good starting point on why this needs to be done.
Companies with many siloes of data will find it extremely expensive if they want to move to one large traditional database to enable good data management and may need to start on the journey of using a big data strategy instead. The key as stated earlier, it that just throwing unclean data into a 'lake' means that it will become very difficult to get the best of data. It is not a surprise that according to Gartner only 40% of big data strategies are successful. As a company delivering some of the first live big data banking projects initially in mainland Europe in 2010 and 2011, we have extensive experience in knowing what a good big data journey looks like. Our track record unlike our competitors is a 100% success rate.
Any Consultancy offering to come in with a 3 year program is effectively wasting your time as any big data strategy needs to start small, a focus needs to be on what added value will be derived from the process and an iterative process needs to be followed. The key for any business is to have stakeholders that really understand what the journey could look like and that they understand the limitations. Too many changing stakeholders is often a recipe for increasing the risk of success. Any Consultancy engagement without a good track record of delivering successful projects, could mean a higher risk than there needs to be - some of the biggest names out there may fall into this category.
The data journey is one from BI to big data, to advanced analytics to machine learning to artificial intelligence. It can include RPA - Robot Processing Automation (a sort of half way house for removing the need of repetitive and boring manual 'human' work and alternative to employing a lot of far shore staff with its risk to data movement and accuracy). At everis we developed everisMoriarty http://everismoriarty.com/ It was developed by our Centre for Cognitive Development to support the machine learning and AI journey. This is being used by Banks in support of areas such as GDPR and other areas. Other practices include big data and RPA. Our everisNext holds a database of 2M start-ups and is free to use https://everisnext.com/ and the largest global database of it's type. Feel free to contact us directly for more information.
Machine learning has been the next big thing for a while now: this can be seen from the evolution of related tags on my website: I have been talking about this for some time, companies are engaged in it are undergoing major acquisitions, and network giants like Google, Amazon, Facebook, Apple or Microsoft are reorienting all their strategies around the issue. We have gone from seeing an algorithm as something with more computing power, more mathematical brute force than a person (Deep Blue beating Kasparov) to seeing it as something capable of understanding human language better than many people (Watson winning at Jeopardy) and able to do things that no human has done thanks to deep learning (AlphaGo winning the world Go championships) or even to make better decisions than a human in situations of imperfect information (Libratus winning poker).