This is one of my favourite things to do every year. Once a year, I sit down with my team and we talk about what will happen in 2018. The reason why it excites me so much is that the CluedIn team are fantastic at looking past trends and marketing hype and go straight to the business value. We are also very cognoscente that time flies and most companies can’t be too bold in their adoption rate of new techniques, new databases and new software. Therefor, we boiled this down to only 3 trends that businesses will “need” to adopt rather than give too many options as to what companies “should” adopt.
Number 1: Practical Machine Learning
Machine Learning is only accessible to companies that have good quality, enriched and verified data to do so. This will be the year where companies will need to embrace breaking down company silos and wrangling the data in this into quality and accessible data. This will be helped by the enforcement of new EU Regulations that will fine companies if they don’t have their data wrangled. This will unleash companies to use Machine Learning practically, knowing that their data is of the quality that is necessary to get value our or Machine Learning. It will be important that getting data is accessible and efficient instead of having to talking and maintain pulling data from many different datasources.
With platforms like Azure ML, Google Machine Learning, Amazon ML, RapidMiner, IBM Watson and more, the importance in practical machine learning will be utilising platforms like this to bring Machine Learning into the natural workflow of your workforce.
Number 2: API’s need to talk to other API’s
The second is all around API’s. API’s are everywhere, they just aren’t talking with each other. So the next prediction is that API’s will start to talk to each other in a meaningful way. If a CRM could benefit from knowing if a customer has paid all their invoices before offering some suggestions on next actions, then the CRM will just do so, it won’t need manually wiring or the need for automating business operations. Most importantly, this is not about just moving data around different systems, but rather, smart API’s, that via rules, or Machine Learning, can determine what data needs to be in what systems. You can imagine that if a Machine Learning function required to know about a companies financial status to make a better decision, then it will talk to the required API’s to get this data.
Number 3: The right tool for the right job
This will be the year where single platforms will be broken up into embracing software that is designed to solve the specific problem a company is wanting to solve. With the explosive rate of specialised startups and software, it is important for companies to embrace specialised software to solve their problems. The challenge will be the integration story of how this data works together.