Is your head spinning around IoT, AI and ML?

All these new buzzwords put "big data" to shame!

Is your head spinning around IoT, AI and ML?

Remember all the buzz around “Big Data” just 5 years ago? It was all the rage in the tech industry and you could barely read an industry article or interact with a tech vendor without a reference to “Big Data”.

That early hype eventually led to a maturing market and big data is now a proven reality with IDC forecasting sales to reach $187 billion by 2019. That’s quite a staggering amount especially when you compare it to Gartner’s estimate of the Business Intelligence and Analytics market being $16.9 billion in 2016.

Nowadays, there’s a whole new set of technology trends which are dominating the technology landscape similar to the way big data dominated things just a few years ago. You know what I’m referring to: hot trends like Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML). Gartner does a nice job of plotting these trends in their annual Hype Cycle for Emerging Technology report.

So what does this all mean as it relates to the business intelligence market? First, it’s important to clarify what each of these concepts are since there’s often confusion.

IoT:

According to this Business Insider article, Internet of Things can be defined as “A network of internet-connected objects able to collect and exchange data using embedded sensors.” For me, the easiest way to make it real is hearing about actual customers who have successfully deployed IoT solutions. There are more and more business intelligence IoT case studies popping up but here are a few interesting ones:

  • Rentokil Initial: Leading pest control company deployed connected digital sensor devices to deliver new levels of proactive risk management against the threat of pest infestation – for instance, mapping weather patterns with rodent behavior or tracking swarms of insects as they cross territories.
  • British Gas: Leading utility company has deployed over one million smart meters and is targeting 16 million by 2020. The data lake contains more than nine billion records and takes in data from sources including over 150 SAP tables. British Gas managers can now find out “what the company isn’t doing well and can change”.
  • Mesur.IO: Innovative agriculture industry startup who places sensors to correlate and measure water consumption, irrigation patterns and weather impact. This enables cost savings related to reduced water usage and also helps conserve resources through automatic ordering of fertilizer, seed and water.

What the heck do #IoT, #AI & #machinelearning mean? Here are some real-world examples!

AI and ML:

Well known big data author Bernard Marr distinguishes between Artificial Intelligence and Machine Learning in this Forbes.com article from December 2016. He defines Artificial Intelligence as “the broader concept of machines being able to carry out tasks in a way that we would consider “smart” and Machine Learning as “a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.”

A really compelling implementation of Artificial Intelligence is from a technology vendor named Narrative Science. They specialize in Natural Language Generation (NLG) which is subfield of AI. The solution transforms data into narratives and can be easily integrated into business intelligence solutions such as Qlik. Check out this slick demoand the product can be downloaded in pilot mode for free.

Machine Learning can be further broken down into “supervised machine learning” where the program is trained based on pre-defined examples versus “unsupervised machine learning” where the program is given data and it outputs patterns and relationships. Machine learning also covers areas such as classification, clustering and attribute selection. Check out this summary from Toptal if you are interested in going deeper.

Here are a few familiar and interesting real world examples of Machine Learning:

  • Amazon.com’s “Featured Recommendations” which is based on prior browsing history
  • IBM Watson which is a question answering computer system capable of answering questions based on machine learning technologies
  • Google Cars uses machine learning algorithms to create models of other people on the road
  • Predictive Analytics integration with BI software. This is more and more common and examples such as RapidMiner with Qlik and R with Qlik are increasingly being deployed.

It’s undeniable that IoT, AI and ML are here to stay. The pace of innovation is staggering and things that seemed impossible just a few years ago can now be demonstrated and implemented with relative ease. My suggestion is embracing this new and exciting wave of technology innovation and seek ways to leverage it for your competitive advantage. Good luck!

Comments

Learn more about how Qlik can help your business.

Follow Qlik