Business Intelligence – (Part 2 of a series)

Big Data, Internet of Things, Machine Learning, Digital Automation, Artificial Intelligence (AI) and The Fourth Industrial Revolution – Part 2

You can find part 1 here.

Internet of Things: 

Part 1 of this series covered big data and obtaining access to data across traditional business silos. What I did not address, is why? It is simple! Better decisions are made based on facts or data. The technical jargon for this is Business Intelligence (BI). So I’d like to take you on a short side trip to provide you with enough background to move onto what part 2 of this series is primarily about; Internet of things (IoT).

The Definition

Business Intelligence (BI)

Wikipedia has the following to say about Business Intelligence (

Business Intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information.[1] BI technologies provide historical, current and predictive views of business operations. Standard functions of business intelligence technologies include reporting, analytical processing, analytics, data and process mining, business performance management, complex event processing, benchmarking, text mining, predictive analytics and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities. They aim to allow for the straightforward interpretation of big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.[2]

This definition in its own adds a whole lot of jargon but before you panic, this is the modern view of BI, old BI was just merely data analysis.

Data analysis also referred to as 1) analysis or review of data, or 2) data analytics. It is the complicated process of inspecting, cleansing, transforming to modeling data with an aim to discover useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches and encompasses diverse techniques under a variety of names, in the different business, science, and social science domains. See the original definition here –

If you are still struggling with your data warehouse, I would suggest that you limit yourself to Data Analysis before tackling BI. Not because the technology is difficult or even expensive, it’s a mind shift. You need to get a handle on what “discovering useful information” really means. And, if you find it challenging, you should get a senior data specialist on board to get you through the basics. A specialist can help you flesh out a strategy and up-skill your organization. Try to avoid the urge to hire a young (i.e., cheap) wiz kid to “wing it”. It will be costly and can lead to detrimental results for your technology vision.

Big Data and the so-called V’s

A lot of Big Data experts talk about the V’s of big data, and each one of them has their list but here is a taste of possible V’s:

  • Volume

  • Velocity

  • Variety

  • Variability

  • Veracity

  • Visualization

  • Value

I have always focused on the last 4 (i.e., getting data from all over the organization and using it) because I felt most businesses still battle with those V’s of big data. To continue our story we need to move on to Volume and Velocity.

The “BIG” in Big Data should be easy for us to visualize. Think of Google, Facebook, and Twitter. What these big companies do with lots of data can fill books on its own. It is not what these companies have done in the past that we need to look at what they are aiming at in the future that we need to discuss. The big corporates are all looking towards even more data to harvest, and that data they hope will come from The Internet of Things (IoT).

Internet Of Things (IoT)

What is the internet of things? In simple terms, it’s a dream to have every physical thing out there connected and talking over the internet with each other and with humans. And when I say everything I mean everything – even stuff that you and I consider disposable items, like baby nappies.

Mostly, you will hear about “smart houses” or “smart cities,” but I will use a more mundane example to try and show how pervasive the technology will become.

Just for a moment close our eyes and imagine your socks telling your shoes that they need to let in more air because your feet are sweating, and your shoes responding accordingly. Need I say more?

Why is IoT important? Well if you ask Google you won’t find a definitive answer because it is mostly hype at this stage. Only a few industries that have indeed shown the return on investment (ROI) of it in their specific niches.

However, in theory, IoT will give us two things, (1) lots more data about what humans and their appliances get up to every second of the day, and (2) the opportunity to react to what is going on in the world at a nearly real-time.

Volume and Velocity

This brings me to volume and velocity, the amount of data out there is already staggering, and it’s just going to get worse. To make sense of it all, we start seeing where the Wikipedia definition of BI is going.

The breadth and depth of the data is already a challenge for data scientists at the likes of Google, Facebook, and Twitter. These scientists don’t just use simple reports and metrics to help with decisions. They build real-time decision machines to drive decisions and just “good enough” to make a difference to the bottom line.

These decision machines are where this series will take us next, Machine Learning. So stay close for Part 3.

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