Evolution of the 5th element in nature: Air, Water, Fire, Earth & Data

Survival of the most agile

Charles Darwin’s theory of evolution and the process of natural selection caused huge public interest just over 150 years ago. His observations and conclusion on the survival of the most adaptive organisms still draw attention, fascination and enthusiasm from both young and old. Some people may feel lucky that large but unadaptive predators are extinct today just like the Megalodon, a huge 25 metre carnivorous shark that would dwarf its modern day relative, the Great White shark who at a fully grown ‘mere’ 6 metres long causes people to fear certain stretches of water. But it is not luck as Darwin would put it, but destiny.

The noise being made for years now on Big Data is following the same story as that of the Megalodon. We are living in an age of never before seen available information, and we were quick to put it to good use. For business a real evolutionary point is upon us where the real competitive advantage is almost gone because using data through analytics is becoming more of a core competency. Strong data led businesses are now putting their knowledge and experience in data and analytics to, for example, enter new sectors where the incumbents have been slow to evolve, or they are deploying risk event mitigation and protection strategies underpinned by Big Data deployments and then taking the advantage when their peers suffer that risk event due to a lack protection (e.g. electronic communications surveillance).

I personally like to drop the word ‘big’ from the ‘big data’ term because it can confuse the topic and lead the conversation off on a tangent, but it is important to cover the problems I see with the term before progressing. The phrase I use with clients, technologists and anyone who's intested for that matter is 'Any Data', simply because irrespective of size, the Big Data 'V' of Value tells us that the size of what you are storing or processing doesn't really matter. It's what you do with it that counts.

The immediate problem with the term ‘Big Data’

Firstly size isn’t everything. Nobody calls an ocean a big lake, or a mountain a big hill. The topic is actually straight forward and it is a case of doing the most with what you have for what you need. I am not dumbing down the fact that specific skills, technology and all data types need careful management but at the end of the day, anyone working with data is either one of two people, the problem solver, or someone who helps the problem solver (most are found in IT or are subject-matter experts providing a context). Here is where evolution has occurred, where Data Analysis is now not the only option for business to put data to work, Analytics has evolved and is thriving. When faced with a business problem, your dataset is straight forward (i.e. structured data), little automation is required and small (<2gigabyte), data analysis is your solution. Conversely if it needs specialised help then you are entering into Data Analytics.

Secondly, Big Data misses the end goal in its name that leads to confusion. I am yet to meet a leader who solely wants to run their business on gut feel. It causes all manner of bad emotion when an important decision or input is required including stress, anxiety, confusion, sadness and even anger. Likewise, nobody wants to be the person to tell the bad news if the issue would have been averted. I speak to my many clients of all sizes from small headhunting firms to leading global banks about the need to ‘exploit’ data, though I find this word too harse. Possible alternatives to the word ‘exploit’ include productise, commercialise and monetise. Favour fortunes the brave, so invariably whoever in your market can get this right quickly will outpace the competition.

Finally for now on this point the term ‘Big Data’ does not encapsulate the need to be joined up - Return on Investment (ROI), Cost to Serve (CTS), Cost Income Ratio (CIR), Profit Before Tax (PBT), Risk Adjusted Value Metrics and Income are all valid end measurements to prove value generation from data exploitation, provided you have the right approach and control groups in place before you pull the Big Data trigger. To get these to truly right, the idea of a connected business/ organisation needs to be taken into consideration. End value cannot be silo’ed to just one function such as marketing simply because the technology department needs to be part of the commercialisation of data, and they have the responsibility to connect the wider business/ organisation with the same value generating fuel. This is one of the greatest challenges teams are probably facing as I hear and see it every single day from my new clients as a main barrier to growth. To calculate value is a dark art in itself, and one which is mostly circular within Big Data (i.e. you need Big Data support to calculate how you calculate end Big Data true value). Anyway, a connected business or organisation needs to exploit data across the organisation with ruthless control and oversight in both proof of concepts and business as usual operations (semi or fully automated) because the ‘Big Data’ technology stack should be flexible and agile enough to support all business functions. If your technology stack isn’t either of these then I personally recommend you to challenge the people who set it up and get someone involved who has delivered an end-to-end connected business vision.

Common misunderstandings, the imposters and the coming of age

Now onto what I believe is the biggest barrier facing the Big Data & Analytics community, principally that the area is still misinterpreted by business leadership. When this happens real-time opportunities are lost and large amounts of money wasted. Let me set out my stall here, Data Analytics and Data Analysis are two very different things, just like two different sub-species of animal and there is a clear divide in the so called ‘data rockstars’ and the data user general population. It has reached a point where we even have one sub-species trying to pretend it is the other. If you work in large organisation you will have observed is how the term Analytics has jumped into people’s job titles and team names, even when these teams/ people are rooted in Access and even Excel. Nothing annoys a person who lives and breathes in terabytes of data on a daily basis when a colleague claims a fancy job title including the word ‘Analytics’ when they simply do Analysis.

Another misconception is that Business Intelligence (BI) is Analytics, but the general Analytics community is still on the fence on this one. Some feel that data discovery through pretty pictures on a specialised data visualisation software is Analytics. I personally feel that at most it is pretty pictures over Analytics in much the same way as if I can operate an elevator it does not mean I am an engineer. So BI is a tool for helping someone explore and translate data quickly and easily. Some BI tools include an analytical layer to join and interact with the underlying data sets and this is where they can call them analytical tools with a BI layer. This point is not to be confused with the Big Data 'V' that stands for Visualisation within the core seven V's (yes there are seven, always has been, not just three that are aimed to get people to try large volume processing).

The final misunderstanding I feel has been caused by the technology industry itself through a lack of true expert data & technology consulting being freely available to talk fluently about both business and technology. I recollect being told by a senior Gartner employee in March 2014 that the average technology department was five years behind the wants of their business. Because we have this challenge, industry is still somewhat pessimistic about Big Data. Every major consulting house is desperately trying to find staff with the 'unicorn' data scientist skillset, and having trained and mentored many in throughout career I know they do exist in good numbers but after roughly six years of exponential growth (thanks Google & Yahoo) the vast majority have not developed the professional or personal life skills needed to advise clients at senior and mid-management levels. To get anywhere here we will need some time and unleash them from their desks (if they want to let go!). Until we reach this stage the market will be left with many large corporations trying their best to fill the void with the little staff they have who really 'get' the differing business department wants and technology department needs.

So Data analytics’ coming of age has now led it to be called internal business insurance, some executives call it career insurance, the important thing is that unlike end-to-end analysis on a spreadsheet, it can and should be deployed in all manner of business. If not to make sure the same issue does not go noticed again, businesses should be smart about how they can benefit from embedding their analytics back into all manner of organisational processes. For example, for a non-metric based process such as employee behaviour or conduct risk management, the capability today to govern and monitor through Big Data surveillance systems is on a whole new planet as the ‘big data stack’ gathers streams of data (e.g. Email, Voice, Chatroom, Print, System log files, mobile, transactions, etc.), interprets, conveys and learns to assess individual employees behaviour against internal control tolerances and regulation.

Darwin’s book On the Origin of Species (1859) gathered a strong following in Victorian times, and I for one believe he would marvel today on how the human race has itself progressed. Only a few decades ago, let alone in Darwin’s time, nobody could have fathomed the idea of a business getting real-time information on how people ‘feel’ and ‘like’ about a product or service. Similarly, the same can be said of information to people’s social relationships.

Data has become like the air we breathe in our evolved state.

Just because we cannot see the digital footprint we leave as we live our everyday lives, does not mean it is not there. Numerous new data suppliers have joined the old vanguard of market information suppliers, however the new suppliers all claim greater sophistication and agility over established information service providers. Web scraped feeds and insights are available from both unstructured social media, company website content, press articles, blog posts augmented and structured company financial statements, patent information and government open data (but to name a few). As human beings all our senses are focused on helping us communicate and understand the outside world and it is then no surprise that technology has become such an engrained medium for communication.

It flows easily like water; all it needs is a channel.

The flow of data from its source of origination is circular. From every input there is an output and these outputs are reused leading to an endless circular flow. It therefore is not wonder that the generation of data has increased to a point where there is estimated to be more bytes of data in the world today than grains of sand on all the world’s beaches. Channels have opened up to give this data much needed purpose and opportunity. They have the ability to flow in both directions capturing, translating, creating and relaying new information. The most exciting channel recently has been mobile. Within this channel, the good use of geospatial data help all manner of users and businesses interact. From location based sales offers to saving lives through geo-location. The only major issue I fear is the misuse of such data by unscrupulous employees or criminals.

Like fire it deserves your respect from the outset

Data can be very dangerous in two ways, firstly by being mistreated by people without the necessary skills, mathematical or otherwise. Secondly, it also needs specific containment controls to prevent the wrong people accessing it. Along these lines it behaves just like fire. But to start a fire you need an ignition source, and that is the user.

Human error with data will always be a worry alongside staff losing sensitive data. However companies are themselves now willingly challenging the boundaries of what is acceptable on data usage. A good example of this is the downloading of an app users entire contact list, and then marketing against these leads. Enthusiastic users tempted by the usually free app easily skip the numerous pages detailing terms of use, but companies should be mindful of certain behaviours even if the terms and conditions are permitted through legal innuendos. Treat the data of others in the same way as you would like your data to be treated is fair way to put it. Ethical marketing is another way to put it.

Data is the new soil, or earth.

The statement of ‘data is the new oil’ quickly was transformed into the idea of it being ‘the new soil’. The former was built on the premise that data is a source of energy for business on which advantage could be gained. The latter argued that oil was a dirty raw material rather preferring a view that data is a nutrient in which business could grow and prosper.

Soil, or Earth needs to be well balanced for it to be productive. Mineral content, bacteria and fungi are just some balancing factors but still some certain plants only grow in certain conditions. So climate is just as important to grow a specific plant as the earth it is in. For me the climate typifies the culture of the organisation that you are in, and whether, or not, it embraces the opportunities data can give.

So that leads to the idea that in 2015 have we reached a point where data can be recognised alongside the core elements? It is a challenging concept but in Japan the great five of air, water, fire, earth and spirit exist. The element of Spirit is even more intangible than data and represents the things around us, for which data is one. Check?

Still not sold on the idea? Well then think about this: the elements themselves have data attributes; the weight of a piece of soil, the heat gradient of a fire, the volume of a glass of water and speed of a gust of wind/air. Checkmate?

Get in touch with me to discuss further:

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