Big Data, what’s in it for me?

Let me start with a fact. An astonishing one at that.

Less than 0.5% of all data is ever used*. Run some quick maths on that figure and if you gather one million lines of data, over 995,000 lines of it was a waste of time gathering. Or was it?

This fact shouldn’t spur you to gather less data, so bear with me. What it means is you need to maximise the opportunity that’s presenting itself, that’s a lot of untapped potential. View your data in monetary terms, treat it like a stock or share. Handling data in the correct way and investing in the right types of data, at the right time will only increase its value to your organisation or business. To champion my point further, here’s another interesting fact for you. Audi (the car manufacturer) generates more revenue from their IT and software business than Cisco (yes Cisco!).

Audi Revenue Image

Data has typically been viewed as a by-product of a business process. It’s produced because it has to be. Usually large amounts of data are gathered, with one specific process in mind. Once that process is complete, the data is analysed, and the report or summary established, it’s commonly left to rot (and very often stored for a lot longer than any legal retention periods require, at great internal cost!).

Now before you run off and commence some crazy Artificial Intelligence or Machine Learning project, trying to automate lots of things etc. The first port of call should be to revisit your existing data that you view as part of a closed process. More often than not, there’s a very likely chance that you can recycle the data into a variety of other uses.

Take an expense system as an example. You gather a lot of data. A lot. The purpose is to reimburse a total value against an approved claim. But what information have you gathered? You can work out how often an employee has worked away, how many miles they travelled, how much time they’ve spent on the train, whether they’ve been working late, whether they’ve been putting in breakfast claims after early starts and much, much more.

Now as a manager, this information could be very useful. Are certain members of the team at risk of burning out? Is one member doing all the travel whilst another works local or remote a lot of the time? What about rewards? Is it worth understanding who has spent the most time away from their family at night and maybe doing something to reward them, or allowing some downtime with the family? After all, keeping people happy is one of the most important factors in staff retention, and as it happens, the busiest, hardest worked, quite often are the ones who excel in their role. They’re in demand after all. It’s easy to feel isolated or overworked very quickly.

If you can drive more purpose out of what data you currently have, the value of that data will increase, at little cost to the organisation. In the manufacturing industry, there’s loads of examples of recycling by-products and making use of them for something else. The exact same can apply to data.

So what else can you do? Well this now moves onto the huge world of Digital Transformation. Some key terms you might hear in the many data conversations you’ll likely come across are below:

  • Data Lake – A large repository of data in a raw, unstructured format.
  • Data Warehouse – A large repository of data in a structured format.
  • Data Analytics – Process of examining large data to highlight patterns, trends, unknown correlations, specific preferences etc.
  • Data Mining – A subset of the analytics but using sophisticated technology to recognise and establish patterns.
  • Artificial Intelligence – The development of systems and services with the capability to perform tasks and processes normally deemed to require human intelligence.
  • Machine Learning – The application of AI in such a format that the systems self-learn and improve from their experiences without having the be re-programmed to do so.
  • Algorithms – Mathematical formulas and statistical calculations used perform an analysis of data and conduct problem solving processes.
  • Data Scientist – A person who can make sense and drive value in big data, not through traditional analytical activities, but via computer science methods, algorithms and data manipulation.

These terms will likely crop up in a variety of technical and business transformation conversations. Some should be considered more technical modernisation whilst others are much more transformative. Regardless of this, you should try to categorise your data aspirations into three main areas:

  • Replacement – Removal of manual processes, conducted by your staff, consumers of your services etc. This includes any potential automation of these processes or services.
  • Business Predictions – Analysing data. Not just to provide insights into previous events (typical Business Intelligence dashboards), but to spot trends and patterns and allow accurate forecasting of future events.
  • New Services – Using the data to provide new services within the organisation.

The replacement of legacy processes should provide an organisation the opportunity to re-skill their workforce, making new or refined roles that are much more value driven, rather than focusing on collating information and providing reports and summaries. Typical workforces employ so many people whose role is simply to provide detail on historical events.

As a minimum, every organisation will have a finance function, which is a prime target for replacement activities. Business Predictions and New Services tend to require a little more thought and planning, as quite often a lot of the data is in an unstructured format. This is where the Data Scientists start to come into play, along with a variety of cloud requirements in technologies such as Azure Machine Learning.

Finding the right people, or retraining current staff is perhaps one of the most difficult challenges CIO’s will face. After all, 65% of global C-Suite executives surveyed in a recent study stated that their organisations are at risk of becoming irrelevant or uncompetitive if they don’t embrace big data**. Also, to give a gauge on the importance of these roles in the growing data market, Jaguar Land Rover last year announced plans to recruit 1,000 software and technical engineering staff. They even advertised their recruitment for some of these roles via a code-breaking challenge!

It’s a huge shift away from the traditional IT services, operations and projects but one worth tremendous value. Manipulating your data into something more valuable can both reduce overheads and operational costs, and also drive future revenue growth in times where the threat of competition is ever increasing.


*Source: Technology Review

**Source: CIO

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