The fundamental mechanics of business have changed.

Well, they haven't quite.

The basic laws of supply and demand still govern the economic principles inside which firms in all industries bring goods and services to market inside a common monetary system on an international level.

But a change has occurred and it is an information-driven shift.

Our core accounting systems used to represent the motherlode of all company information. Onward from there... somewhere around the end of the last millennium we added so-called Customer Relationship Management to the corporate information arsenal and started to build up the commercial data bank.

Fast forward into the first decade of the new millennium and we found ourselves deeply entrenched (and enamored with) the world of Enterprise Resource Planning (ERP). In the ERP-enabled world we started to define Key Performance Indicators (KPIs) and use business metrics in a more mathematically sensitive way than ever before.

What makes a truly data-centric firm?
Today we take ERP as a given element of a wider total corporate data stack. The modern firm captures data from accounts, from customers, from business units (in the ERP sense) of course, but that's just the start. A truly data-centric firm also captures information from employees, external competitors, business equipment (in the capital expenditure CapEx and operational expenditure OpEx sense) often with Internet of Things style sensors and more besides.

To clarify our argument one crucial step further, this (above example) is not a truly data-centric firm; this is only a data-aware firm. A truly data-centric firm is also capable of capturing these multi-level information streams and being able to analyze them for operational (and so therefore) commercial advantage.

Future investment brokers won't ask to see profit and loss statements; they will ask "how good is your Big Data information capture and analytics procedure system?" or such like. Okay yes they will ask for P&L too, but you get the point.

This practice of analytics is defining the modern 21st century business. Knowing what customer movements mean is important, but knowing how to analyze what connected ancillary factors will influence customer behavior before it happens is what really makes the difference.

This overall trend for change toward analytics has certain effects. Firms need the same mix of salespeople, IT, finance, admin and other staff; but now they need a defined specialist to serve as a Data Scientist (CAPS intended to denote job title) -- or, at least, they need to be able to outsource the consultancy services needed to supply that analytics intelligence.

Patterns and anomalies
Companies who "get" the analytics challenge are using a variety of tools to surmount and conquer the Big Data challenge. The data scientist (lower case from herein) is using elements such as the Apache Hadoop open source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. On top of Hadoop the data scientist is using In-Database and Hadoop In-Memory Analytics to start to uncover patterns and anomalies to get new insights and make decisions based on facts discovered.

The data scientist uses Data Vizualization tools to (in theory if he/she does it right) begin to uncover patterns in both internal and external data and start to perceive and act upon the resulting analytics at hand. These same analytics tools can of course be turned inwards so-to-speak and focused on the firm's own operations to uncover trends and perceive and predict actions that could and should be taken to maximize profitability and welfare of employees and customers.

Analytics used at its most effective level becomes a tool for firms to drive their ability to compete and innovate.

Nobody's perfect (with data analytics) yet
This is the pure (as opposed to applied) theory of data analytics where it sits in perfect post-deployment harmony inside a Hadoop (or other) managed Big Data framework. Not all of this theorizing is easy to pull off over night and we know that Hadoop installations are complex by their very nature. But taking the purist pure view is a good exercise to undertake at this comparatively still early stage for cloud, Big Data and analytics (and let's not forget mobile too). We need to discuss what is possible and then see how close we can get to perfect.

In this new world of business is it now fair to table our opening gambit again? Have the fundamental mechanics of business changed? For many real-world businesses today there is now an open admission and acceptance that data is the greatest commercial asset that they have. Not every firm has complete control of its data asset base, but this is precisely why we are having this discussion.

The fundamental mechanics of business have changed and the non-data centric company will ultimately be history. Soon after that, the non-analytics company will also be a distant memory. Senior management is (largely) agreeing with the need to shift resources toward data-driven decision making and wider Line-of-Business strategies are also falling in line.

A mindset for the future
The sophisticated data analysis innovator has fine-grained business control and a stable strategic growth path planned out that is capable of constant and continuous dynamic change. We're not re-inventing the light bulb or the wheel today, but we are re-inventing our core operational business ethos and mindset. Nonbelievers in the data revolution will be historical figures sooner than they think.

This post is brought to you by SAS.

SAS is a leader in business analytics software and services and the largest independent vendor in the business intelligence market.