The Data Divide

The BBC sketch showing the 'class divide' in Britain in the 60's is a classic of its time. In my talk at the Geological Society's "Big Data in Geoscience" conference I used this image to talk about the competence needed at each level to exploit Big Data. The first character, sporting the bowler hat, represents the board level, with budgetary, cashflow and return-on-investment concerns. In the centre is the Asset Manager with primary concerns about keeping the bit turning and the pumps pumping. The character on the right represents the technical level. At the technical level there may be a too much of an interest in the science or technology for its own sake.

It is difficult to take on additional strategies, but Big Data is 'coming- ready-or-not' and every organisation needs a response. Every organisation needs basic competence in data at each level in order to exploit Big Data and to manage data liabilities.

In relation to an organisation's Data Competence, the talk asked the questions:

What is Data? - from your point of view.

What are the software plays in Big Data? - avoiding putting technology first. 

Data Science vs. Data Management? - does data science need data management?

Why do we need a Data Strategy? - focus, focus and er .. focus.

What lessons have we learned? - mistakes that will be repeated.

(If you are interested I am happy to send a copy of the talk with explanatory notes: [email protected], or check the Geological Society website for the video.)

Endless Opportunities

Big Data is happening whether the organisation is prepared or not. Every online activity and every embedded processor generates more and more data. Mobile phones, fitness bands and even your car are constantly transmitting information that is of potential interest. In my view the key opportunity from Big Data comes from this increasing variety of data collected. New data can provide new insights, further comparisons or correlations and improved prediction and planning. There are step-change reductions in cycle time available from integrating new approaches into operations. New software tools that can deal with more data faster and can select data to compare with previous expert judgments promises automated or augmented seismic and log interpretation. Along every element of the E&P cycle there are costs to be reduced and decisions to be made more quickly. The possibilities for transformation are endless and exciting, like finding a window to another green world.

Delivering the available benefits to the organisation requires competency at each level. Exploiting Big Data needs planning and focus on the key business challenges and coordination across the enterprise.

Compliance

There are regulations in place to ensure that every organisation has a nominated point of contact for data. Reasons for storing personal data and the processes needed to update it are required by the new GDPR. Most organisations don't have these things in place. Data breaches have to be notified and companies can face stiff penalties.

The technical world of Oil and Gas doesn't have too much personal data to worry about, but the UK Oil and Gas Authority has asked that each company nominates a 'Samples Coordinator" with the role of fulfilling data compliance(Energy Act 2016). Any data relevant to finding and managing hydrocarbons could be requested.

Speak my Language

To most people in an organisation data is something that provides insight or numbers to help with making decisions or increase understanding. Data is shown through graphs or perhaps through modelling. For most users the best available data or information doesn't need to be system generated, it could be from a brochure or news item. On the other hand, for the systems professional, data is the other half of systems thinking from processes. If software is a process captured in code then data is what it acts upon. If a process isn't adding or changing data what is it doing? For the systems professional data is modelled and captured in databases according to strict rules in a two-level hierarchy. Big Data doesn't always fit this model. Systems professionals might grudgingly admit data on spreadsheets but shudder (often with good reason!) at the thought of business decisions made from locally stored excel.  

An organisation needs to allow for individuals and groups thinking differently about data. What is clear is the value of data is in its application by the business.

Coordinated Effort

Big Data challenges systems professionals to deal with unstructured and previously uncontrolled data. At the same time Big Data challenges the rest of the business to understand the need for handling data with more care and diligence as well as the challenges of effective corporate-scale implementation and operational support. The different points of view need to be challenged and supported within the enterprise for good things to happen.