Search This Blog

Tuesday, 20 August 2013

Why Data Management is the heart of E&P industry

Let us face it. Data Management within Upstream is no walk in the park. All the V's of Big data are applicable for this industry - Volume, Variety and Velocity of data.

Experience in E&P space has suggested the following DM trends and let us explore the When, Why, How and Where organizations do Data Management

When : Data Management is NOT a mandate to run Business. It is always performed when organization make huge profit and looking to spin off initiatives / Project
Why : To explore Opportunity in an attempt to improve efficiency and excellence
How : Spin off an effort to draw out a multi year Strategy and Roadmap for Data Management
Where : Usually concentrate in subsurface as the DM market is mature in subsurface - for process, standards and tools.

All of the above has made Data Management an after thought and deteriorated the importance of managing data.

The primary motive of any business is to first stay in the Business. For O&G companies to stay in business,
1. Safety on all operations to Habitat, environment : Organization need to create, maintain, update safe operating guidelines. Gather and analyze data to ensure no risk to Habitat and enviroment
2. Abide by the rules (County, State, Federal rules) : Organizations need to provide data for regulatory and compliance requirements for license to Operate

Both of the above is driven by Data. This clearly indicates Data and managing the data (Data Management) is a mandate for E&P businesses to stay alive. In my opinion Data Management efforts in organizations should start with addressing the above.

Monday, 19 August 2013

Data Governance

DAMA puts Data Governance on the top of the food chain of Data Management.  DAMA defines Data Governance as "the exercise of authority, control over management of data assets". 

To reiterate the importance of the Governance, here is a simple analogy.  Governance is like the King in a Chess game.  At the outset, the King does not do anything drastic.  All he had to do is stay alive.  The game revolves around him.  The game is lost when the King dies.  Governance is very similar, at the outset on a day to day activity of Data Management, Governance has less role to play, but without a Governance, the Data Management within the organization will die.

Let us simplify the activities of Data Governance.  
  • Approval on spend, strategy and roadmap :  Facilitates in implementation of Data and Information projects, Data management organization 
  • Data Policies, Standards and Procedures :  Drives standardization within the organization.  
  • Communicate and promote value of Data & Information : Change agent in ensuring the projects and standardization (from the above 2 points) are followed / mandated within the organization
  • Continuous improvement : Continue the momentum and quality of data management initiatives in line with the business vision

Given its importance, one would think every organization that is keen on Data management will have a strong Governance as its backbone.  Unfortunately, Governance in the Data world is far fetched. This is one of the key reasons on the lack of data management maturity within Upstream.  With the technology boom -IT consumerization, Big data and Analytics, and its impact on data and information management, the need for strong Governance is far more important that before.  

Before we look at the reasons Data Governance has not been strong within Upstream, let us look at the few types of Governance.
  • Collaborative :  This is more of a democratic way of Governance.  Members of the board vote on the decisions and the way forward
  • Advisory :  This is more of a advisory Governance, where there is lack of authority.  
  • Autocratic :  More of a dictatorship.  Do or Die :)

Most upstream organizations, the Data Governance follows the Collaborative model. There is usually board or boards that meet at regular intervals and review a hopper of activities and make decisions.  Here are some of the reasons for failure
  • Lack of single point of accountability.  There is no incentive or penalty  on decisions. Decisions are made by a group of individuals and the Data Manager is just a part of the decision 
  • Pace of decisions / Activities : I had a drilling engineer attend one of the collaborative Data Governance meeting and was visibly upset at the pace of activities, decisions etc... Not in line with the pace of the business or technology advancements
  • Lack of Focus on change management : This includes Leadership support to governance and its role as a change agent.  Standards, policies and procedures defined by Governance is optional and not mandated within this organization.  

In the words of Frank Underwood, "Democracy is Overrated".   Clearly this kind of governance has not yielded the results for data management within the industry.

Let us review the initial definition of Governance.  It talks 2 important things -  Authority and Control.  Those are the key that gets missed with Collaborative Governance.    With the emergence of CDO (Chief Data Officer) existence within every organization, probably it is time for us to look at the other extreme of Governance.  Change is imminent.  The question is should we start considering a certain degree of the the Autocratic model with the Authority and Control given to CDO.  In my humble opinion, we should. 

Tuesday, 13 August 2013

Uptake of Data Management within E&P industry

There has been multiple blogs, articles, forums and conferences on challenges of Data within E&P industry. Yet there seems to be not a huge uptake within the industry for Data Management. There are sproadic efforts but none too transformational in nature. There has not been a huge uptake of DM within the E&P industry. Here are 3 things as to why

Lack of Objectivity for DM Business case : Last month, I was approached by a colleague asking to help in engaging the Business on Data Management discussion. I reviewed a 10 page presentation that was presented to the business outlining the great things Data Management can do to bring Consistency, Standards, Accuracy, Accessibility to data. The presentation was a perfect pitch for Data Managers. But it did NOT outline the business capabilities that Data Management provides. The missing links include but not limited to Safe Operations, Compliance with regulatory agencies, Productivity etc. Even the above are subjective in nature. There is not really a easy way to drive ROI on data management which Management would like to hear and see.

Long drawn Implementation : Most Data management projects are multi year, multi million dollar projects predominantly starting with implementing a Master Data Management solution. The projects are seen as a continual effort with no end at sight. Predominant reason being the different dimensions of Data management - starting with defining Governance, Standards, Quality Rules, Processes & workflows, Integration in addition to technology, services and support. Most DM projects tend to address all of the above before the solution is deployed for use for enabling Business capability. This long drawn effort often loses interest from Business

Lack of Change Management : Most DM Business case and Implementation neglect probably the most important thing that makes DM a success - Change Management. Data Management is a enabler and NOT Business critical. Data Management does add work to Business, change their current ways of working - not one of the favorite topics to discuss with the users. Added to this is the size of the organization / function. However without an effective change management driven by Governance body, all the efforts put in to implementing the Data Management are reversed. This will yet another toolkit that is seldom used.

As Data Managers, it is essential to address the above to a reasonable / varying degree to implement Data Management efforts and enable Business capability / Efficiency