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What data managers do

Thursday, July 2, 2015

Nalin Jena, Upstream Data Manager with Indian oil company Reliance Industries Ltd, gives his views on the best way to do E&P data management

Good data management leads to better and faster decision making, leading to improved asset performance.

Good data management aims to improve the productivity of geoscientists by reducing the time they spend on accessing data, improving data quality, creating an integrated working environment in which the data, systems and applications function as a single unit.

Poor data management means you're reinventing the wheel again. Something that's been done before, gets done again, so you've just completely wasted resources.

The cost of data management is so small compared to all the other expenditures, like drilling of wells that it isn't a cost constraint, it's a people constraint, recognizing what should be done and having the resources in terms of people to actually do it.

Good data management usually aims to develop a centralized database which helps geoscientists to have all data at their fingertips.

The data manager will decide where the data is to be stored; define the naming convention and the guidelines to be followed once the user has finished with the data.

Another responsibility is to overcome the struggle of users to get access to good quality of data, so that it does not remain on people's C drives.

The data being managed needs to include digital images, structured technical data, unstructured emails, spreadsheets and video files.

The data manager needs to define the most appropriate architecture and operational environment to store and retrieve the data effectively and efficiently.

Discipline

Many exploration and production companies are building a data management discipline that competes with the geoscience and engineering disciplines.

Earlier E&P data management used to be focused on only geology and geophysics domain, but now it is broadening out to cover all of the functions like drilling, production and reservoir.

The data management team can consist of geologists, geophysicists and IT people.

Data managers need domain skills, such as understanding of seismic, well log, geology, drilling and production.

They also need IT skills such as data modeling, database development, data integration / reporting /migration, project management and analytics.

So geologists and geophysicists are provided with all kinds of IT training, and the IT people are trained in geosciences domain.

Working with the 'business'

The data management team is still struggling to capture the added value created by the interpreters, such as horizons, markers, faults, geological models, reservoir models, analysis and reports.

The data management team cannot decide which model, horizon or report is the correct one to store.

So it is important to have a close working relationship between the data managers and the business functions.

Another challenge is the link between the static data and the dynamic data. Static data (e.g.geological or geophysical) needs a different approach than reservoir data that is more dynamic over time.

It is important to manage the relationships at a number of levels within the organization like data manager meet with various users of the data, where issues are brought up and solved.

The data management team should adopt a proactive approach to raise issues and propose new solutions without expecting the business to tell them what they need.

Too often, the data management community is waiting for the business to tell them what they need and the domains are waiting for the data management to come up with solutions.

Audit

Apart from defining policy, procedure and naming conventions there should be periodical audit to ensure that the policy and procedure are followed by the user.

The audit should check availability of data, to see if it is getting to the right people, right data and right time.

It should check on redundant data stores.

It should check on appropriate use of data, taking into account business sensitivity, confidentiality, retention.

It should check the documentation of what data is added to which data repository and when.

It should check the documentation of data sources used for data processing and publishing.

Four key areas of good data management

The four key areas of good data management are:

Standardization: Have standards been established and documented? Are data models architected and documented? Is secure access managed?

Roles and responsibilities: Are data producers and data consumers identified?
Are data management teams identified, trained, and their role communicated? Are data analysts and data custodians engaged?

Process: Is master data managed? Have business processes and business/data rules been applied?

Data quality: Are data rules tied to business processes? Are data rules tied to, mapped and documented to appropriate repositories? Is data monitored to a standard?

Associated Companies
» Reliance Industries Ltd.

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