The current landscape of A&D activity indicates a steadily growing market for transactions. At the end of Q3-2013, the total value of Deals in Play in US and Canada crossed $58 billion, which constitutes a massive 43% of the market. The estimated value of US Deals in play now stand at $32 billion. Deals in play as on Q3-2013 include a whopping 31% that have been on the market for more than a year and 44% for 3-12 months. What is seldom captured is the amount of data being acquired, which in turn brings challenges in maintaining data quality and in managing the acquired data.
There can be two broad classifications that can be made to the data acquired on asset acquisitions
With larger “gestation periods” for the deals, come added complexities of integrating asset data. On an average, there is an estimated effort of 6 calendar months and a minimum of 24 person months in successfully integrating data. This follows a complex lifecycle of data acquisition, cleansing, integration, validation and finally publishing. Each stage brings its own set of intricacies that are experience and knowledge dependent. The golden mantra of “doing only what is absolutely necessary” has become the unaccepted norm as operators are working against operations handover deadlines.
A typical cycle of integrating acquisition data can be depicted as
What is rarely realized is the average data degradation that happens with each step of the cycle. Based on industry studies and standards, approximation of the amount of data lost in the worst case scenario can be modeled as below.
Assuming an average of 1 million records of data being acquired over all data sets (covering assets, equipment, DOI and transactions), the average estimated “clean” data that is migrated is only 65%*.
* Worst case scenario depicted. Average loss of data ranges between 18-24%. Total number of records migrated also affects total data loss.
The data inconsistencies (up to 35% in cases) are not categorized as a high risk owing to multiple other critical operational constraints – which works well in the short term, can lead to bigger problems in the long term.
Data issues are costing companies more than they realize – we will explore a bit of this over the course of the next article.