bim5 history and background update 2014 v2.4

8/11/2019 BIM5 History and Background Update 2014 v2.4

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BIM5 Westheimer Energy - New Digital Business Feb 2014

Using Business Intelligence Maturity to Survive Change

Jess B. Kozman Data Management Practitioner, Mua!ala Petroleum " #estheimer $nergy Consultants

Intro!uction y $! $vans, Senior Consultant an! C$%, &e' Digital Business, an! Jess Kozman

Pre(rint o) ac*groun! in)ormation )or 'or*sho( +o' to survive change in $-P I/ at SMi0s 12th annual

$-P In)ormation an! Data Management Con)erence, 13th 4eruary to 15th 4eruary 3617, 8on!on, Unite!

King!om

htt(9""'''.smi:online.co.u*"energy"u*"e(:in)ormation:!ata:management

INTRODUCTION

Projects in the area of business intelligence for oil and gas Exploration and Production (E&P) IT have long

been the target of efforts to reduce risk and measure performance. Various metrics have been proposed overthe years, from positive indicators of user take-up to negative measures of user complaints. In an environmentwhere every project or initiative involving geotechnical data has to do two things simultaneously; namelydeliver business value and integrate painlessly into existing workflows, it can often be the way the message ofchange is delivered which makes the difference between a successful outcome and back to the drawing board.When organisations fail to share and align goals with workflows, business culture and capability for change(Haue et al! 2004), the result can be the failure of an otherwise promising technology for transforming data intobusiness intelligence.

Today's data management practitioners acknowledge that delivering technology painlessly to support E&P IThas both an emotional and a practical side to it. Perceived success requires that people feel that a good job

has been done and business success depends on keeping geoscientists and engineers (Petro-TechnicalProfessionals, or PTP) working effectively. Since business intelligence projects inevitably include a technologycomponent, analysing and quantifying a team's tolerance for technology change and tailoring implementationsis one way to both minimise disruption and find and leverage internal champions who can reduce the friction ofchange. Understanding where an organisation resides and where it is moving on a technology maturity matrixis a first step toward quantifying tolerance for, and benefits of change. This paper presents metrics adoptedfrom technology capability maturity models ("r#sby! 1$%$) and expands on early work in E&P that applied themeasurements specifically to the area of petrotechnical data management (D&'ngel# an( )r#y! 2000).

The principles of the Business Intelligence Management Maturity Metrics Matrix Model (BIM5) *SM have nowbeen used in diverse projects on a global scale to determine the best strategies for surviving the changesrequired by business intelligence projects. No matter how well designed or how much of an improvement atechnical solution may be, technical end users will have to change something about the way they work in orderto realize benefits. Measurable improvements in individual workflows need to be communicated, in thelanguage of the business. Impacts on everything from IT users with command-line interfaces to the CIOscontinuous view of an SAP implementation need to be considered, along with how the business depends onthe technical environment. Without constant attention both the value and effectiveness of the technicalenvironment and information on which it runs degrades over time. The impact of this data entropy (Fli*hy an(+#,ell! 2011) in the absence of managed change can also be measured with a suitable application of thedescribed model.


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Change Management is a science in itself and has spawned a metaphor menagerie visible in any airportbookstore business section, from rivers and puddles (#gers#n! 2011) to puzzles (M*"#urt! 1$$%) . There arereams of papers, books, even cheese shops and melting icebergs dedicated to understand and explainingchange to the reluctant and how to implement change to the management team. This is very useful material

and can be applied in many areas of Oil and Gas. However, practitioners have found much of it to be targetedat scales and facets of maturity change that may not be effective for technical business intelligence E&P. Onekey to success is knowing what kind of change you are managing and which parts of this body of knowledgewill have the most impact, based on the capability and complexity of your organisation and team. The wrongmix of well-intended change management process can be as damaging as mixing your cheese with youricebergs.

This paper provides some insight, examples and areas for discussion leading to an improved understanding ofhow to survive and thrive in change in E&P IT and how to improve your chances of success in any initiative,based on successful implementations in real-world case studies of a proven method for measuring capabilityand complexity metrics. It leverages first-hand knowledge based on what one client described as "an ability to

manage effectively in an environment of deliberate uncertainty".

HISTORY

The pressures from incursion of large data volumes on geotechnical professions have been recognized sincethe early 1970s (W##(lan( et. al.! 1$%/), and have been the subject of continuous discussion and debate sincethen. This has spawned a continuing litany of often revisited, but seldom resolved, challenges at industrymeetings and conventions, with a growing need to quantify just how much effectively managing data adds tothe bottom line of extractive resource industries. Describing the volume of data involved in this debate has inturn created a cottage industry of metaphor mangling and acronym abuse, from the seismic tsunami of theearly 2000s (alinin*he,! 2000) to the digital explosion (Farr! 2012) of today, from Create-Read-Update-Delete

(CRUD) (H#llan( an( Nash! 2001! Harris#n! 200%), Knowledge-Information-Data (KID) (un et al.! 2004), and onto theubiquitous presence today of Big Data (Farris! 2012). Even discussions of staffing requirements and shortagesin exploration and production (E&P) now regularly refer to an employee cohort by its relationship to the onsetof digital data (yan! 2012), and the design of many query-based user interfaces for business intelligence reflectsthe influence of the Millennial workforce which is comfortable with and expects nearly instantaneous andunfettered access to mind-bogglingly large volumes of data and information.

As large volumes of digital data began to attract the attention of Information Technology (IT) professionals inthe oil and gas industry, technology vendors developed tools and services designed to manage the flood ofpetro-technical data, leveraging technologies such as relational database management systems (RDMS) andhierarchical storage management (HSM). But some early deployments of what could have otherwise been

effective information management technologies experienced failures, in part because target organizationsfailed to share and align data management capability maturity goals with workflows, business culture andcapability for change (Haue et al! 2004). In parallel, the nascent IT discipline was developing managementmethodologies for technology resource activities which recognized that organizations were best served byprojects and strategies that proceeded in stages over time (N#lan! 1$%/). This resulted in the concepts behindCapability Maturity Models (CMM) and some of their early applications to quality control ( "r#sby! 1$%$). Theneed to carefully match data management technologies in oil and gas organisations to the capability levels ofpetro-technical end users was recognized as soon as digital volumes began to dominate high end petroleumexploration workflows (M*enie! 1$$5). At the same time studies were also acknowledging the lack of high levelvisibility for data management personnel and budgets (Feineman!1$$2), and the difficulty of quantifying the value

of data management as compared to higher profile interpretation activities (+#we! 1$$5). To this day, measuring


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and justifying the value of data and information management remains a popular but elusive goal for most largepetroleum organizations. In this paper we trace the history and development of a robust tool for evaluating,measuring and tracking the impact of business intelligence management strategies on the bottom line of oiland gas exploration and production. The tool has a documented history of usage, widespread application to

various data types and geographic locations and plays, and a scientific and statistical basis that makes itessential for data management practitioners in todays business driven climate for data management. It alsoprovides an unprecedented opportunity to quantify and predict the relationship between appropriate datamanagement strategies and the bottom line of exploration and production, by providing a measure of financialimpact for specific facets of business intelligence capability.

It is a defining characteristic of oil and gas exploration that the adoption of industry standard methodologiescan lag by years (Wallis! 2011) behind other similarly data-driven industries, such as medical,telecommunications and aerospace. Thus the introduction of maturity models as a way of measuring andquantifying the evolution of data management capabilities in the petroleum industry dates back only slightlymore than a decade (D&'ngel# an( )r#y! 2000), despite the introduction of the concept in other technology

intensive processes, especially software development, more than a decade earlier (Hum3hrey! 1$%).

4igure 1. $arly version o) the Data Management Maturity Mo!el ;)romDAngelo and Troy, 2000ahan! 2004) betweenpresent and future positions can be used to differentiate between tactical and strategic implementations. Thematrix can also be devolved into individual facets along each access, or aggregated into views that compareacross locations, disciplines, or data streams. Quick win tactical and strategic projects can be identified anddeveloped to move the organisation closer to the main sequence and to previously identified islands ofstability. The anticipated initiatives are then matched against the appropriate and required level of changemanagement to design communication strategies and increase the probability of defining business successthrough measurable improvements (Eggert et. al.! 2011). Since the capability metrics and complexity metrics are


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correlated with changes in an organisations core activities and assets, respectively, the degree of changealong each axis helps to define the change trajectory and can be used to select a change managementmethodology and level best suited to the project or program designed to implement the change.

The current version of the standardised assessment methodology is supported by several BIM5 assessmenttools. This technique recognises maturity as a combined metric plotted by comparing capability and complexityon a two-dimensional matrix. Data management capability reflects the way in which oil and gas asset teamsuse the facets of Process, Resources, Organisation, Metrics and Technology (PROMT) to improve their datamanagement performance, although for comparison across multiple organisational types, some configurationsuse a revised set of facets of Standards, Technology, Organisation, Resources and Metrics (STORM). Thesefacets can be mapped to international standards such as the DAta Management Association (DAMAInternational) DM-BOK2 (Data Management Body of Knowledge) (which describes functional areas andorganisational context. They also correlate to industry surveys such as those conducted by Common DataAccess (CDA) and Schlumberger in the U.K. for North Sea Operators, or broader international benchmarkssuch as the one recently referenced by Halliburton in its Landmark Smart Vision methodology (2013 17th

PNEC Conference with Berry Petroleum). Unlike linear and one-dimensional capability models, in the BIM5methodology, capabilty is one dimension of overall maturity, and is plotted on a horizontal axis from a Baselevel of I to a Critical Level of V (Note that to accommodate the findings from some asset teams andcompanies, a capability level of Regressive had to be added below the Base level, organisations at this levelactively discourage progress in the use of data management). Capability is generally measured by identifyingobservable characteristics that place the organisation along the continuum of Levels, or by surveying endusers of data about their perceptions of how the organisation's approach to data management can bedescribed.

The vertical axis of Complexity is a quantitative measure of the challenges for data management that increasewith an exploration and production organisation's size and reach, that is they are dependent variables that

scale with size. In the BIM5 model the same 5 facets are measured to provide a meaningful matrix forcorrelation. Many measures for complexity facets can be gleaned from public domain sources, such as size ofreserves and volume of production, operating budget, staffing levels, and number of operating assets orgeomarkets. The number and overlap of applications and steps on industry standard Supplier-Input-Process-Output-Consumer (SIPOC) workflows and the volume of the data itself also contribute to the ComplexityMetric. While some traditional descriptions of "big data" have identified volume, velocity and variety as facets ofcomplexity, the BIM5 model develops quantitative measurements, where the required metadata statistics canbe obtained, of data propagation, proliferation, pervasiveness, and persistence.

Capability can be measured by performing an assessment at client offices and interviewing members of staff inorder to gain an overall consensus as to the state of the companys data management operation. Complexity

can be assessed by examining such characteristics as staff numbers, Supplier-Input-Process-Output-Consumer (SIPOC) process steps as defined by Six Sigma methodologies, data volumes, and technologyredundancy and overlap. The complexity metric can also be weighted by the number and diversity of operatingareas covered in the assessment.

Past analysis of a number of other oil and gas organisations reveals that results on the matrix tend to clusteron a curve where capability and complexity are optimally balanced. This curve has been termed the mainsequence by analogy with allometric laws such as those from organisational dynamics and productivityoptimisation. As a company matures, it can be expected to progress along this main sequence as it developsboth in terms of complexity and capability. The BIM5 methodology differs from other maturity models in that itprovides a correlation with financial performance metrics that are scale-independent. Neither datamanagement maturity nor data management complexity can be correlated with better financial performance or


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more effective or efficient oil finding, but an organisation's position relative to the main sequence on the BIM5matrix does provide such a correlation.

An assessment can determine the current position of an organisation on the matrix relative to both perceivedand identified peer groups, and can help select quick win projects to move to a position correlated withoptimum financial performance. In this way, after decades of struggling with the business value case for datamanagement, organisations that benchmark themselves against the BIM5 model can show quantitativecorrelation with corporate performance metrics such as production replacement rates and reserves acquisitioncost. Recent application of the model shows that movement toward positions on the main sequence can alsobe correlated with improvement in scale-independent financial performance metrics such as finding costs,return on investment, and PetroTechnical Professional Efficiency (*hlumberger Business "#nsulting! 2012).

APPLICATIONS OF THE MODEL

A recent application of this methodology to a set of mergers and acquisitions (M&A) compared across two dataintensive resource plays ()h#m3s#n! 2012), showed that M&A between organisations with more disparity in

position on the BIM5model require a larger investment in change management in order to produce the samegain in financial performance when large amounts of data need to be assimilated. This is an aspect of M&Adue diligence that is frequently overlooked in the oil and gas business but can be mitigated by the use of cleanteams, groups of third party service providers working under legal protocols and confidentiality agreementsthat can access and review competitive petro-technical data, information technology, and business intelligencesystems and processes that would otherwise be off-limits after the announcement of M&A activity. This avoidssurprises in issues of data storage, accessibility, technology interoperability, access or formatting that canderail a quick merger or acquisition after regulatory approval (#use an( Frame! 200$).

@ master !ata management maturity mo!el 'ith )acets an! (resent an! )uture state in!icators ;)rom@mal)i, 366A

bim5 history and background update 2014 v2.4

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