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Outsourcing Law & Business Journal™ – March 2012

March 21, 2012 by Bierce & Kenerson, P.C.

Computer-Assisted Document Review in e-Discovery Can Avert Manual Review, Even if Not 100% Certain: “Predictive Coding” Protocol Passes Muster

March 21, 2012 by Bierce & Kenerson, P.C.

When can parties rely upon nearly fully-automated “Big Data” procedures under U.S. federal e-discovery rules?  In a landmark class-action decision under the Equal Pay Act and Fair Labor Standards Act for gender discrimination and pregnancy discrimination, U.S. federal Magistrate Judge Andrew J. Peck ruled on February 24, 2012, that “predictive coding” can be legally sufficient even though it fails to meet a 100% confidence level.  The decision is welcome news for business defendants accused of mass torts or repeated wrongful practices under class action litigation procedures.  It is also welcome news for software developers that market computer-driven “relevancy” analysis, but bad news for FTE-based legal process outsourcing vendors hoping for large labor-intensive discovery requirements.

In short, this precedent adopts labor-saving software technology in lieu of manual review of Big Data in large litigations.

The Importance of E-Discovery in Class Actions, Collective Actions and Mass Torts. In Da Silva Moore v. Publicis Groupe & MSL Group, ___ F3d ___, (S.D.N.Y. Feb. 24, 2012), the defendants were accused of discriminating against a class of women employees, relating to pay rates, promotions, terminations, demotions and /or job assignments, based on female gender and pregnancy status.  The plaintiffs’ lawyers bundled the claims into a class action in order to maximize damages and settlement potential.  In deciding whether to settle, defendants in such situations fight to escape a judicial determination that the claims are sufficiently similar to meet the requirements of a class action.  Accordingly, the discovery process can be decisive in defeating the plaintiffs’ claim, since an adverse decision on class action requirements can dissuade contingency fee plaintiffs’ lawyers from further investment of their own time and expenses.

As a result, it is essential for defendants in such class action cases, “collective action” claims (for opt-in claims) and any other large-document litigation to minimize the burdens of discovery up to the point of the decision whether the plaintiffs’ claims are sufficiently common to move forward as a class action.

Computer-Assisted E-Discovery: Predictive Coding. Judge Peck quoted his own prior published article to explain “computer-assisted document review.”

By computer-assisted coding, I mean tools (different vendors use different names) that use sophisticated algorithms to enable the computer to determine relevance, based on interaction with (i.e., training by) a human reviewer.

Unlike manual review, where the review is done by the most junior staff, computer-assisted coding involves a senior partner (or [small] team) who review and code a “seed set” of documents. The computer identifies properties of those documents that it uses to code other documents. As the senior reviewer continues to code more sample documents, the computer predicts the reviewer’s coding. Or, the computer codes some documents and asks the senior reviewer for feedback.

When the system’s predictions and the reviewer’s coding sufficiently coincide, the system has learned enough to make confident predictions for the remaining documents. Typically, the senior lawyer (or team) needs to review only a few thousand documents to train the computer.  Some systems produce a simple yes/no as to relevance, while others give a relevance score (say, on a 0 to 100 basis) that counsel can use to prioritize review.   For example, a score above 50 may produce 97% of the relevant documents, but constitutes only 20% of the entire document set.

Counsel may decide, after sampling and quality control tests, that documents with a score of below IS are so highly likely to be irrelevant that no further human review is necessary. Counsel can also decide the cost-benefit of manual review of the documents with scores of 15-50.  Slip Op., at p. 4.

An Acceptable Predictive Coding Protocol. After rejecting several objections by plaintiffs’ counsel concerning reliability of computer-assisted coding, Judge Peck ruled that it would be acceptable to adopt a predictive coding protocol with the following procedures:

• The court (or an agreement of the parties) determines which custodians (and other sources) of electronically stored information (“ESI”) are to be the subjects of the discovery process (e.g., the directly involved employees or all persons potentially suspected of being wrongdoers).

• The parties agree to identify key words and other tags (which could include metatags) for searching, as well as the Boolean logic (“and”,” “and/or”, “and X but not Y”, etc).

• The court (or an agreement of the parties) determines the percentage of confidence required before the software that conducts the electronic search of ESI can be determined to be legally adequate.  (In this case, Judge Peck ruled that a 95% level of confidence was legally sufficient).

• The parties (under judicial review) create a “seed set” of documents to train the software for coding by degree of relevancy.  In this case, the initial seed set was approximately 2,400 documents.

• The parties (or the Court) decide(s) upon the sources of ESI, including e-mails, Cloud-based software such as SalesForce.com, databases and other e-repositories.

• The defendants agreed to turn over all documents that the computerized predictive coding showed was adequate, subject to exclusion under attorney-client privilege.

• The parties bring their e-discovery software consultants to court to argue the questions of reliability and technical “levels of confidence” in their software products and the ability to train software by iteratively tweaking the search parameters and Boolean logic.

• The defendant proposed, and the court agreed, to seven rounds of iterations of computer-assisted predictive coding, followed by human confirmation and review of the results and training of the software to improve the level of confidence.

  After the seventh round, to determine if the computer is well trained and stable, MSL would review a random sample (of 2,399 documents) from the discards (i.e., documents coded as non-relevant) to make sure the documents determined by the software to not be relevant do not, in fact, contain highly-relevant documents. (2/8/12 Conf. Tr. at 74-75.) For each of the seven rounds and the final quality-check random sample, MSL agreed that it would show plaintiffs all the documents it looked at including those deemed not relevant (except for privileged documents). (2/8/12 Conf. Tr.at 76.).  Slip Op., at p. 11.

The Legal Logic of Computer Logic.  In evaluating the use of computer software instead of human reviewers, Judge Peck cited academic conclusions that computer-assisted discovery could be more effective than manual discovery.

The objective of review in e-Discovery is to identify as many relevant documents as possible, while reviewing as few non-relevant documents as possible. Recall is the fraction of relevant documents identified during a review; precision is the fraction of identified documents that are relevant. Thus, recall is a measure of completeness, while precision is a measure of accuracy or correctness. The goal is for the review method to result in higher recall and higher precision than another review method, at a cost proportionate to the “value” of the case. See, e.g.. Maura R. Grossman & Gordon V. Cormack, Technology-Assisted Review in E-Discovery Can Be More Effective and More Efficient Than Exhaustive Manual Review, Rich. J.L.& Tech., Spring 2011, at 8-9, available at http://jolt.richmond.edu/vl7i3/articlel l.pdf. Slip Op., at p.17.

The Da Silva Moore, et.al. v. Publicis Groupe & MSL Group decision appears to be the first to explore and justify the use of computer-based protocols for relevancy search in e-discovery.  It suggests that manual review will become outdated and obsolete if sufficient “level of confidence” can be achieved using iterations of corrective instructions.

This decision issues a clarion call to use technology to unclog the problems that technology created in e-Discovery.  And, in legal process outsourcing, this decision will rebalance the equilibrium of pre-trial discovery burdens.  LPO will now move towards more senior level analysis such as for legal privilege and other objections to disclosure.  The semi automation of e-Discovery should also benefit both adversaries.  Costs saved from wasted manual verification of relevancy of a large volume of potentially relevant documents can be allocated to settlement payments if liability seems clear enough.  This decision also serves as a landmark for legal process outsourcing.  It defines a workflow for litigation to minimize labor and optimize the training of intelligent computer tools. It retains human control and responsibility but benefits from information technology tools.

Outsourcing Law & Business Journal – May 2011

June 1, 2011 by Bierce & Kenerson, P.C.

OUTSOURCING LAW & BUSINESS JOURNAL™ : Strategies and rules for adding value and improving legal and regulation compliance through business process management techniques in strategic alliances, joint ventures, shared services and cost-effective, durable and flexible sourcing of services.  www.outsourcing-law.com.  Visit our blog at http://blog.outsourcing-law.com.

Insights by Bierce & Kenerson, P.C. Editor. www.biercekenerson.com.

Vol. 11, No. 4, May 2011

_________________________________

1.  Legal Issues in Using Performance Metrics for Innovation.

Such real-time vendor governance tools can be used for more than just benchmarking actual performance against contractual obligations.   They can also be used to:

• identify process improvements in the outsourcing services delivery process for “better outsourcing”; and
• innovate across the entire customer organization and customer supply chain.

This article takes the leap from just governing an outsourced function (through dashboards and performance metric reporting) to re-structuring the entire enterprise based on “big data.”  By applying “business analytics” to such “big data,” organizations can improve the quality of their procurement organization (and their internal resource commitments) by looking for innovation.   In short, organizations that outsource can tap into governance data to help innovate processes for efficiency and higher revenue per Dollar of expense.

This article explores key attributes of business analytics in outsourcing and the challenges for integrating analytics in outsourcing contracts and a shared-services SLA-based services environment.   It also sheds light on why innovation never comes from outsourcing, unless the parties plan for it and adopt appropriate legal protections.  For more on the “next step” from “vendor governance” to “organizational transformation,” click here .

September 20-22, 2011, SSON presents Finance Transformation 2011, Dallas, Texas. This conference is targeted to owners, controllers, procurement leads, sourcing strategists, shared services and global finance leads who want a complete view of transformation, incorporating holistic vision and operating strategy, end-to-end process optimizations, technology enablement, business performance management and sourcing strategy, whether that strategy is shared services, outsourcing or a combination of the two.    Click here to get more information.

September 26-27, 2011, IQPC presents e-Discovery Oil & Gas, Houston, Texas. IQPC’s Third e-Discovery for Oil and Gas program is specifically tailored to address eDiscovery issues in the energy industry.  The program features detailed case studies from Oil & Gas ProducersService Companies that have implemented eDiscovery practices that have lowered the cost and increased the efficiency of producing ESI.  Panel presentations will discuss critical issues in eDiscovery from leading experts in the legal, information technology and records management sectors.  For more information, click here.

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FEEDBACK:  This newsletter addresses legal issues in sourcing IT, HR, finance and accounting, procurement, logistics, manufacturing, customer relationship management including outsourcing, shared services, BOT and strategic acquisitions for sourcing.  Send us your suggestions for article topics, or report a broken link at wbierce@biercekenerson.com.  The information provided herein does not necessarily constitute the opinon of Bierce & Kenerson, P.C. or any author or its clients.  This newsletter is not legal advice and does not create an attorney-client relationship.  Reproductions must include our copyright notice.  For reprint permission, please contact:  wbierce@biercekenerson.com.  Edited by Bierce & Kenerson, P.C.  Copyright (c) 2010, Outsourcing Law Global, LLC.  All rights reserved.  Editor-in-Chief:  William Bierce of Bierce & Kenerson, P.C., located at 420 Lexington Avenue, Suite 2920, New York, NY 10170, 212-840-0080

Legal Issues in Using Performance Metrics for Innovation

May 26, 2011 by Bierce & Kenerson, P.C.

Today, several software developers offer comprehensive software suites for governance of IT and business process outsourcing (BPO) services.  Such software enables outsourcing customers to see real-time data, presented on “dashboards,” of vendor performance metrics, including service catalog offerings, demand and supply volumes, service level management (measuring and reporting on SLA’s), incident and problem management (identifying outages) and change management data bases (CMDB’s) and computations of billing adjustments to reflect gaps between actual performance and contractual obligations.  Such software reports on “what is happening.”

Such real-time vendor governance tools can be used for more than just benchmarking actual performance against contractual obligations.   They can also be used to:

• identify process improvements in the outsourcing services delivery process for “better outsourcing”; and
• innovate across the entire customer organization and customer supply chain.

This article takes the leap from just governing an outsourced function (through dashboards and performance metric reporting) to re-structuring the entire enterprise based on “big data.”  By applying “business analytics” to such “big data,” organizations can improve the quality of their procurement organization (and their internal resource commitments) by looking for innovation.   In short, organizations that outsource can tap into governance data to help innovate processes for efficiency and higher revenue per Dollar of expense.

This article explores key attributes of business analytics in outsourcing and the challenges for integrating analytics in outsourcing contracts and a shared-services SLA-based services environment.   It also sheds light on why innovation never comes from outsourcing, unless the parties plan for it and adopt appropriate legal protections.

BUSINESS PROCESS MODELING FROM GOVERNANCE DATA

Sourcing activities that are governed by SLA’s generate huge databases.   The data can be assembled in a data warehouse that collects metadata about the operations (such as time of day, capacity and load management parameters, and quality parameters) from multiple sources for information retrieval and decision support.

Supply chain management requires continuous attention to performance management by external and internal service providers.  Business intelligence refers to the management tools for identifying, analyzing and acting on operational performance metrics for a business enterprise.  Such tools can include software, IT and telecom infrastructure and databases of “best practices” to help optimize operations and continuous process improvement.

Based on BI metrics of operations, finance and marketing, business process modeling involves taking big operational datasets from multiple business processes (“silos”), deconstructing and parsing them into value chains and optimizing the value chains for the bottom line.  Such modeling yields financial and operational efficiencies through business process re-engineering, productivity and growth.  In addition, such modeling improves transparency for process governance and operational risk management purposes.  In short, BI is a core tool for “governance, risk management and compliance” (GRC).

Beyond more efficient current operations, BI metrics enable predictive modeling for future demand management.  Exploring the relationship of different operating conditions based on historical data, predictive modeling extracts meaningful patterns, trends and anomalies from huge datasets, pointing the way towards future operational requirements and contingencies.  Predictive modeling can be used to refine and reduce the scope of outsourcing services, revise the “base case” for sourcing initiatives and thereby narrow the “flux” and uncertainty in performance-based pricing models.   Predictive modeling can answer business questions such as what transactions are likely to require special human problem solving and how to redesign marketing, product development, manufacturing and customer services to improve sales and reduce waste.  In short, SLAs can be analyzed to improve top and bottom lines.

STRATEGY

What are business analytics? Business analytics measure the business value of a business process.  Unlike SLA’s, analytics define the business benefits of an operating function.  While SLA’s measure how well a function is being performed, business analytics measure how effectively a business function generates business value.  At the “machine” level, they measure availability, transaction volumes and “speeds and feeds.”  At the BPO level, they measure quality, throughput, error rates, re-work cost, customer satisfaction and service delivery at specified times of day and week.  At the KPO level,metrics are a work in progress.

Insights. Business analytics can expose deep insights into business process and transform existing operations for higher performance, business value, proximity to the customer, market share and customer loyalty.  Business analytics represents an iterative improvement to the management of vendors, surpassing service level agreements in strategic value.

Causality. To be meaningful, metrics used in business analytics must correlate directly and proportionately with business success.  Indeed, the analytics process involves identification and quantification of such correlations.  For example, reliability data (system uptime, availability, mean time between failure, mean time to repair) correlate directly with productivity of workers, throughput in the manufacturing process, outbound sales calls by a call center and customer trust and repeat purchasing (or, conversely, churn ratio).  The analytics process establishes such causal connection, allowing insights in to pain points and opportunities for increasing revenues, reducing costs and eliminating waste.

Baseline for Developing Business Analytics. Contractual SLA’s establish a methodology for quantifying the outputs of delivered services.  Business analytics depend upon a rock-solid foundation for measurement of such outputs.  Unless core SLA’s are delivered, there can be no hope of identifying business benefits, inspecting such metrics for business insights and transforming the business to achieve the opportunities afforded by such analysis.  Since outsourcing is only an extension of the enterprise’s internal functions, effective business analysis of outsourcing requires corresponding metrics for internally managed processes and functions.  Already certain business consultants have developed, and some leading companies are implementing, software that identifies the same metrics across internal and external providers, including across multiple providers.

Building Analytics into the Business Process. The key to successful implementation of business analytics is to gather metrics and adapt them to shareholder value.  It is the responsibility of the CEO and the CFO to set forth the business metrics, which can be as simple as financial accounting reports, to the individual processes.  Extending the reach of activity-based costing, business analytics can show real-time SLA measurement and real-time impact on finance, sales, resource utilization, cost of goods sold, capacity utilization, resource efficiency and other income-statement metrics.

Delivering Analytics to Senior Management. Delivering analytics requires asking senior management to define what  they want to measure and how they wish to have it measured.  In a manufacturing environment, such metrics involve production line uptime, reject rate, production throughput, waste ratio, etc.  In a services environment, such metrics involve similar quantification of uptime, reliability, error rate, customer call abandonment rate, performance latency (response time) and accuracy (such as the re-work rate).  Senior executives need to define what they want, with help from the operations department.

ARCHITECTURE AND TOOLS

Pilot Projects at the Top, Implementation at the Mid-Level. To get started, small projects are used as pilots.  They serve to elicit questions for analysis and demonstrate proof of concept.   BI projects normally involve senior management at the strategic level, with implementation and ongoing reporting by middle management.

Harmonization of Conflicting Metrics. Pilot projects are helpful for standardization.  If different departments use different metrics, the pilot will show the differences, either by wildly divergent outcomes (inviting analysis of definitions and assumptions) or direct statement of conflicting assumptions. For example, “uptime” might include scheduled maintenance periods for one group, but omits such periods for another group.   Harmonization will help the organization adopt standard metrics and prevent under-reporting and over-reporting of the same data.

IDENTIFYING AND ACTING ON OUTCOMES

Pricing: Linking Performance Metrics to Business Value. Incentive compensation structures typically link payments to creation of shareholder value.  Similarly, “shared risk” and “shared reward” pricing structures also link performance to business value.  In each case, BI / BA tools can improve the value generated by measuring.   But identifying effective metrics is an iterative process.

In sourcing, what pricing metrics and methods are best linked to performance?  A license royalty offers a simple solution, since it is based on sales and gross revenues.

Innovation. The enterprise customer of outsourced services can use the “big data” generated from monitoring vendor performance over long periods as the inputs for analysis to enable organizational transformation.   Innovation occurs when the BI tools are structures to

• eliminate  processes that have high error rates or high consumption of resources;
• identify high-value relationships (and functions) and cutting out (or outsourcing) low-value relationships and functions;
• redeploy back office personnel into centers of excellence for shared services organizations;
• compare performance between vendors and between internal and external teams; and
• create value chains across the supply chain.

Contract Compliance. In short, the retained team that manages contract compliance for outsourcing engagements should interface with organizational designers (whether in-house or external consultants) to mine the “dashboard data.”  Contract compliance thus feeds innovation, but only if BI tools and strategies are separately adopted.

LEGAL, REGULATORY AND COMPLIANCE ISSUES

Legal Issues in Business Intelligence and Business Analytics. Several legal issues must be addressed to implement and manage an effective BI / BA program across the organization’s internal and external supply chains.  Corporate policies should be designed (or at least audited and revised periodically) with a view towards capturing the innovation opportunities that come from analyzing contractual compliance and other operational and performance data.

SLA Design and Management. Service level agreements (SLA’s) should be designed for achieving multiple goals:

• quality services for reliability, predictability, scalabilty and cost management;
• metrics tied to the returns on investment (and equity) for operations that are considered “core” or “prime value drivers” for the internal organization.

Thus, paradoxically, outsourcing can be used to shed light on insourced functions to improve ROI, ROE and overall shareholder value.

Data Rights Management for BI Processing. Most BI software applications process data from different sources and different data types.   The enterprise needs to ensure that it has legal rights to access and use all data that it wishes to analyze.

• The enterprise should specify, in its third-party supply chain and service contracts, that performance data obtained from evaluating the services of the service provider belong to the customer and may be used for any internal purposes.
• Service providers, of course, may wish to prevent the publication of performance outcomes through the use of appropriate confidentiality agreements.
• The question of data rights management can become a contentious issue in case of a disputed termination for cause, so the outsourcing contract should address how to handle performance metrics and their disclosure in any dispute resolution process.
• Since most enterprises are also service providers and may be subject to similar vendor management, they need to ensure that they can use the “big data” from vendor management tools for their own internal process improvement and innovation strategies.  In short, every customer is also a vendor, and everyone needs to have access to use “supply chain data” across internal and external supply chains.

Software Rights Management. Both service providers and enterprise customers need to ensure they have the licensed right to use the BI / BA tools.   This requires a software license audit for both parties whenever vendor governance tools and BI/ BA tools are used.

Business Method Patents, Trade Secrets and “Proprietary Rights”: Ownership and Licensing. The insights derived from BI analysis of outsourced (and insourced) operations can create new business methods.   Such methods may include patentable processes.  Organizations investing in SLA management, performance metrics analysis and BI need to protect their rights to own exclusively the insights and new business models developed using the data fees from internal sources and outsourcing service providers.   The organization should take all appropriate measures to ensure it will own the trade secrets (and any related business method patents).   Such considerations need to be addressed in non-disclosure agreements, confidentiality clauses in outsourcing contracts, subcontracts (at all levels in the sub-supply chain), employment documentation (employment offer letters, manuals, stock option plans (particularly in California) and termination procedures).

The waters are a bit murky as to ownership of improvements developed by the service provider.   Most outsourcing agreements mandate that the service provider deliver better service without being specially compensated for “continuous process improvements.”   Such clauses are  treated as standard “gimme’s” without cost or inconvenience by the customer, and many vendors ignore such clauses or swap them for a price concession over time.

Rather than swim in such murky waters, service providers and enterprise customers may benefit from a frank discussion on the opportunity to specify who owns those improvements and whether there is any license rights in them.  At this point, the customer is thinking that all improvements should enure to its benefit, but such thinking vitiates any incentive for the provider to deliver any real value for no additional compensation.  Hence, most service providers sell “consulting services” for “business process transformation” or “organizational redesign” and try to keep “innovation” out of the outsourcing contract.  At the very least, by independent BI analysis (“continuous process improvement”), the service provider can conduct its own BI analysis and own its own rights in trade secrets (and any related business method patents).

Confidentiality. Outsourcing contracts should include BI-type data and insights in the definition of “confidential information.”  Of course, whose “confidential information” it is should be defined as well.

Trade Secrets: Protection and Survival. The insights and process changes derived from BI / BA analysis should be protected as trade secrets.  Non-disclosure agreements should segregate “confidential information” from “trade secrets” so that “trade secrets” can survive the expiration of the non-disclosure period.

Competitive Advantage and Innovation. Sometimes BI will yield some crucial insights that the organization does not want to reveal to its service providers (or business management consultants), who might recycle such processes to benefit the organization’s competitors.  The BI governance model should help managers identify the competitive impact of sharing improved processes with outsourcing service providers.  If there is a significant innovative competitive advantage, then the process becomes “core” and thereby ineligible for further outsourcing until it is “best practice” widely known among competitors.  This example underscores the tension between outsourcing for operational excellence and outsourcing as a tool for “innovation.”  Recently, savvy service providers have begun developing and deploying their own proprietary business models for generic industry operations, thereby reducing this tension and making it easier to sell “best of breed” BPO.

Value Creation Process:  Training. Generally, outsourcing customers and their service providers do not compete for the same pools of talent.  In the field of business analytics, this generalization does not apply.  Service providers hire talent from the customer’s industry (and potentially from customers) to acquire process expertise, regulatory and risk-management expertise and build service delivery models.  The outsourcing contract should address matters of permitted and prohibited employment practices relating not only to operational personnel, but also to GRC and BI initiatives.

Regulatory Compliance Issues in BI.

Unwanted Industry Standards. Transparency of process has become the mantra of health regulators since HIPAA in 1995, securities regulators since the Sarbanes-Oxley Act of 2002, and financial regulators since the Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010.  Innovation strategies that data-mine outsourcing performance data can improve transparency.  But such successes can risk inducing the regulators to require other regulated enterprises to adopt one company’s “trade secrets” (which generate comparative advantage and shareholder value) as generic “industry best practices.”  Hence, disclosures to regulators should be couched in terms of proprietary information.

Auditor’s Checklists. Auditors conducting SAS-70, Type II audits (and their successor ISAE audits) have their own checklists.  Enterprises (and service providers) should require auditors to preserve the confidentiality of BI-generated process improvements.  But how can the auditors be prevented from using such “intelligence” in auditing competitors?

Corporate Governance Meets Vendor Governance.

Governance. Governance defines who is managing the BI/BA process, their reporting and accountability, and their incentives for finding, championing and achieving process improvement and enhanced shareholder value.  BI initiatives require careful attention to access controls and use of the inputs and outputs of the BI applications.   Polices and procedures for governance of BI / BA should address the rules for adopting changes in business processes (with supporting explanations and BI reports).   Internally, organizations will want to identify the roles of Finance, IT and Operations in BI / BA governance as applied to outsourced services.

Segregation of Operations from Management (and Innovation). For optimal outcomes, should enterprises seeking innovation segregate corporate governance (and innovation) from vendor management?   Probably not.  Segregation facilitates protection of trade secrets.  However, as a matter of career path for some individuals, some vendor managers from the procurement group might move to the BI analytics group.

Integration of BI into Operations Management. Conversely, the BI team should serve as internal consultants to the outsourcing vendor management group.  Such consulting (and training in BI concepts) can improve the design of service level management protocols, adjustment of SLA’s as processes changes (such as with mergers, acquisitions, divestitures and regulatory mandates).

CONCLUSION

Outsourcing engagements have spawned “big data” that can be analyzed for improvements to core processes (such as brand management, customer satisfaction, product design and development).   The legal framework for BI should be carefully planned and implemented to ensure that the enterprise retains flexibility, legal rights and avoids potential infringement in pursuing its mission and innovating its core processes.

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