The CIO’s guide to transforming an organization through building an information foundation Power up the business processes with operational and proactive insights.

Julianna DeLua, Informatica Sachin Khairnar, Deloitte Consulting LLP Amit Bhasker Patel, Deloitte Consulting LLP

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Abstract In the era of volatile markets and blurring boundaries, the demand to improve/enhance/leverage business operations with actionable insight is growing more widely and rapidly. Organizations are seeking to exploit evolving patterns and interdependent market partners to compete effectively across operational environments. The vision is to use analytical insights to drive agility and improve business performance by taking faster, smarter actions armed with business insight. Yet the question remains: are you, as a leader in IT, ready to enable your organization to operate with business insight from timely, actionable, and trustworthy information?

In this article, Informatica and Deloitte will discuss:

• Success factors for becoming insight driven

• Rethinking the information foundation — overcoming common challenges and root causes

• Modern information management approach for achieving business success

• Leading in the era of high expectations — driving efficiency and innovation

Chief information officers (CIOs) and other information technology (IT) leaders have been focused on building an IT infrastructure that empowers organizations to run efficiently. They were asked to reduce the costs and risks of “keeping the lights on,” sustaining IT applications, networks, and other operational systems. But for the IT leaders today, the measure of success is changing. Building on the improved IT infrastructure, the leading CIOs are placing a deeper emphasis on creating business value by allowing business to innovate, armed with better insights. Drawing from case studies, this executive brief illustrates how an IT organization can enable business to become insight driven by building the appropriate information foundation.

Becoming insight driven With the volatile nature of the market, business leaders have had no option but to act more insightfully, yet decisively. Doing more with less has been a top imperative for organizations of many sizes. Sustaining and expanding on core business preservation has become more vital than ever before as business assets are reduced and operational efficiency becomes a key measure. These initiatives have propelled organizations to scrutinize assets, both tangible

and intangible. They involve far deeper analysis on how organizations run businesses, what their customers will likely buy, what products will generate higher pull-through, and how they use risk insight to their advantages.

Organizations are rethinking how to invest in IT to manage/update business execution and compete more effectively. A whole new world of data, structured and unstructured, online and off-line, has expanded significantly. Business leaders are emerging with a

renewed respect for risk, volatility, and flexibility, and developing a fresh agenda for growth with a careful understanding of business. To shift the focus from tactical maneuvers to strategic changes designed to fundamentally reposition and expand the business of IT, now is the time for you to reexamine where your organization is on the journey to becoming insight driven.

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Paths toward more effective business insight Some organizations follow a similar pattern of evolving how they make decisions. You may consider the following specific dimensions to evaluate the maturity of using information to drive business performance.

• From analytical application to embedding analytics into operational applications

− Are you using information for analytical applications only or for both analytical and operational applications?

• From historical to proactive insights

− Can your business only access historical insight or use careful historical information combined with on-demand insights? Can your business extend it with proactive/predictive insight?

In Figure 1, we describe the typical paths and project types that IT organizations cross and implement, respectively. On the vertical axis, you can see how many organizations are in the process of expanding the use of historical data from being limited to traditional reporting and dashboarding tasks to more operational applications that would suggest a broader staff, including frontline workers. Projects that involve “single view of x” are often initiated to build a golden record and harmonize across operational applications for on-demand analytics, instead of simply consolidating data for historical analysis. Seeking to tap the most current insight for operational processes, IT organizations shift from delivering only historical data in batch to injecting up-to-the-minute information in the context of historical information, as illustrated along the horizontal axis. Going one step further, some organizations are giving business leaders a means to proactively address the risks to avoid threats and capitalize on the opportunities that they are facing. Many organizations are also applying the results of predictive analytics and data mining to operational scenarios for improving how they treat customers and partners in a mutually profitable and satisfying manner.

Figure 1. Typical maturity path for becoming insight driven (Source: Informatica)

Top-five success factors for an insight-driven enterprise

1. Shift culture of decision making to insight driven

What is often missed yet mandatory in becoming insight driven is building the culture of becoming insight driven. Whenever we go to a company that is really good at analytics, we find that an analytical orientation is deeply embedded into its culture.1 You can sit in the driver’s seat for transformational change by looking at the specific areas that are:

• Biggest pain points • Fertile with potential opportunities that can produce better results

• Quantifiable costs and risks that you can link to

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This is due to the lack of analytical culture and making decisions without facts or meangful analysis. Once you identify those areas to infuse a data-driven approach to decision making, you can contrast the analytics-driven versus non-analytics-driven approach, and start sponsoring those core initiatives that can serve as a launching point for demonstrating the business results for using pragmatic insight and putting new insights into actions. This often involves reexamination of the organizational models, information-sharing strategies and workforce development, and new talent acquisitions that push your organization toward interacting in a new way.

2. Focus on decision management to manage/improve performance

To undertake changes necessary to become analytics driven, you can probe further into how your organization is approaching its decision making. The questions to ask about the linkage between analytics and performance are:

• Which areas of your organization are more competitive? Are they using an analytics-driven approach?

• How strong is your company’s position? Is this a result of good strategies and execution based on operating with business insight?

• Is your organization making decisions based on greater insights compared to three years ago? Did this help your company demonstrate better results?

• Do you know where future growth comes from and what your risk exposure is? Are you ready to provide expanded, emerging insights to the business to help them make better decisions in day-to-day operations?

• What decisions have more impact at the strategic level? How well is your organization aggregating the decisions enterprisewide?

Answering them provides information to help you clarify which business domains to focus your efforts on building an analytics foundation and improving/updating/enhancing the practice of managing decisions. This approach—prioritized by decision impact—also minimizes the risk of launching a massive “data-driven” effort that may not produce positive business results.

3. Improve business processes with reliable information flows

When engaged with analytics projects, you are likely to confront a significant debate about data versus process. Often, data strategies will kick off with a focus just on the analytical consumption of data within a business intelligence or reporting environment, but that focus often ignores the upstream operational processes like order management, customer service, and procurement that capture the raw data in the first place.2 Many organizations sacrifice quality and trustworthiness of data to complete business transactions, or worse, use gut feelings to make strategic decisions without reliable insight. These symptoms are the result of limited, up-front assessment and planning on when and how to inject the right information into which parts of the business process. You will also discover that inadequate process governance contributes to the usability and viability of information which, in turn, negatively affects business performance, despite the investments across process and data-rated projects. It is worth putting extra energy into synchronizing the initiatives for business process management and data governance, and aligning the deliverables from process- and data-centric views.

4. Embrace unstructured content as part of information management discipline

Sharing specific, up-to-date insight involves unstructured information including texts, audio, video, locational, sensor, Web, social data, etc. There are critical building blocks for embracing the diversity of content and unleashing the value of analytics.

• Analytics pragmatism and experimentation to prioritize the deployment of a multitude of information management projects driven by business. For instance, exploiting unstructured data to increase predictive power

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may involve data enrichment and content analysis techniques like image tagging and annotation, along with modeling relationships across documents.3 These types of social analytics and user activity modeling require the knowledge and operational understandings of target business domains, and thus often are more successful when business units lead with IT’s support.

• Common information model to account for data as an asset including the touchpoints across and beyond the enterprise, including customers, partners, and other market participants. This often includes the use of industry standards for data exchange and information sharing. Some organizations can evolve from the current department-specific model to a unifying organization-wide model, and progressively make relevant information available to improve business interactions between organizations, groups, or individuals.

• Governance program to promote accountability and consistency of treating data as an asset and sharing the business impact of data. You also need to define the areas where IT is responsible for governing information and other areas where IT lets business lead information usage and analytics. While structured and relational content has been traditionally managed by IT, it is important to keep in mind that exploiting unstructured data involves many iterations and frequent changes. Therefore, IT may serve business better by setting up high-level processes and policies, instead of attempting to govern every data movement in business domains, as long as it does not compromise security or compliance mandates.

5. Lead beyond boundaries of business and IT

Collaboration is a vital topic for any executives as they seek to increase the productivity of their employees and make each business interaction more profitable and meaningful. Interactions are becoming more varied and can have rapid, significant, and even entirely unexpected outcomes as traditional boundaries are blurring across the globe between customer relationships, partner engagements, and employer-to-employee relationships, to name a few. The dynamics between business and IT is no exception, as many organizations are putting new organizational designs and matrix management into effect. As a leader in IT, it has become ever more crucial for you to spearhead the initiatives to promote alignment and collaboration between business and IT.

Rethinking the information foundation — overcoming common challenges Executive teams are looking to their analytics initiatives to help answer a range of key questions:

• “How do we predict customer behaviors for upsell and cross-sell?”

• “What products are becoming more profitable, and how long does the profitability edge last?”

• “How can we exploit potential cost synergies in supplier relations?”

• “What additional compliance requirements will we need to address and manage?”

• “What amount of investment in which assets will drive required business performance?”

Analytics projects can add an additional degree of complexity to existing and already complex IT environments. You need to manage the business of these strategic IT projects along with endeavoring to provide the delivery of day-to-day IT operations. Despite the investments in decision management, business leaders are still feeling the pain of accomplishing and sharing necessary information that leads to fact-based decisions.

Root causes for coming up short

Complex influences and issues are at play for those pain points described above. What we often hear are:

• “There was no formalized organizational effort to transform the culture of using analytics.”

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• “We built out analytics and reporting functions without coordinating how that affects tactical, operational, and strategic decisions.”

• “We did not allow sufficient integration planning for information flow to be embedded in business processes.”

• “Access is limited to departmental, structured data and we cannot share sales or customer data across and beyond enterprise boundaries securely.”

• “Business and IT priorities are out of synch and disconnected from each other.”

These “gaps” frequently stem from the following factors:

• People — Human resources are not adequately deployed to design and implement effective analytics initiatives

− Lack of alignment and sponsorships from business and IT executives − Skill-set gaps at varying levels, including analytics pattern recognitions, translating business requirements

to data standards and information flows, modeling enterprise information, enterprise architecture planning, and readiness to use selected tools

− Organizational designs that are tightly linked to departmental success, not enterprise-level program success

• Process — Information requirements were not developed for better, smarter decision making as part of business processes

− Unable to shift program focus from bottom-up data delivery to a process- and decision-impact driven approach

− Inadequate scoping and prioritization of activities and subtasks based on assessing how aggregate decisions affect operating metrics

− Inability to control the quality of information at multiple points across business processes as part of the design considerations

− Inadequate focus on developing and governing processes that create and maintain critical data assets, such as products, customers, partners

• Technology — information backbone is not scalable or adaptable to deliver trusted information tuned to business demand and change

− Disparate systems and applications inhibit knowledge capture from the diversity of sources and the reliable flow of information to analytics users

− Lack of a common information model that can address physical and logical requirements and constraints − Incomplete view of business data, including customers, partners, products, etc., with accurate hierarchies

and certifiable linkages − Missing or inaccurate data, leading to misleading representation of the business − Inability of business users to readily tap the most updated information with historical insight to perform the

next recognized actions − Lack of detailed information infrastructure to deliver accurate business-critical data across and beyond the

enterprise Availability of timely, actionable, and trustworthy information is critical to the success of analytics projects and, thus, becoming insight driven. You have a pivotal role in guiding the direction of information infrastructure during analytics-related initiatives and working effectively with business leaders to sustain the momentum of the analytics program forward.

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Modern information management approach for achieving business success A detailed, agile information foundation is the bedrock of effective enterprise analytics, and a direct correlation between the value drivers and elements of the information foundation is required. This section will address the technology of information infrastructure and the people-process aspect of a Lean Integration practice.

Enteprise value driven by information management practice Timely, actionable, and trustworthy information is required to enable the four primary value drivers in enterprise analytics.

• Revenue growth — cross-sell/upsell a new product/service portofolio for the customer base, improve pricing, enter new market segment or geographies, and use expanded channels and distributions

• Cost reduction — develop a combined IT infrastructure, reduce duplicate roles and functions, integrate operational structures and processes, and increase negotiation power over suppliers

• Asset efficiency — improve inventory management, measure the return on assets across a broader portofolio, increase efficiencies across rationalized equipment and facilities, and improve cash and treasury management, including payables and receivables

• Governance, risk, and compliance (GRC) — Adhere to regulations, meet other applicable regulations, and advance an enterprise risk model

Each of these “value drivers” has an associated set of requirements for information infrastructure. As a result, information management competency has become one of the key indicators for the readiness and outcome of enterprise analytics projects. Information management competency incorporates six key elements as follows:

• Access to all enterprise data — aggregating any data regardless of format, including unstructured data

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• Physical and virtual data integration — flexibile foundation to access and integrate data without being restricted to physical location or type of application or data

• Data quality management — quality metrics tracking and improvement

• Master data management — single view of x with a golden record with hierarchical structure and content lineage

• Real-time delivery with historical insight — delivery of information at the right time with meaningful context

• Business availability of information across and beyond the enterprise — scalability, flexibility, and availability of a common information foundation on premise and in the cloud across and beyond the traditional boundaries of business

Table 2 summarizes how the four enterprise “value drivers” are enabled by the six elements of information management:

Table 2. Impact of information infrastructure on enterprise value

Elements of management compentency

Revenue growth Cost reduction Asset efficiency GRC

Access to all enterprise data

Aggregate demand data from all sources

Synchronized view of supplier and partner activities

Enterprise information inventory

Secure data access and archival

Physical and virtual data integration

Abstracted view from CRM, SFA, and support data

Flexible access to SCM, ERP, and payment systems

Agile finance and treasury management

Application-neutral approach to data

Data quality management

Accurate and consistent customer and purchase records

Metric-driven loss mitigation and containment

Continuous asset monitoring with certified data

Explicit risk and compliance reporting and notification

Master data management

Single view of customers with data to support cross-sell/upsell or customer retention

Product master with cross-functional context including spend records

Employee and asset master data including activity and utilization records

Finance and risk master data including risk reserves

Real-time delivery with historical insight

Customer profile available for front-line workers

Logistics data and workflow integration across the product lifecycle

Right-time inventory management and forecasting

On-time audit and real-time disclosure

Business availability of information across and beyond enterprise

Continuous availability of sales, support, and partner data

Extensible data foundation with information exchange

Standardization and reuse of asset catalogs

Business continuity and high availability

CRM = customer relationship management, SFA = sales force automation, SCM = supply chain management, ERP = enterprise resource planning

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You may consider the following set of actions for putting all elements of information management to work to achieve the four identified “value drivers.”

1. Revenue growth

• Aggregate demand data from all sources: Bring together data from combined customer, product, and other relevant internal records. Consolidate, retire, and maintain an integrated view of data with cross-business visibility for upsell and cross-sell.

• Abstracted view from CRM and SFA data: Virtualize access to core selling data sets including customer, partner, and other sales-related data. Endeavor to provide synchronized access to CRM and SFA. Expand and accelerate sales efforts by capturing customer support and inquiry logs, as well as improving sales activities with partners.

• Accurate and consistent customer and purchase records: Measure and improve the accuracy and consistency of customer records. Deliver next-best actions with targeted offerings, segment customer data, householding information, and their associated product offerings to understand purchasing trends to achieve higher share of wallet.

• A single view of customers with data to support cross-sell/upsell or customer retention: Develop a golden record of customers to perform booking analysis, forecasting, and analytics on sales and customer analytics. Make relevant interaction data from support, partners, etc., available for segment-specific and cross-divisional analysis. Endeavor to provide proper hierarchy and content-level lineage analysis tosupply reporting and analytics with use

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of metadata. Deliver context-rich, reference data to manage opportunities and pricing with expanded alliance and distribution networks.

• Customer profile available for frontline workers: Model how the frontline workers, such as sales and support staff, use information to service customers and determine what level of quality and timelines are required for customer loyalty and the propensity to do more business with your company. Empower customer support and the sales force with real-time delivery to take advantage of predictive information.

• Continuous availability of sales, support, and partner data: Assess service-level requirements for supporting operational staff who directly interact with customers. Integrate fresh, actionable insights from other areas, such as support and billing, as well as partners with human workflows.

2. Cost reduction

• Synchronized view of supplier and partner activities: Reduce duplication and look for economies of scale along the order processes and tasks for supply chain and partner management. Make a transaction history and associated pricing data available to increase negotiation power and forecast accuracy with suppliers and distribution partners.

• Flexible access to SCM, ERP, and payment systems: Tap demand insight to rationalize and consolidate view for cost tracking and monitor cash flows with access to applications deployed on premise or in the cloud. Create a better cost model that takes into account customer demand, vendor relationships, and incentives cross-enterprise and with partners.

• Metric-driven loss mitigation and containment: Link the impact of data quality to the cost of doing business as well as the burden to IT operations. Establish the data quality metrics and use scores that correlate with your cost-related key performance indicators to make the data-to-business linkage more visible. Detect underperforming data assets that are no longer in use or used incorrectly.

• Product master with cross-functional context including spend records: Establish a product hierarchy and perform segmentation assessment on the booking trend. Reduce or retire products that are decreasing the margins. Minimize manual review and intervention of multiple reports by building a product master data hub with linked databases, reporting systems, and applications.

• Logistics data and workflow integration across the product lifecycle: Resolve duplication and inconsistencies across logistics processes and workflows using high-quality data as a guide. Track and free up redundant processing and low-performing product assets, and allow partners and distributors to focus on growth products and associated offerings.

• Extensible data foundation with information exchange: Build a new model, policy, and process to extend data sharing across and beyond the enterprise. Promote collaboration between business subject-matter specialists and information management specialists to accelerate the project cycles, including requirement gathering, assessment, and prioritization processes.

3. Asset efficiency

• Enterprise information inventory: Build an inventory of asset data to measure asset performance using current, accurate data directly from the operating systems. Combine cross-business inventory data to detect waste and the misuse of raw materials, finished goods, or work-in-process assets.

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• Agile finance and treasury management: Create an integrated environment for financial transactions, payments, and risk management that delivers a view for target analytics. Monitor asset efficiency, and rationalize capital expenditures and other major items on the balance sheet.

• Continuous asset monitoring with certified data: Deploy a metric-driven approach to oversee the conversion of working capital into revenues and other productivity measures using certified data. Manage payables and receivables through integrated applications and data using quality rules, thresholds, and alerts.

• Employee and asset master data, including activity and utilization records: Develop a record of human capital and other major asset categories with utilization statistics and other records for prioritization and reallocation. Reclassify and redeploy assets using hierarchical information and segmentation to improve current and future usage.

• Right-time inventory management and forecasting: Target specific business domains to arm staff with operational intelligence to improve management of inventory and other asset classes. Establish a service-level agreement between report consumers and providers, including predictive and proactive intelligence on equipment, facilities, and other principal assets.

• Standardization and reuse of asset catalogs: Profile, capture, and catalog assets and take a phased approach to develop an integrated view. Promote standardization and reuse by functions, such as finance, human resource, customers, etc. Protect assets, including human capital, intellectual properties, and other knowledge bases by developing enterprise-level insights on how these assets are performing and are likely to perform in the future.

4. GRC

• Secure data access and archival: Classify and maintain sufficient granularity of data required for judicial and governance review. Integrate applications and control data movements to record actions, including “read” and “write.” Adhere to proper storage and archival policies.

• Application-neutral approach to data: Reestablish a top-down approach to drive business logic and workflows embedded across applications and systems to achieve GRC goals. Develop a logical architecture and implement a service-oriented approach to manage data so that the availability of information is not constrained by physical locations or functional boundaries.

• Explicit risk and compliance reporting: Demonstrate adherence to regulatory mandates and risk management goals by monitoring data quality on target data. Use historical compliance records, including real-time disclosure logs, to quantify the level of compliance and deviation, if any.

• Financial and risk master data, including risk reserves: Integrate information required for transaction-centric business processes and requisite data, including financial, customers, products, and risk, to assess and manage financial and risk management processes, including exposure. Develop a single view with associated entities and hierarchies to mitigate potential losses and the misallocation of capital reserves.

• On-time audit and real-time disclosure: Design the right-time architecture to maintain timely GRC reporting as well as real-time disclosure. Develop the business processes and workflows to address current issues and potential losses in the future. Use an adaptable infrastructure to meet future compliance and governance mandates.

• Business continuity and high availability: Revisit recovery scenarios and architect a solution to maintain high availability and business continuity for the high volume of data organization wide. Prepare for contingencies with systems that can be colocated across regions and business units.

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Figure 2 explains how the information backbone can be designed to deliver intended results from analytics. In this example, a large financial services company suffered from not having timely, actionable data, causing reporting errors, rising administration costs, and out-of-compliance risks. The myriad of disparate systems and applications contained conflicting and inaccurate data within the company. To facilitate consistent and timely analysis and forecasting, the company is reestablishing information flows along both analytical and operational processes. Critical sources of data regardless of the format are accessed whether they are external feeds from data providers or internal systems. By leveraging enterprise data integration, they accessed and normalized data from the various sources/systems where that data originated. The client leveraged data quality to proactively analyze and cleanse data and the master data management hub to create and maintain a high-quality view of customer data harmonized for enterprise use. They also implemented a role-based approach to facilitate effective collaboration of technical developers with data analysts and business owners with data stewards, traders, and risk managers.

Figure 2. Example of information management capabilities and flows to enable analytics (Source: Informatica)

Lean Integration for business-led IT transformation An effective technology foundation is a prerequisite, but not often sufficient, to achieve the intended business results. As discussed earlier, fact-based analyses often require changes in both people and process. One way to determine whether insights are incorporated into business processes is to confirm that insights are incorporated into business applications and work processes. Incorporating analytics support applications into work processes helps employees accept the changes and improve standardization and use.4 Therefore, to succeed, an organization must treat integration as a business strategy and systematically address its people and process dimensions in concert with the technology dimension. The solution that many leading IT organizations are implementing is Lean Integration. Lean Integration is a management system that emphasizes continuous improvement and the elimination of waste in end-to-end data integration and process integration activities.5 This system recognizes the common pitfalls of implementing complex integrations on a project-by-project basis that cause unnecessary expense, risk, and delay.

The lean principle puts a premium on alignment between business and IT. The major difference between a lean and nonlean approach is whether it is business led or not. For this reason, sponsorships among business and IT leaders become foundational for Lean Integration. An organizational structure is also leveraged/managed to put the “business customer” at the center to empower the team and improve the output for the metrics that business and IT mutually agreed to deliver.

Since a typical analytics initiative involves multiple phases and many departments, it is worthwhile for you to examine how to incrementally build and improve the integration and information delivery processes using “lean” techniques. For complete treatment of Lean Integration, please refer to a copy of “Lean Integration” by John Schmidt and David Lyle. 6

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Leading in the era of high expectations — driving efficiency and innovation In light of today’s volatile and ever-evolving market dynamics, the role of a CIO in empowering its organization with actionable business insight is more critical than ever before. According to the surveys of CEO and executive officers conducted by Gartner in 2010, 35% of business executives considered expansive information management in the Top- Five Strategic Contributions of IT for 2010 to 20197. Gartner advised, “Act now to define or refine an aggressive strategy and initiative to establish enterprise information management. Above all, define an approach to evolve and maintain business alignment as technology and information evolves during the coming decade.”7

Higher and broader expectations for information management also represent opportunities for many CIOs who have been seeking to direct their companies to use information as a strategic asset. CIOs adept at translating the value of data into business results are in a unique position to guide their organizations on how to effectively invest in information management to turn better decisions into tangible benefits. It is also an opportune time for a CIO to reflect on the current strategies and chart a course for transforming the culture of decision making, taking the matter of people, process, and technology head-on, and exceeding the business’s expectations.

There is heightened recognition and visibility on how the value of data is still largely buried in the expanding world of the new information economy. To compete more effectively in the global economy and/or perform at desired levels, an organization must be able to capture, analyze, and apply information more deeply and insightfully to each critical process and operational aspect of running a business. By more actively engaging in planning, design, and deployment of analytics and foundational information management initiatives, IT leaders can become more successful in delivering the intended results and increasing potential value for those IT investments. Furthermore, with greater emphasis on the insight-driven approach, you can pave the way toward embracing the value of information and more fully supporting the success of your busine

1 Davenport, Thomas H, Harris Jeanne G. and Morison, Robert, “Analytics at Work”, Harvard Business School Press, 2010 2 Karel, Rob and Richardson, Clay, “Avoid Process Data Headaches: Align Business Process And Data Governance Initiatives”, Forrester Research, Inc, November 2010 3 Taylor, James, “PARC Research – Exploiting unstructured data for predictive applications”, Enterprise Decision Management Blog, October 2010 4 Davenport, Thomas H & Harris Jeanne G, “Competing on Analytics ”, Harvard Business School Press, 2007 5 Schmidt, John & Lyle, David, “Lean Integration: An Integration Factory Approach to Business Agility”, Addison Wesley 2010 6 Schmidt, John & Lyle, David, “Lean Integration: An Integration Factory Approach to Business Agility”, Addison Wesley 2010 7 Harris, Kathy, “Gartner CEO and Senior Business Executive Survey, 2010: Fuzzy IT Futures Need Strategic Clarification”, Gartner 2010

Jane Griffin, “Business Analytics: Just Another Passing Fad?”, Deloitte Debates, 2010 Jane Griffin, “The rise of asset intelligence: moving business analytics from reactive to predictive –and beyond”, Deloitte Review, 2010

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