Is Big Data hype or ready for prime time? Once again CIOs are tasked with making sense of this new technology while charting a pragmatic course for generating real value. There are five disruptive trends shaping the corporate IT landscape today (Figure 1), and of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. Industry literature is usually focused on discussing the technical characteristics of big data—Volume, Variety, and

Velocity (the “3 V’s”)—without an adequate emphasis on the challenges associated with generating value from big data—Capacity, Capability, and Culture (the “3 C’s”). We believe an evolutionary approach utilizing a series of pilot projects supported by a network of key partners, with strong business collaboration and positive feedback mechanisms, are necessary to address these challenges and will adequately hedge investment risk while generating quick returns. Additionally, there may be network effects and first mover advantages; therefore, it is imperative that organizations begin this process now.

Drive Your Business

Starting Small with Big Data A Pragmatic Approach to Generating Business Value

Strategy Brief | Big Data

1

There are five disruptive trends

shaping the corporate IT landscape today, and of the five, Big Data has the biggest potential to generate new sustainable competitive advantages.

Figure 1: Five Disruptive Trends

Responsibility for IT is Moving to the Business

Convergence of BPO and ITO

Big Data, Mobility and Analytics

Commoditization of IT and Global Delivery

Consumerization of IT

1

2

3

4

5

Source: WGroup

“ While the market for Big Data is real, it is important to remember the fundamental differences between Big Data Analytics (BDA) versus traditional analytics and how they complement each other. Both are needed for a comprehensive analytics strategy.”

Eric LiangPrincipal Consultant WGroup

Unique Characteristics of Big Data Analytics (BDA)The market for Big Data is real; it is projected to be growing to $16B by 20151, equal to or more than a third the size of the ‘traditional’ BI (Business Intelligence) and analytics market and at twice the speed. However, it is important to remember the fundamental differences between Big Data Analytics (BDA) versus traditional analytics, how they complement each other, and why both are needed.

Much has been said about the Volume, Variety and Velocity of Big Data. While a plurality of companies nowadays typically deal with data sets exceeding 10’s of terabytes, analyses using large data sets do not necessarily leverage the uniqueness of Big Data Analytics. We have segregated data-driven analytics into three categories: ‘Simple’ problems use relatively simple algorithms to manipulate (e.g., slice and dice) small to moderately large, structured data records (see Figure 2). This is the domain of traditional BI. ‘Quant’ problems are those that require highly specialized numerical analysis to operate complex algorithms, use intensive computational power for a single solution, and the algorithms will grab as little or as much data as needed in the course of computation. Examples include DNA sequencing, protein folding, nuclear physics and computational fluid dynamics in aerospace.

By contrast, true ‘Big Data Problems’ are those that use relatively simple algorithms to mine, associate, or discover patterns from huge data sets that include lots of unstructured data. Some problems, like security monitoring, are inherently Big Data Problems because patterns can only be discovered by examining voluminous data sets. Others, like Google Search, choose to use large datasets and incremental learning algorithms in lieu of approaches that use less data and more complex algorithms. Thus, BI focuses on individual transactions, whereas BDA seeks to predict trends and anticipate opportunities. Recognizing this distinction, the proper architecture for a company expanding into the world of BDA would probably look like the diagram in Figure 3.

Big data analytical techniques include, for example, content analysis, sentiment analysis, text analysis and natural language processing, associative analysis, plus the ability to do predictive simulations (predictive analysis) and return recommendations with low enough latency to enable interactive decision-support (real-time analytics.)

1 D. Vesset et al, “Worldwide Business Analytics Software 2012-2016 – Forecast and 2011 Vendor Shares”, IDC

WGroup2

Big Data Problems

‘Simple’ Problems

Quant Problems

Algorithmic Complexity D

ata

Volu

me

Figure 2: The Three Categories of

Data-Driven Analytics

Source: Chris Swan, ”Big Data – a little analysis,” Chris Swan Weblog, Apr 2012.

Implementing Big DataLaunching into Big Data is difficult given the large number of projects and initiatives typically underway in most IT organizations coupled with the perceived lack of benefits experienced from traditional BI and Data Warehousing efforts. The challenges to successfully implementing big data revolve around Capacity, Capability and Culture:

Capacity—Most IT organizations are operating under resource constraints. Additionally, traditional BI and Data Warehousing efforts continue to drain resources due to data issues, integration problems, etc., reducing their effectiveness and perceived value. The appetite for adding new capacity is very limited, and where approved directed towards addressing problems with current systems.

Capability—Even where there is excess capacity, the resident skills do not match what is necessary to implement Big Data. Existing IT resources are often unable to effectively assimilate leading-edge technical capabilities. Brand new capabilities are also required—a combination of strong data modeling, statistical analysis with business domain and process knowledge—the so called “data scientists.” These resources are in short supply.

Culture—Lastly, cultural issues play a big factor in the ultimate success of using Big Data. Managers are typically used to making decisions based on past successes and subconsciously carry an intuitive model of how the business operates. Intuitive insights may not be transferrable from one experience to another, or at

“ The ‘Three C’s’ of Capacity, Capability and Culture are key to understanding a framework for a successful implementation of big data technologies.”

Eric LiangPrincipal Consultant, WGroup

Starting Small With Big Data 3

Figure 3: A Reference Architecture for Big Data Analytics

Source: WGroup

T he real challenge is to transform an

organization from an intuition-driven decision-making culture into one that is data-driven.

the very least be only partially informed, especially in today’s hyper-competitive environment subject to the interplay of macro-level trends. Intuition must be backed up with data and facts. This requires a sea-change in behavior to get to a more data-driven culture—using data to balance intuition and vice versa.

Given this understanding, we recommend an evolutionary approach to the deployment of big data capabilities that targets investments, minimizes risk, and captures value while building longer-term capabilities:

WGroup Approach to Big Data1. Pick an analytics-friendly cross-functional team; challenge team to

identify top business opportunities based on Big Data concepts

2. Evaluate and prioritize top opportunities for piloting

3. Develop prototype process for implementation

4. Measure results; share experience and data sets with other teams for process replication

Picking the right team to delve into BDA is important, because a key to extracting Big Data insights is the ability to pair analytical skills with detailed business knowledge in order to address relevant business problems in context. Furthermore, the real challenge is to transform an organization from an intuition-driven decision-making culture into one that is data-driven. An analytics-friendly team would at least already have the beginnings of using hypotheses-testing-feedback loops as a habit. To the extent that strong statistical and/or data modeling skills are not available in-house (but are almost certainly required for a BDA initiative,) an analytics-friendly team would also be in the best position to acquire and assimilate such talents.

WGroup4

Assess & Indentify Opportunities

Prioritize & Select Pilot Opportunity

Develop Implementation

Plan

Implement & Assess Results

Prove Business Value

Assess Current Capability

Identify Opportunities

Evaluate ROI Potential

Prioritize & Recommend

Identify Partners

Develop Infrastructure

Develop BD Analytics

Feedback& Learning

Figure 4: Evolutionary Approach to Building BDA Capabilities

Source: WGroup

Traditional BI3 Big Data Analytics

Rationalizing and reducing operational costs

Trend sensing and operational log analyses from numerous sensors enables predictive analytics

Improving the customer management process

Customer profiling, segmentation & sentiment analysis based on more dimensions, leverages unstructured data. Analysis is more granular and fluid

Maximizing operational agilityEmbedded BA with minimal latency enables CEP4 (Complex Event Processing) and real time responses

Enhancing business performance alignment across the enterprise

Variety of data sources for analysis present holistic view for enterprise-wide strategic decision support

Avoiding unnecessary risk exposure and ensuring adherence to regulatory compliance

Sophisticated pattern and anomaly detection no longer hampered by small sampling sizes or sample bias

3 Helena Schwenk, “Business intelligence and analytics fundamentals,” Ovum, July 20104 Nenshad Bardoliwall, “The Top 10 Trends for 2010 in Analytics, BI & Performance Management,” Dec 2009

Being creative in coming up with business opportunities based on Big Data concepts should not be hard, but to evaluate and project the value of such initiatives would be. Here an important concept is the so-called ‘Return-On-Data.” Traditional analytics, using structured, cleansed and carefully sampled data, are able to extract useful insights out of a relatively small data set (insight per byte), yet the cost of acquiring that pre-processed data is high per byte. By contrast, Big Data tends to require large quantities of data to extract one insight, which is why it must use techniques, algorithms and infrastructure (Hadoop-based distributed storage and massively parallel processing) with low cost per byte to justify the economics. Thus the key metric is to maximize the ratio of these two “per byte” numbers.

Once a pilot project from among the creative Big Data business opportunities is selected, the next step is to select technology partners for both the infrastructure and analytics tools. Typically, Big Data infrastructure choices are different than traditional BI; e.g., direct-attached storage (DAS) and high capacity SATA disks sitting inside massively parallel processing nodes are preferred over slower shared storage. Simultaneously, a new process needs to be defined, starting with how the data from varied sources are to be gathered, integrated and governed.

Finally, in the fourth step, the analytic models need to be deployed and the business benefits measured. It has been reported that companies using data-driven decision-making enjoy 5-6% boost in productivity2. More importantly, BDA initiatives should focus on extracting insights not discoverable using smaller, structured data sets. Some typical BDA benefits are contrasted with those obtainable from traditional BI in the table below:

2 Erik Brynjolfsson et al, “Strength in Numbers: How Does Data-Driven Decision Making Affect Firm Performance?” MIT, Apr 2011

Starting Small With Big Data 5

A key criterion to the evaluation of big data

initiatives is the so-called ‘Return-On-Data’ metric. Cloud-based, massively parallel processing and storage have brought this metric into viable territory for Big Data sets.

How WGroup Can HelpSimilar to any new technology that has the potential of transforming existing operational models, the adoption of BDA should be approached systematically by starting with a careful formulation of the overall strategy. WGroup can assist you with identification of early opportunities, development of a medium term (2 – 5 years) strategy, formulation of a pilot project initiative and implementation plan, or facilitation of selection and engagement with technology providers. Please refer to Service Offerings list below and contact WGroup for more information.

Contact UsWGroup 301 Lindenwood Drive, Suite 301Malvern, PA 19355610-854-2700

Copyright © 2012 WGroup. All Rights Reserved.6

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About WGroup

Founded in 1995, WGroup is a boutique management consulting �rm that provides Strategy, Management and Execution Services to optimize business performance, minimize cost and create value. Our consultants have years of experience, both as industry executives and trusted advisors, to help clients think through complicated and pressing challenges to drive their business forward.

For more information on WGroup, visit http://thinkwgroup.com

Figure 5: WGroup Big Data Service Offerings

■ BDA Pilot Initiative De�nition and Planning

■ BDA Operating Model Design

■ BDA Requirements Capture and Vendor Selection

■ BDA IT Strategy Development

■ BDA Assessment & Recommendation (quick scan)

■ BDA Pilot Implementation and Value Realization

WGroup assists clients in Big Data

opportunity identification, strategy assessment, pilot execution and continuing management. The four steps should form an iterative feedback and learning cycle.