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Analytics Magazine

Better Decision-Making: How HP delivers business value with enterprise-wide analytics services

July/August 2010


A common approach to deploying business analytics across the enterprise to elevate the analytical maturity and deliver actionable intelligence.

Sanjay Singh Analytics Magazine Prithvijit Roy Analytics Magazine Arnab Chakraborty
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By Sanjay Singh (left), Prithvijit Roy (center) and Arnab Chakraborty (right)

Conversation around business analytics is becoming a boardroom discussion. With the abundance of data, information and content that enterprises have today, enhanced focus is on the discipline of business analytics to enable better business decision-making across the enterprise. This article provides a unique perspective on how companies should create an enterprise-wide approach to deploy analytics decision-making, as well as a summary of the journey that Hewlett Packard took in deploying analytics in a shared services model.

Enterprise-wide Analytics Paradigm

IIn terms of an enterprise-wide approach to deploying analytics, organizations pass through different levels of analytical maturity as shown in Figure 1, typically evolving from companies with limited capability of data-driven decision-making to sophisticated analytical organizations. The enterprise-wide analytics approach helps to identify processes with low analytical maturity, where the organization can use common data, analytical tools and techniques to enable better, efficient and pro-active decision-making.

better decision making analytics

Figure 1

The ability of the organization to bring together certain key enablers determines the analytics maturity of the enterprise. The key enablers are:

  • People – Enhancing the depth of analytical skills, consultative skills and building a critical mass of business domain experts.
  • Techniques – Deploying simple to use techniques and constructs of logical frameworks, mathematical modeling and visualization to analyze the business data and represent findings effectively.
  • Tools – Deploying a standard set of scalable tools to carry out data management, reporting and data mining.
  • Technology – Deploying an enterprise-wide data and technology platform both in terms of hardware and software to deliver the high quality analytics services in a cost-effective and real time manner.

However, getting to an enterprise-wide analytics platform calls for a long-term strategy and requires considerable management commitment (as shown in Figure 2). The journey entails:

  • Developing successful pilots, establishing the initial talent pool/leadership and building credibility.
  • Establishing standard processes and frameworks to deliver the output in a replicable and predictable manner.
  • Scaling up the footprint across business units (BUs) and regions to attain sponsorship.
  • Building innovative solutions/IP that become an asset to be leveraged across the enterprise.

Based on the HP experience of building up the enterprise-wide analytics shared services model, this is a four-to five-year journey at minimum.

better decision making analytics

Figure 2

In 2005, HP Global Business Services (GBS), which forms the shared services organization for HP, decided to set up a central group of analytics practitioners to drive business analytics across the enterprise. The group has since evolved significantly.

The HP Story

The analytics shared services group within HP is called Decision Support and Analytics Services (DSAS). DSAS is a multi-functional analytics center based in India that drives data-driven decision-making across sales, marketing and supply chain functions by leveraging structured and unstructured data. This is done with a key focus to influence key business outcomes for HP in terms of revenue growth, risk minimization and cost reduction. The analytics team at HP has invested in top-notch talent comprising professionals with advanced degrees in quantitative disciplines and rich industry and functional experience.

At DSAS, the analytics practitioners apply sophisticated research techniques, data-mining and predictive modeling to enable better decision-making. Over the last five years, this has led to the creation of pan-HP Analytics Centers of Excellence (COE) (Figure 3). COE work on enterprise-wide data sources/ structures, apply a common set of data-mining and modeling tools and leverage best practices to deliver actionable intelligence in a cost-effective and efficient manner.

better decision making analytics

Figure 3

Market Insights is one of the most mature COEs in DSAS, and the evolution of Market Insights in DSAS – from standard market share reporting to advanced competitive analysis to predicting market size for HP – illustrates the value of housing analytics in a shared services organization (Box 1).

Analytical Innovation in a Shared Service Model

As in any other setting, innovation in a shared service model requires a combination of skills, organizational commitment, planning and investment. In DSAS, innovation is driven through a multi-fold approach.

  • Innovation bubbles up organically through the domain expertise and deep understanding of the data that the team works with.
  • Complementary partnerships with internal innovation hubs in HP such as HP Labs & SPaM (Strategic Planning and Modeling).
  • Targeted investment in areas where DSAS is best positioned in the organization to generate business value.

These efforts have resulted in recognition from both internal HP innovation forums and external competitions. Recently, a DSAS project on Supply Chain Stock Outs Prediction won the Wharton Innovation Award. DSAS Projects have also been recognized in HP-Internal events such as Tech Con and the HP Circle Awards.

Driving efficiency and effectiveness in market intelligence
In 2005, Market share & sizing reporting and analysis in the enterprise business (eb) of Hp was delivered locally in disparate formats with significant differences in quality and timeliness. With internal and external benchmarking, DSAS identified best-in-class reporting and analysis methods and redesigned the market intelligence process to a standardized, central model with significant automation, improved accuracy and data transparency. using tools such as SAS, Access and Excel, dsas established a standard data cleansing, data validation and data organization process for different market data sources (such as idc trackers) to make it consistent with Hp’s view of the market. a set of key design principles around the taxonomy, presentation, competitive and regional views was also identified for all reports. A clear set of design principles and an organized database also helped automate the reports generation process.All this led to productivity gains of ~80 percent. the success in transforming market intelligence for eb led to recognition from other business units as well. today, Market share & sizing reports are centrally delivered for all the three business groups in Hp. the team has also invested in developing desktop apps and intuitive tools that provide self-service capabilities to the broader market intelligence community. Over time, the team has been to deliver similar benefits to the competitive intelligence and primary market research processes. More time has been freed up for the marketing teams for investment in higher-impact marketing activities.
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End-to-End Business Value

With increasing maturity of DSAS’ analytics portfolio and enhanced focus on innovation, HP businesses today see DSAS as an integral partner in achieving their core business objectives – be it boosting revenue growth, optimizing costs or mitigating risks. DSAS’ engagement with the HP’s Direct-to-Consumer Business ( is a good example of how analytics is seen as a critical driver for transformational impact to the business.

Analytics-based tool to predict stock outs in supply chains
In early 2009, Hp’s channel partners in the europe region for top value product lines such as laptops, desktops and handhelds were faced with higher stock outs. it was unclear why stock outs had increased from 1 percent to about 5 percent to 6 percent. What this meant for Hp was not only a significant loss of revenue, but also a loss of customer goodwill and credibility. dsas was called upon to investigate the causes for these stock outs and to develop a tool to predict future stock outs.The problem was solved in two phases. In the first phase of the project, the team investigated root causes for stock outs and identified the correct variables that would help generate signals around stock outs. in the second phase, an early detection tool – “signature” – was developed, which would help predict a stock out in the future so that action could be taken to prevent it.

for each product, the tool calculates a “stock-out parameter.” based on the value of the stock-out parameter being beyond different threshold levels (Ts = Threshold 1, 2), the tool identifies “high,” “medium” and “low” probability of stock outs. Within three weeks of implementation, the stock out level decreased from approximately 5 percent (pre-tool implementation) to about 3 percent.

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Benefits and Learning

In summary, investing in a central analytics group like DSAS to deliver enterprise-wide analytics has yielded rich dividends for HP. These include:

  • Economies of scale from multifunctional analytical services that help drive an end-to-end solution for the enterprise.
  • Economies of location – Centralization of the talent pool in a few centers drives best practices and career development.
  • Economies of skill – Getting the right analytical talent, equipping them with the right tools and building intraorganization knowledge networks has enabled creation of centers of expertise.
  • Economies of process through process standardization and simplification, common enabling tools and technologies, and continuous process improvement.
  • Business impact – From predictive analytics and close collaboration with businesses and other niche innovation hubs, the organization was attributed with millions of dollars in direct and indirect business impact.

As data explodes within and outside the enterprise, business impact and innovation targets will only increase for organizations. Companies that invest in a central analytics organization to elevate the analytical maturity are well positioned today to compete effectively in the marketplace.

Sanjay Singh is vice president, HP Global Business Services. Prithvijit Roy is director, HP Global Business Services. Arnab Chakraborty ( is an analytics service delivery leader, HP Global Business Services.

Analytics for the U.S. direct-to-consumer store
The online consumer segment presents a multibillion-dollar market opportunity for Hp. in 2008, HP direct engaged with gbs-dsas to realize the objective of gaining share in this highly competitive and growing market.From developing a standard performance measurement framework that helps manage daily operations and marketing campaigns that drive traffic/sales, to building sophisticated predictive models that help plan and meet financial commitments, GBS-dsas is becoming critical to HP direct’s success in the marketplace.The end-to-end analytics support spans the planning, demand generation, operations and category management business functions. dsas, with its deep knowledge of customer and business performance data and the application of analytics/computing tools such as sas, R and omniture, provides:

  • An enhanced understanding of customer behavior on the Web site through Web analytics – click-stream analysis, cart abandonment analysis, segmentation, etc.
  • Recommendations on which marketing levers work (price, promotion, placement) and what needs to be tested across the main marketing channels – online Web store and call centers.
  • Measurement of impact on performance metrics through statistical testing (currently done) and experimental design techniques (work in progress).
  • Predictive models to improve demand generation forecast accuracy that helps the business improve accuracy of financial forecasts and adherence to them.
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