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Can it be managed in real time, or nearly so? This competency also involves a rigorous approach to data governance, a structured management approach designed to track strategic objectives against the allocation of analytical resources. Decision makers at every level of the organization can then be confident they have the right information to do their jobs effectively and make informed decisions using analytics to guide day-to-day operations and future strategies.

Transformed organizations effectively manage data: percent proficient, Transformed versus Aspirational organizations.

Why Big Data is the new competitive advantage

Organizations that deploy new skills and tools for analytics can typically answer much harder questions than their competitors. Which customers, for example, are most likely to opt into high-margin services? How will specific shortages within the supply chain impact future delivery capabilities? Competency in analytical skills and tools , essential for answering key business questions, can be achieved through internal development and cross-training or external hiring and outsourcing in areas like advanced mathematical modeling, simulation and visualization.

Advanced skills and techniques also make it possible to embed analytical insights into the business so that actions can take place seamlessly and automatically. Embedded algorithms automate processes and optimize outcomes, freeing employees from routine tasks for example, looking for customer records to process a claim or repeatedly recalculating variables to determine the best distribution route. As a result, individuals have time to apply data and insights to higher-level business questions, such as using analytics to detect fraud or finding patterns that yield new customer insights.

One key success factor in achieving mastery of this competency is the creation of analytics champions. Transformed organizations have analytics champions that initiate and guide activities by sharing their expertise to seed the use of analytics throughout the enterprise. These specialists pair expertise with a deep understanding of the business. They are able to provide guidance in getting started with analytics, as well as identifying resources for ongoing support.

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Transformed organizations understand the data: percent proficient, Transformed versus Aspirational organizations. In a data-oriented culture , behaviors, practices and beliefs are consistent with the principle that business decisions at every level are based on analysis of data. Leaders within organizations that have mastered this competency set an expectation that decisions must be arrived at analytically, and explain how analytics is needed to achieve their long-term vision.

Organizations with this culture are likely to excel at innovation and strategies that differentiate them from their peers see case study, BAE Systems: A New Business Model Takes Flight. They typically benefit from a top-down mandate, and leaders clearly articulate an expectation for analytical decision making aligned to business objectives. Transformed organizations, in fact, are nearly five times more likely to do this than Aspirational organizations. In these data-driven cultures, expectations are high. They express their conviction in the value of faster and more precise decisions by using analytics to guide to day-to-day operations.

Employees are confident they have the information to make data-based decisions. They are encouraged to challenge the status quo , and follow the facts in order to innovate. Transformed organizations are more than twice as likely as Aspirational groups to be receptive to new insights. Transformed organizations act on the data: percent proficient, Transformed versus Aspirational organizations. Each of these three competencies — information management, analytics skills and tools, and data-driven culture — is critical to analytics sophistication.

Mastery of these competencies is how Transformed organizations manage, understand and act on data to create a competitive advantage. For organizations seeking to emulate Transformed organizations, it is useful to know which actions have the biggest impact on their level of sophistication. Analysis showed that of all the characteristics exhibited by Transformed organizations, their proficiency represented by the percentages in six characteristics distinguished them the most see Figure The breadth of these leading characteristics suggests that excellence in all three analytics competencies noted in our study is fundamental to the competitive use of analytics.

An organization may be able to capture, integrate and analyze its data, but it will not likely be able to act on what it finds unless it has a culture that is ready to embrace ideas that depart from intuition or experience. For example, a leading global bank transformed its operations when it decided to analyze the impact of debit and credit card purchases on mortgage default settlements. The bank was able to use this new customer information effectively because it developed a culture that encouraged multiple departments to collaborate on managing, understanding and acting quickly on data and ideas that went above and beyond traditional approaches to lending decisions.

In using analytics as a strategic asset core to their business and operations, Transformed organizations embed data-based insights into every process — from scenarios that manage risk, to algorithms that process orders coming in through new digital channels. Going one step further, they also empower employees to act confidently and decisively in a fast-paced marketplace. For example, a global telecommunications company faced customer attrition that was rising by double-digit percentages. It quickly succeeded in stemming these defections after using social network analysis to re-segment its portfolio, then comparing segment profitability to create customized solutions for use by call center employees. Only by providing data and insight to employees across the enterprise are organizations able to benefit from fresh perspectives of customers and operations.

While Transformed organizations serve as benchmarks for establishing analytics competencies, almost half of the organizations we surveyed are at the Experienced level, somewhere between the most basic and the most advanced segments. We found that organizations, after starting, diverge in their approach to analytics. We characterize the alternative paths as Specialized or Collaborative, based on the way analytics is leveraged and deployed:.

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The Specialized path. Deep analytics expertise is developed within lines of business or specific functions using a wide array of analytical skills and techniques. Analytics is used to improve specific business metrics. Slightly more than half of the Experienced organizations took this route. The Collaborative path. An enterprisewide information platform is created, enabling insights to be developed and shared across lines of business.

Analytics is used to improve enterprise objectives. Slightly fewer than half of Experienced organizations took this route. See Figure 12 for a comparison of the relative proficiency levels these paths exhibit for each of the three analytics competencies. With impetus coming from within lines of business, organizations on the Specialized path pragmatically focus on improving their operational metrics while growing revenue and increasing efficiency. They use their analytical prowess in advanced skills and techniques, such as predictive modeling, to focus on orchestrating marketing campaigns and finding the best match between individual customers and sales representatives.

In addition to the revenue gains resulting from these programs, the Specialized path takes organizations through a wide range of efficiencies and cost savings.

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Predictive scenarios and simulations, for example, make it possible to understand how changes caused by internal strategies and external forces will impact individual units in terms of resource allocations, revenue growth and operating costs. We found that organizations on this path increased their use of analytics over the last 12 months, but rarely as a core part of the overall business strategy. Information management is siloed. However, integrating and disseminating data across the enterprise is a hurdle they have yet to overcome.

On the Specialized path, identification and selection of projects is made within business units, often by using process-driven problem-solving methodologies like Six Sigma. Analysis takes place where and when insights are needed, or by analytics departments within the business lines. While this approach serves individual business lines well, it can create or deepen barriers to developing the information management competency, because collaboration for effectively integrating and sharing enterprise data is insufficient or lacking.

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Improvement of analytical skills and tools is a passion. On this path, organizations are eager to keep up with new technical advances and apply them to the data they have on hand. Within their individual lines of business, these organizations have the capability to spot and analyze trends, patterns and anomalies.

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Passionate about a wide range of analytical tools, organizations on this path embark on a journey that takes them far beyond spreadsheets and basic visualization techniques. For budget planning and resource allocation, what-if scenarios are used to predict threats and opportunities. Algorithms automate tasks ranging from mundane report development to complex data analysis.

And a wide range of discrete business processes, such as automatic inventory replenishment or call center assignments, are optimized by embedded algorithms. Data-oriented culture will require extra momentum. On the Specialized path, organizations are open to exploring new analytical techniques and applying them liberally within discrete areas of the business. However, when it comes to taking an enterprise approach, most respondents considered the organizational challenges extremely difficult to confront and resolve.

Political constraints and a lack of cohesion within the organization can be major barriers to integrating data and using analytics for enterprisewide objectives. Unless these hurdles are overcome, the Specialized path to analytical transformation may reach a point of diminishing returns as siloed programs impede establishment of analytics as a core enabler of business strategy and operations. Either a strong push from senior leaders or grassroots momentum from individuals at many levels will likely be required to create a culture that is open to new ideas and ready to move forward on the basis of fact-based insights.

By contrast, organizations taking the Collaborative path use analytics more broadly and effectively. Unlike Specialized organizations, which typically have pockets of excellence in one area or another, Collaborative organizations achieve consistent levels of effectiveness across functions.

Like a rising tide that lifts all boats, analytics in Collaborative organizations spreads beyond finance and operations to bring capabilities to the same levels across unit and function — from marketing and sales to human resources to strategy and product development. By connecting information and programs across silos, organizations can create an agenda that makes analytics core to operations and business strategy. In doing so, the Collaborative path creates an appetite for new ways of understanding value and competitive advantage that permeates the entire organization see case study, Pfizer: Next Generation Sales Insights Through Analytics.

On the Collaborative path, organizations draw on information from many functions and departments. Consequently, they may be better positioned to create seamless one-on-one interactions with customers across channels and over time. Not surprisingly, they are twice as likely as organizations taking the Specialized path to provide customer-facing employees with access to data and insights. Information management is an enterprise endeavor. On the Collaborative path, organizations gain valuable ground by applying themselves to the integration of disparate data into an enterprise analytics platform.

This cross-unit endeavor is enabled by a willingness to share and accept data and insights from other parts of the organization. The enterprise moves toward consistent data definitions, data management standards and shared responsibility for analytics. Analytics skills and tools are not fully developed. Despite comparative weakness in analytics skills and tools, organizations on the Collaborative path are adept at using visualization techniques. Data visualization and departmental dashboards provide snapshot views of performance.

These user-friendly approaches help individuals who are less accustomed to working with large quantities of data interact with information and make analytically based decisions. A data-oriented culture has emerged. Organizations on the Collaborative path integrate data from silos and then disseminate the insights across the enterprise.

They are almost three times more likely to use analytics to guide future strategies than Specialized organizations, and twice as likely to rely on analytics for day-to-day operations. Collaborative organizations have cultures where individuals are prepared to challenge current ideas and practices on the basis of new information.

To support this culture, they are twice as likely to provide insights to anyone in the organization who needs them. These organizations enjoy executive-level endorsement for the broad use of analytics to manage day-to-day operations and shape future strategies. Ideally, as organizations begin their transformations to analytical sophistication, they start building a solid information foundation and acquiring analytics capabilities simultaneously. In reality, we find that they tend to do one or the other, based on their existing culture, organizational structure and skills.

Knowing what is likely to happen and what resource constraints there may be, models can be created to give guidance as to what to do and in what order to create the best business outcome — taking into account such things as business strategies, available resources, market forces, customer preferences, and physical reality.

Advanced Analytics create profound insights that can transform business performance — all in an automated way, sifting through facts buried in millions of transactions and other records.

Advanced analytics do not pertain solely to customers and markets. Advanced Analytics can do such widely different things such to help detect fraud, improve health outcomes, and enhance customer service. The biggest ROI comes when organizations make strategic use of analytics that predict and optimize future business outcomes.

A Very Short history of Data Science Shortage of skilled people: At this rate, there will soon be a shortage of talented analysts who can help organisations work with this much big data. A Data Scientist's Real Job:Storytelling Critical business impact: Some specific applications of Business Analytics, which allow for many novel avenues of opportunity for businesses to optimise and adapt their business model, are: critical product analysis; improved customer service; up-selling opportunities; simplified inventory management; and competitive price insights.

Individual growth: As an individual it will help to move up the Data —Information — Knowledge — Insight value chain. This will enable to you to apply the techniques of BA to have a positive impact at your work and consequently succeed in your career aspirations. Quick Links Contact us. Know more. The right use of Big Data allows Analysts to spot trends and give niche insights.

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