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REAPING THE BENEFITS OF BIGDATA AND REAL-TIMEANALYTICS

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Big Data Analytics: Are Today’s IT Orga-nizations Equipped to Reap the Benefits?

Real-time analyt-ics brings BI data directly into business operations

REAPING THE BENEFITS OF BIG DATA AND REAL-TIME ANALYTICS

he majority of enterprises are either em-barking on initiatives related to big data or intend to do so in the near future; however, most IT organizations do not have an ar-

ticulated big data strategy that ties technological solutions to busi-ness goals and objectives. This E-Guide explores how organizations can embrace big data and real-time analytics to deliver immediate improvements in business performance and gain a competitive edge on rivals.

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BIG DATA ANALYTICS: ARE TODAY’S IT ORGANIZATIONS EQUIPPED TO REAP THE BENEFITS? George Philip

Market-leading enterprises have been investing heavily in analytical appli-cations to derive competitive differentiation, market growth, revenue maxi-mization, cost optimization, and to address aspects like regulatory reporting requirements. They make thousands of decisions every day at different levels of the organization with differing degrees of complexity, impact, frequency and predictability.

Typical operational decisions are highly structured, repeatable and made at a higher frequency based on well-documented decision logic and a structured decision-making process. Due to this, the degree of automation of these pro-cesses tends to be high with the exception of human collaboration in decision making that is too complex to automate. Operational decisions individually have low impact on the overall organization; however, they can have significant impact if viewed collectively.

On the other hand, strategic decisions tend to be highly interactive based on


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non-routine, unstructured and ad hoc analysis. Though higher-level decision processes could be defined for some of the decisions of a repeatable nature, the degree of automation possible is low given the high degree of collaboration re-quired among the decision stakeholders. These strategic decisions tend to have a low degree of repeatability and frequency, yet individually they have a high impact on the organization. Though these decisions tend to be made at differ-ent levels within the organization, they are highly interconnected. Identifying, capturing, analyzing and managing these interconnections tend to be critical for overall decision-making effectiveness.

It is because of these interconnections that organizations are beginning to look beyond standard business intelligence (BI) and reporting to take bet-ter advantage of emerging data sources, optimize business processes, gain deeper insights, address complexity, reduce regulatory risks and evaluate new business scenarios in order to leverage competitive differentiation. While BI implementations are typically meant for tracking and managing business key performance indicators (KPIs), advanced analytics implementations provide more far-reaching organizational benefits. Typically, operational decision making is supported by descriptive and diagnostic analytics that tend to focus on the past and present while strategic decision making needs to be supported


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with predictive and prescriptive analytics approaches that can look into the futuristic business aspects.

NEW DATA SOURCES

Traditionally, organizational transaction data with limited attributes tended to be the mainstay of business intelligence and advanced analytics. Today there are new, burgeoning sources of data available that can enrich limited organizational data with additional attributes that can describe an entity in a much more detailed manner. For example, it is now possible to enrich the information available about a customer from within the enterprise transac-tion systems with additional customer attributes that have been gleaned from external social engineering interactions, marketing interactions, customer service interactions, complaints interactions, maintenance interactions and other similar sources to give a richer picture of the customer and his/her inter-action with the enterprise. This enhanced understanding about the customer typically improves behavioral modeling capabilities, resulting in better predic-tion of organizational interventions and likely outcomes. This data increases the organizational ability to attract and retain more profitable customers who will improve both top line and bottom line growth.


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SHORTCOMINGS OF TRADITIONAL APPROACHES

These new sources of data tend to be of much higher volume, velocity, variety and complexity and include unstructured text, weblogs, voice data, machine data and other variety of non-traditional data structures. Traditional approaches for tackling these new big data sources tend to be inefficient, and organizations are gradually being forced into the adoption of relatively new data management approaches like Hadoop, HDFS, MapReduce, stream ana-lytics and related architectural constructs that can programmatically analyze these data structures for faster decision making.

Major business opportunities for big data are not limited to decision mak-ing or incremental improvements. Big data can transform the business and dis-rupt the overall industry through asking and answering highly computational questions that were never possible before. This has the potential for radically changing existing business processes, and opening the door for the introduc-tion of new processes, new products and services that are aimed at the prover-bial “market of one” customer segments that were previously out of reach.


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BIG DATA STRATEGY

While line of business is highly enthused about the deeper business benefits foreseen from these additional information sources, most IT organizations today are facing a challenge in adopting big data concepts to augment their existing information management paradigm. Though most enterprises are ei-ther embarking on initiatives related to big data or intend to do so in the near future, most IT organizations do not have an articulated big data strategy that ties technological solutions to business goals and objectives.

Many big data initiatives are originating from within business units, and this increases the pressure on IT organizations to get adequately prepared to support them. Low-cost big data processing solutions are permitting deploy-ment of such solutions at a much faster pace than previous technological ad-vances in this area. Many IT organizations also tend to primarily look at big data from a volume perspective. Volume by itself is transitory in nature with ad-vancing hardware architectures able to consume them at a faster pace; hence, more focus needs to be placed on addressing the variety, velocity and complex-ity aspects. Business benefits tend to be higher while addressing these aspects.

In this scenario, it is the right time for IT organizations to take the lead in developing an enterprise-wide big data strategy. One of the first steps in this


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journey is the evaluation and selection of all architectural and infrastructural components to ensure ability to adequately support anticipated growth in vol-ume, velocity, variety and complexity aspects of data. Data governance, policies and controls need to be augmented to ensure all big data sources and use cases are adequately managed to prevent emerging business and regulatory risks. Because big data implementations require specific data management, quality, preparation and analytics skills, specific focus needs to be on obtaining these skills at the right time.

Once a carefully planned enterprise-wide big data strategy is in place, the floodgates of new advanced analytics capabilities can be opened to leverage the enhanced business value derived from these new data sources.

GEORGE IS currently Vice President & Global Practice Head of Analytics & Information Management at Mindtree. George has decades of hands-on experience in architecting, managing and delivering analytics, business intelligence, information management, information governance, data warehousing, performance management, master data management, CDI, data quality and metadata management applications for large global clients across industries including telecom, banking, financial services, insurance, retail, technology, government, education, media, entertainment, manufacturing, electronics, and services. He is an active speaker at international conferences such as the Gartner BI Summit and plays an advisory role in various industry forums. George holds a post graduate diploma in business administration and a bachelor of technology in computer science.


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REAL-TIME ANALYTICS BRINGS BI DATA DIRECTLY INTO BUSINESS OPERATIONSBeth Stackpole

Looking to generate immediate improvements in business performance and gain a competitive edge on rivals, companies increasingly are trying to take ad-vantage of business intelligence and analytics tools not just to garner strategic insights, but also to drive operational decision making in real or near real time.

Real-time analytics -- or operational intelligence, as many prefer to call it -- has been heralded as the next logical progression for BI deployments. Never-theless, consultants say real-time tools are still in the early stages of technology maturity and that implementations are far more prevalent in larger companies with sizeable IT budgets and deep benches of BI and analytics professionals.

"Simple kinds of real-time or near-real-time analytics are within the bud-get of virtually every company," said Roy Schulte, an analyst at Gartner Inc. in Stamford, Conn. As an example, he cited a business dashboard that gets refreshed every five minutes to monitor the volume of incoming customer calls received at a corporate contact center. But, Schulte added, more powerful

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real-time analysis systems "can require hundreds of thousands of dollars in software license fees and several times that in staff costs to develop and deploy."

Schulte said real-time BI technology gives business users or automated systems immediate access to operational data. The goal is to enable analytics applications to be applied to business processes that have limited time win-dows or require rapid reactions to events and changing conditions.

SALES OFFERS ON THE SPOT WITH REAL-TIME TOOLS

In that scenario, a real-time BI system might help a customer service repre-sentative make a cross-selling or up-selling offer to a customer based on his recent activity on the company's website or something said during the phone call. "Any large company has a number of applications in which their business processes would be smarter and more effective if they were using real-time analytics," Schulte contended.

In fact, with the cost of processing power coming down and more tools be-coming available, it's going to become easier to make a business case for real-time analytics beyond the traditional use cases in call centers and financial and trading applications, said John Myers, senior BI and data warehousing analyst at Enterprise Management Associates Inc., a research and consulting company


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in Boulder, Colo."With the barrier to entry lowering, it starts to open up a whole new realm

of things people can do," he said. For example, a retailer could push real-time data analytics to the cash register level, according to Myers. In such an appli-cation, the analytics system could serve up a simple alert that would direct the cashier to make specific offers to customers based on what they were buying as well as their previous purchases and risk scores assessing their payment and credit histories.

Being able to make BI-driven decisions as events unfold can be especially important when customer satisfaction is at stake. "Companies used to be able to look at [key performance indicators] every three months to see how they were doing," said John Crupi, chief technology officer at JackBe Corp., a vendor of real-time analytics software in Chevy Chase, Md. "Now, if there's a problem impacting customers and you don't find out about it for a few weeks, you won't have your customers for too long."

REAL-TIME NOT ALWAYS THE RIGHT BI FIT

Even with all its potential, real-time business intelligence and analytics is cer-tainly not a match for every company or every BI business case. Organizations


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need to think through whether providing end users access to the most current data will actually change business outcomes and results -- or just flood them with more data to no great effect.

"Not every instance requires real-time data," said Lyndsay Wise, president and founder of WiseAnalytics, a Toronto-based consultancy that focuses on BI and data visualization deployments in midmarket companies. "It depends not on how quickly you need the data, but what are you going to do with the data and where are the action points."

An online retailer might benefit from using real-time analytics to help it better service customers or manage product inventories more effectively, Wise said. But giving business executives updated views of sales data in real or near real time isn't so useful if that information doesn't precipitate any immediate actions on their part. To properly assess the need for real-time capabilities, she added, organizations need to look closely "at the cause and effect" of proposed deployments.

BETH STACKPOLE is a freelance writer who has been covering the intersection of technology and business for more than 25 years for a variety of publications and websites.


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