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Big Data Analytics Are Today’s IT Organizations Equipped to Reap the Benefits?

Originally published May 9, 2013

Market-leading enterprises have been investing heavily in analytical applications to derive competitive differentiation, market growth, revenue maximization, 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 processes 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 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 required 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 different 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 better 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 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 transaction 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 interaction with the enterprise. This enhanced understanding about the customer typically improves behavioral modeling capabilities, resulting in better prediction 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.

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 analytics 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 making or incremental improvements. Big data can transform the business and disrupt 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 introduction of new processes, new products and services that are aimed at the proverbial “market of one” customer segments that were previously out of reach.

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 either 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 deployment of such solutions at a much faster pace than previous technological advances 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 advancing hardware architectures able to consume them at a faster pace; hence, more focus needs to be placed on addressing the variety, velocity and complexity 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 journey is the evaluation and selection of all architectural and infrastructural components to ensure ability to adequately support anticipated growth in volume, 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 PhilipGeorge Philip
    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.

    Editor's Note:
    More articles and resources can be found in George's BeyeNETWORK expert channel: Analytics @ the Speed of Business.


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