Analytics and the Growing Dominance of Big Essay

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Analytics and the Growing Dominance of Big Data are

Revolutionizing Strategic Decision-Making

The level of uncertainty and risk that pervade many enterprises today is growing, as the dynamics and economics of markets are changing rapidly. The many rapid, turbulent structural changes in industries is also leading to a greater reliance on analytics and the nascent area of Big Data as well. The potential of this second area, Big Data, is in determining patterns in massive data sets that have in many cases been collected for decades within enterprises. The abundance of data within enterprises, when combined with Big Data aggregation and analytics techniques, can be used for drastically reducing risk and uncertainty in even the most challenging and fast-moving industries. Big Data is being hyped heavily by analytics systems and enterprise application providers as well, as this category of software allows for the use of long-standing analytics and business intelligence (BI) tools expanded supporting larger data sets. Many companies today are working to create enterprise-wide platforms for managing massive data sets, many of them integrating legacy and 3rd aprty databases many of which have never been integrated into a broader platform strategy before (Jacobs, 2009). These larger data sets and their inherent complexity make the overall analysis, aggregation, creation of taxonomies and customizing of reports challenging and difficult to achieve with the baseline or current set of analytics and BI tools available today however. The continual evolution of these applications and the fine-tuning of specific aggregation technologies including Hadoop and Map Reduce (Jacobs, 2009) have also contributed to making Big Data a more strategic foundation fro decision making. Enterprises are facing greater time and cost constraints than ever before, which also leads to the create and continually invest in larger data sets, analytics, BI and advanced reporting technologies all orchestrated to make the most of the terabytes of legacy data companies have (Chisholm, 2009).

The rapid development of analytics, BI and data reporting platforms and tools has led to a level of innovation in enterprise software that is making it possible for enterprises to get more insights from the terabytes of data they have been collecting for decades. This category of software tools include analytics, BI, data visualization, product lifecycle data and predictive analytics all orchestrated to create a common platform for reducing risk while bringing greater intelligence into an organization (Ericson, 2010). As is the case with any high growth enterprise software category, there is an abundance of hype surrounding what these analytics and BI platforms and tools are and aren't capable of. The tendency to overlook the very difficult processes to extracting, transferring and loading (ETL) data from legacy systems and creating a highly effective ecosystem of data is very expensive for companies who have never attempted this before. Further, the methodologies needed for consistently and accurately capturing the data within a given enterprise require a level of discipline that many companies are lacking in their core process areas (Jacobs, 2009). Simply put, it is very hard work to capture all the heterogeneous sources of data throughout an enterprise, from the legacy systems to the 3rd party databases, and then perform ETL functions on them in order to create a new system of record for the entire organization to make use of (Ericson, 2010). Yet for organizations to capitalize on the potential that exists from these many diverse forms of information, intelligence and insight throughout their businesses, they must take the time and effort to create a unified, highly integrated single system of record to galvanize their Big Data strategies together (Jacobs, 2009).

The objective of this analysis is to provide the arguments for and against having Big Data included in the strategic decision-making process within an enterprise. The strengths are presented first, followed by the weaknesses of this approach to harnessing data throughout an enterprise. The strengths and weaknesses are next compared and an assessment provided. One of the most prevalent technologies used for accomplishing Big Data analytics and intelligence are MapReduce and Hadoop, two aggregation technologies that can compress terabytes of data into taxonomies and quickly analyze them (Jacobs, 2009).

Assessment of the Advantages of Big Data, Analytics and Business Intelligence In Enterprises

There are many advantages to using Big Data taxonomies and data reduction technologies to aggregate, analyze and report data. Foremost among all of these advantages is the ability a Big Data system of record provides in terms of insights into customer behavior, purchasing habits, preferences and loyalties to a given product or service.

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Big Data has also successfully been used to understand the price elasticity of products and forecast them successfully as well (Meijer, Bierman, 2011). Business strategists, from CEOs and CFOs to directors of marketing and sales, are using Big Data to better plan and predict the overall demand for new products and services. They are also using the insights and intelligence from Big Data to define upsell and cross-sell strategies for existing products as well (Ericson, 2010). The accumulation of this data provides companies with greater ability to minimize support and service calls while increasing customer satisfaction and profits at the same time (Jacobs, 2009).

Big Data has also emerged as an exceptionally powerful catalyst for strategic planning and future product development strategies, taking into account insights gained from working with specific suppliers, developers and on the sell-side of their businesses, with distributors and dealers (Ratner, 2002). The entire value chains of businesses are being streamlined by these developments, making them more customer-centric, efficient and profitable over the long-term (Meijer, Bierman, 2011). Implicit in the performance gains of these strategic frameworks is the ability to move more rapidly from product concept to launched product, from global channel management or supply chain initiative to action. Big Data is the catalyst that gives enterprises the insights they need to move beyond being tactical and short-term focused to being more strategic in scope and ability to execute (Meijer, Bierman, 2011).

The early adopters of advanced analytics and Big Data strategies include financial services firms and consumer packaged goods (CPG) product companies. These two industries, financial services and CPG, generate literally terabytes of data daily and use advanced analytics tools including Hadoop, Map Reduce and others to gain greater insights into the myriad of details they collect on any given day of operations (Ratner, 2002). Financial institutions and banks specifically benefit from this data as it provides them with greater insights into the transactions and lifetime customer value of their best customers, in addition to guiding their new service development strategies (Meijer, Bierman, 2011). The use of Big Data is so pervasive in banking that Bank of America has a Vice President just for this area, and he is an acknowledged expert in Hadoop and MapReduce technologies, guiding the company to strategies for reducing transaction costs and increasing lifetime customer value (Ratner, 2002).

Analysis of Weaknesses in Implementing and using Big Data For Strategic Management

The most significant weakness of using Big Data in the context of strategic decision-making are the costs associated with initial application software, costs of implementing and customizing the many modules, and then training the entire staff of an organization on how to use these technologies. The change management strategies necessary for any business to succeed with these technologies is very significant as well; it often costs up to ten times more to actually get the analysts and teams of employees to use this software than it costs to purchase it (Ericson, 2010). Resistance to change is the single leading cause of failure for Big Data projects, so companies invest the majority of their budgets on overcoming this limitation. As there is much hype surrounding Big Data today, it is common to find vendors who routinely overcommit to projects and often taken much longer to implement systems as a result (Bughin, Chui, Manyika, 2010). When enterprise software vendors do this, there are on average a 40% cost overrun or greater, and eventually a lack of adoption due to the system not completely reflecting what employees need to do their jobs (Rogers, 2011).

Second, the costs associated with customizing adapters and connectors are very significant, and can lead to even more costs if they must be configured continually to stay current with corresponding changes in IT infrastructures (Chisholm, 2009). Making it more difficult for companies to predict and plan a suitable Return on Investment (ROI) for their activity, many companies will perpetually pilot Big Data initiatives and not get the value they could rom them otherwise (Rogers, 2011). Related to this is the lack of training within many companies, specifically in the areas of how to use the advanced features in Hadoop and MapReduce in the taxonomy development phases of projects (Jacobs, 2009). With a lack of training there is an imprecise integration of these technologies into value chains and business models, and strategies that would have been successful suffer as a result (Rogers, 2011).

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