The Most Effective Methods To Improve As A Pioneer In 2015: Managing Big Data

Information Management and the Challenge of Big Data: The term big data describes the exceptional growth in the volume and variety of information available from online sources. Its emergence offers enterprises the opportunity to derive unparalleled business insight for enhanced decision-making for corporate strategy and operations. As the quantity of big data proliferates, astute information management (IM) becomes increasingly necessary to avoid becoming overwhelmed by the challenges of using it correctly.

The challenge of big data is selecting what information to use and how to apply to optimally meet your enterprise requirements. The very quantity of data can cause problems for business leaders trying to adapt their IM processes for efficient application. A lack of big data know-how can be intimidating from the perspective of both theory and practice.

In theory, big data encompasses data sets of extreme complexity so large, their meaning can be nearly incomprehensible. Practically, traditional data processing methods continue to be challenged by such basic procedures as data capture, selection, analysis/curation and storage, essential to using the information. Further difficulties involve sharing and transfer of big data; development of better-refined search processes is also necessary to diminish the incidence of privacy violations.

Yet, big data is a competitive necessity because it provides management with a vast range of information that can be applied to enterprise priorities. Big data analytics simplify complex data, leading to better quality business information (BI).

  • Big data’s superior BI generates far larger data sets faster, supporting enriched and deeper assessment of operations,
  • Workflow efficiency improves as job-assignments are completed with greater relevancy to strategic objectives.

The result is enhanced organizational functioning by a factor of 5% – 7%. While these benefits are recognized, they are not always achieved. The essential priority for upper echelon management is determining how big data is applied to organizational decision-making, a sometimes daunting task in consideration of profusion of data available for analysis.

big data

Appraising Big Data for Enterprise Relevance

Big data’s sheer volume and complexity can overwhelm under-prepared executives. The potential over-abundance of information can be confusing, distracting management from enterprise’s essential objectives. To achieve the quality data analyses that engender better business decisions, the objective is to:

  • Identify pertinent data,
  • Select assessment methods most appropriate to the confluence of BI and enterprise strategies,
  • Factor out inconsequential data, and
  • Apply the relevant information toward greater operational efficiency in delivering products and services to customers.

Appropriate application of big data technology and teams can vitalize corporate performance, making it more agile and flexible; productivity and profits grow while operating costs decline.

Much depends however, upon the accuracy of your forecasts and for this, you need to select the appropriate material from the entire range of big data made available to you.   Having the tools to capture, quantify, evaluate, and describe data is, by itself, insufficient to convert the information into explicit enterprise strategies. Big data appraisal begins with identifying data pertinent to your express corporate needs

While understanding the capacity of big data to spark better enterprise decision-making is important, it is more essential to develop and maintain a staff of expert personnel capable of selecting data appropriate to your enterprise needs, directing them toward strategic directives. IM also requires capture and analysis technologies devised specifically for the intricacies and extreme density of big data.

IM’s Big Data Dilemma

Raw data needs to be separated and appraised to synthesize data-supported insights into explicit strategies for essential corporate events like brand building, marketing campaigns, production schedules or creating consumer content. Big data’s real value lies in this predictive capacity, wherein information is used to forecast coming events based on analysis of past and real-time data. Tracking and reporting such key performance indicators (KPIs) as relations with suppliers/distributors, web-page traffic, product shipment, or sales-per-region provide the basis for superior decision- making and corporate development.

Executive decision-making has corporate impact with repercussions felt throughout the organization. Rather than hundreds, or even thousands, of items of information requiring evaluation, big data often offers many millions. Innumerable factors can influence such basic business considerations as the price of raw materials or their delivery, the cost of shipping finished products, lender repayment requirements on corporate loans, or customer preferences across global markets. The big data dilemma is choosing which data are most pertinent to resolving precise corporate problems.

Doing so requires exceptional information appraisal from IM and data center personnel, expertly applying technologies devised for big data analytics. Valuable information can be omitted or overlooked within the volume and variety that characterizes big data. Moreover, divergent opinions about data sources and their application are to be expected among IM executives and staff.   Under these conditions, an appropriate mix of human and technological resources can diminish the confusion inherent in the magnitude and variety of information generated by big data.

Analytics for Accuracy

Big data is useless out of context with the business problem requiring resolution.  In addition to monitoring real-time developments, analytics can forecast longer-term industry and consumer trends, separating plentiful and diverse data to focus important management decisions.

By consolidating multi-sourced data over time, big data analytics evaluate business developments across most enterprise financial, marketing, production and transaction processes. These data are further refined through use of big data technologies utilizing analytical methods — correlational analysis, predictive forecasts, profiling, etc. — most relevant to particular enterprise issues.

Appropriate application of analytical reporting tools quantifies the success of such business activities as marketing budgets, product placement/positioning, and personnel selection by interpreting the critical numbers derived through quantitative reporting measures. The resulting performance metrics clarify and detail key data for upper management.

Developing a productive mix of personnel/technology eliminates much of the confusion of using big data. While many useful technologies are available, remember the people-side of this equation; on their own, technologically-generated analytics are not absolute. .For all their ability to explicate big data’s complexity, algorithmic analysis is no substitute for human experience. Technology can make mathematical sense of big data, but insightful leadership is required for its informed application for enterprise decision-making.

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Brad Smith is CEO and co-founder of Rescue One Financial, headquartered in Irvine, California. Rescue One Financial helps individuals resolve unsecured debt during troubling times and have settled over $3.1B in debt. Brad’s 18-year financial services career includes Wall Street with Merrill Lynch, where he helped pioneer the restricted stock diversification business at Morgan Stanley. Smith still holds all of his licenses today (Series 7, 31, 63, and 65).
Rescue One Financial on Social media: Google+, Twitter, Facebook, and Linkedin.

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