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How Big Data Is Changing the Face of Modern Business and It

Autor:   •  April 4, 2018  •  1,990 Words (8 Pages)  •  589 Views

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Conventional data analysis involves defined analysis of a fixed relational data set through application of pre-programmed schema. Now, should someone want to track a new function, the entire schema has to be changed, and that’s not an easy task.

This is where big data becomes interesting. You design analytical and statistical schemes for the analysis of large volumes of data without having to change the schema each time a new metric needs to be established or a new variable is introduced into the equation.

From the conventional ETL paradigm, we must shift to late binding – not defining schemas at the outset, but rather at the point where a query is raised, so that an answer meaningful to decision-making is retrieved. With big data, it’s impossible to predict all questions that can be asked from the data, which makes initial schema generation impossible. But implementing this paradigm shift with the modern business intelligence tools presents an additional challenge.

Big data will revolutionize how companies handle their internal IT departments even before moving on to sales and marketing. From identification of security threats to intelligent IT operations, the conventional service desk will be transformed to a powerful tool for service delivery.

- Identify, Describe & Define the intersection of Modern Business and IT; Specifically, as it is impacted by the concept of big data?

Ans. The convergence of data availability and processing power is helping to unlock the potential of big data for most sectors and industries. The results of big data can beneficial to a wide range of stakeholders across the organization — executive management and boards, business operations and risk professionals, including legal, internal audit, finance and compliance; as well as customer-facing departments like sales and marketing. The key challenge is having the ability to interpret the huge amount of data that can be collated from various sources. While there is no doubt that the big data revolution has created substantial benefits to businesses and consumers alike, there are measurable risks that go along with using big data.

Traditional risks:

a. There is continued regulatory pressure on companies to meet a variety of policies and laws (e.g., Basel II, MiFID, SOX). Compliance governance is an expensive and complex problem to deal with, but failing to meet regulations can mean safety risks, hefty penalties, loss of reputation or even bankruptcy.b. In a global and continuously and rapidly changing legal and IT landscape it is not always clear exactly what legal and regulatory compliance entails (Who is responsible? Who is liable?), or how best to translate abstract rules from laws into organizational and technical measures within a company.c. Companies need to balance contradictive rules and regulations e.g., obligations based upon the US Patriot Act and the EU Data Protection Directive (and its many local implementations).d. Increasing the volume of data puts a strain on infrastructure, resulting in slow processing, storage problems and back up requirements.e. The inability to work with unstructured data reduces the quality of analytics and reporting.f. Numerous data silos create the risk of poor data integrity, inconsistency and high implementation and maintenance budgets.g. There is a long implementation cycle for data warehousing and reporting solutions.h. Challenges over unifying data definitions are made even more complex across multiple business lines.i. Modeling, storage and processing challenges arise from the growing volumes of data with dynamic structures.

New risks:

a. Not considering information from outside the organization (e.g., weather) that is relevant to answering bigger question is an ongoing concern.b. There is a shortage of qualified “data scientists” globally for the near to mid-term.c. Organizations can get overloaded and overwhelmed by trying to handle too much data.d. The challenge of getting the right information to the right person at the right time is expanded due to the sheer size of big data.e. The costs associated with managing and monitoring the quality, credibility and integrity of big data can be prohibitive.f. There is a necessity to temper the expectation that big data will solve everything.g. More is not always better. More data can lead to an increased number of data quality issues, and confusion and lack of consistency in business decision making — especially when conflicting information is present.h. Integrated data architecture increases the challenges of data linkages and matching algorithms to distinguish items of relevance from piles of data.i. Increased complexity of architectural landscape and the growing amount of data bring new challenges around data governance and data privacy.j. Lack of capabilities, both within organizations and externally, make it hard to keep up with rapidly evolving hardware/software technology and implementation methods.k. Simplified access to diverse sources of data and easy-to-ingest large amounts of information may result in increasing amount of “noise” in data and decrease in the overall level of data quality.l. Many new technology market players don’t have mature enterprise-ready capabilities around implementation, support, training, etc.m. New big data methods, architecture and volume variety impose additional risks of lack of control and governance over data, and this requires additional organizational focus. Under the context of the complex data landscape, it is especially important to establish and maintain data lineage.n. Organizations may struggle with finding the right skills and building internal capabilities for handling big data as most of the technologies and methods are relatively new, and market resources are in short supply.

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