Challenges Faced By Big Data

These businesses realize that maintaining the public’s trust is one of the keys to commercial success, so they employ investigative accountants to strategically manage the complexities of risks and threats. Investigative accountants scrutinize fraudulent activities, assist senior management with risk management and strive to mitigate potential vulnerabilities. Investigate accountants often respond to fraud allegations and reported financial irregularities. They adopt a strategic threat management approach that enables them to anticipate and respond to risks. They apply advanced technology approaches to help internal customers track and manage data activities. They employ information management principles, data analytics techniques and sophisticated technology tools to help management make well-informed

The increasingly popular terms “big data” and “predictive analytics” have become so widely discussed that, in many ways, they lack a cogent meaning – even within the financial industry. The Capstone team used the literature review as an opportunity to scan the field for defining characteristics, resulting in a suggested guide for how the Federation should articulate these concepts. Generally speaking, big data is about the growing availability of data, as well as the tools needed to analyze the information. Predictive analytics refers to the analysis of big data which can help uncover patterns, correlations and trends that were otherwise unknown to the organization. Predictive analytics is also known as

Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet certain goals.

Big Data is an expansive phrase for data sets so called big, large or complex that they are very difficult to process using traditional data processing applications. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. In common usage, the term big data has largely come to refer simply to the use of predictive analytics. Big data is a set of techniques and technologies that need or require new forms of integration to expose large invisible values from large datasets that are diverse, complex, and of a massive scale. When big data is effectively and efficiently captured, processed, and analyzed, companies

I agree that I am an informed skeptic, because you need to effectively balance judgement and analysis, and possess strong analytic skills. You also need to listen to other’s opinions, but be willing to dissent. The problems that prevent organizations from realizing better returns on their investment in big data are analytic skills are concentrated in too few employees. IT needs to spend more time on the I and less on the T. There is reliable information, but it is hard to locate. Business executives do not manage information as well as they manage talent, capital, and brand. Also companies that want to make better use of the data they gather should focus on training workers to increase their data literacy. Another is to have more efficiently incorporating information into decision making and giving workers the right tools to use.

Technology companies are using big data to analyze millions of voice samples to deliver more reliable and accurate voice interfaces. Banks are using big data techniques to im- prove fraud detection. Health care providers are leveraging more detailed data to im- prove patient treatment. Big data is being used by manufacturers to improve warranty management and equipment monitoring, as well as to optimize the logistics of getting their products to market. Retailers are harnessing a wide range of customer interactions, both online and offline, in order to provide more tailored recommendations and optimal pricing.96

Despite the potential challenges, it is still believed that big data is the new way of the world, and that businesses should aim to overcome these challenges. The first typical challenge small businesses will face is finding “talented people who know big data and analytics” (Taylor, 2012, para. 6). Unfortunately, since small business owners tend to not be familiar with big data, then they will be challenged to know how to use it. Big businesses have the money, reputation, and influence to hire people who are very knowledgeable with big data. Also, if the storeowner is technologically challenged, it’ll be even harder for him/her to know how big data can help him or even what big data is in order to go out and look for someone to help his/her store with big data. Even if the storeowner is comfortable with technology and is familiar with big data, the storeowner could be averse to change, and that would be another reason why using big data would be a challenge for this category. The storeowner who is used to doing things a certain way will not be eager to start using big data to improve his business. S/he will probably think that if the business is doing well, then they don’t need to change. This will prevent them from increasing profits and will stagnant them from keeping with current times and current technology. For

Big data and its definition has changed over the years. In a 2011 research project by MGI and Mckinsey’s Business’ defined big data as

The emergence of big data has provided different avenues for organizations to use data to improve different aspects of their respective operations. Be it customer service, research and development, or market position, Big Data has the potential to be a significant driving force in all these areas. However, there’s still a significant gap between the ability of Big Data to produce insightful analytical information based on real-time data and the ability of organizations to capture and utilize this readily available tool. This is, in part, due to the fact that the systems and processes necessary to fully maximize the usefulness of Big Data is currently lacking in most organizations. This lack of a conducive habitat for Big Data is further magnified in new organizations without any knowledge of Big Data. For organizations that have that have little to no knowledge of Big Data, there must be a thorough assessment of the benefits of big data and how they could improve the organizations overall place in the market. There also needs to be steps taken towards the design of frameworks that will enable the organization to better capture and utilize Big Data.

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.

customers, channels and risks, big data also is going to change the ways banks operate by breaking down organizational boundaries and forcing people out of their proverbial comfort zones. As McKinsey & Co. partner Allen L. Weinberg notes in a discussion of the competitive implications of big data featured in Bank Systems & Technology’s February digital issue, “It has to be the data scientists sitting with the business folks. ... It’s getting these people talking together and sharing across the data.” One reason why it’s so important to knock over the cultural obstacles that can detour efforts to seize big data’s promise is that the competition probably is on a parallel path. The irony of “Moneyball” is that while Billy Beane confronted skeptics in his own organization, executives of other teams (most notably the deep-pocketed Red Sox) embraced his vision and tactics. The A’s appear to have had a very small window of opportunity to capitalize on the team’s data advantage — with only 29 competitors in the major

Today, data is a growing asset that various businesses are having difficulty converting into a powerful strategic tool. Companies need help turning this data into valuable insight, which can diminish risk and enhance returns on investments. Companies are struggling to make sense and obtain value from their big data. Superior and reliable

Traditional statisticians have been solving data problems for years. Example, when faced with the large data problem statisticians had a simple and proven technique to solve the problem by sampling the records. This is a sound methodology as the statisticians could prove statistically that a representative or random sample could speak for the whole population. The only problem was that when stakeholders wanted to drill down into the details they inevitably had to query the whole file. The reality is that data scientists run various data mining techniques and various algorithms that can accommodate both large and small files. However, companies may want to consider examining and cleansing the data they collect before running their analyses. This brings up the question of whether or not big data methods and algorithms can be considered reliable. Data quality is still a challenge even in the world of big data.

Auditors serve an essential role to the business world – they deter fraud by ensuring that public information about business organizations is a faithful representation of their financial status. Therefore, it is crucial for auditors to be knowledgeable in the system of internal controls employed by their clients. As technology continues to impact the nature of how business is conducted, auditors must also understand their client’s use accounting information systems. The Statement of Auditing Standards No. 109 (SAS 109) did not provide information regarding how auditing professionals must use their employers information systems to perform an audit. However, SAS 109 did provide information detailing the facets of an entity’s accounting information systems that must be understood and