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Big Data Challenges

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Last Updated: 02 July 2021

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Data volumes are continuing to grow and so are possibilities of what can be done with so much raw data available. However, organizations need to be able to know just what they can do with that data and how much they can leverage to build insights for their consumers, products, and services. Of 85% of companies using Big Data, only 37% have been successful with data - driven insights. A 10% increase in accessibility of data can lead to an increase of $65Mn in net income of a company. While Big Data offers a ton of benefits, it comes with its own set of issues. This is a new set of complex technologies, while still in nascent stages of development and evolution. Some commonly faced issues include inadequate knowledge about technologies involve, data privacy, and inadequate analytical capabilities of organizations. Lots of enterprises also face the issue of lack of skills for dealing with Big Data technologies. Not many people are actually trained to work with Big Data, which then becomes an even bigger problem. This is not the only challenge or problem though. There are other challenges too, some that are identified after organizations begin to move into Big Data space, and some while they are paving a roadmap for the same. Here, we will discuss the top four critical challenges that enterprises are likely to face, if they are planning on implementing Big Data. Handling large amounts of data, there is a huge explosion in data available. Look back a few years, and compare it with today, and you will see that there has been an exponential increase in data that enterprises can access. They have data for everything, right from what consumers likes, to how they react, to particular scent, to the amazing restaurant that opened up in Italy last weekend. This data exceeds the amount of data that can be stored and compute, as well as retrieve. The challenge is not so much availability, but management of this data. With statistics claiming that data would increase 6. 6 times the distance between earth and the moon by 2020, This is definitely a challenge. Along with the rise in unstructured data, there has also been a rise in the number of data formats. Video, audio, social media, smart device data etc. Are just a few to name. Some of the newest ways developed to manage this data are hybrid of relational databases combined with NoSQL databases. An example of this is MongoDB, which is an inherent part of the MEAN stack. There are also distributed computing systems like Hadoop to help manage big data volumes. Netflix is a content streaming platform based on node.S Js. With increased load of content and complex formats available on platform, they need a stack that can handle storage and retrieval of data. They use MEAN stack, and with a relational database model, they can in fact manage data.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

1. Dealing with data growth

Big Data creates unique features that are not shared by traditional datasets. These features pose significant challenges to data analysis and motivate development of new statistical methods. Unlike traditional datasets where sample size is typically larger than dimension, Big Data is characterized by massive sample size and high dimensionality. First, we will discuss the impact of large sample size on understanding heterogeneity: on one hand, massive sample size allows us to unveil hidden patterns associated with small sub - populations and weak commonality across the whole population. On the other hand, modeling the intrinsic heterogeneity of Big Data requires more sophisticated statistical methods. Secondly, we discuss several unique phenomena associated with high dimensionality, including noise accumulation, spurious correlation, and incidental endogeneity. These unique features make traditional statistical procedures inappropriate. Unfortunately, most high - dimensional statistical techniques address only noise accumulation and spurious correlations issues, but not incidental endogeneity. They are based on exogeneity assumptions that often can not be validated by collecting data, due to incidental endogeneity. Big Data is often created via aggregating many data sources corresponding to different sub - populations. Each sub - population might exhibit some unique features not shared by others. In classical settings where sample size is small or moderate, data points from small sub - populations are generally categorized as outliers and it is hard to systematically model them due to insufficient observations. However, in the Big Data era, large sample sizes enable us to better understand heterogeneity, shedding light on studies such as exploring association between certain covariates and rare outcomes and understanding why certain treatments benefit one subpopulation and harm another subpopulation. To better illustrate this point, we introduce the following mixture model for population: where j 0 represents the proportion of the j - subpopulation, P j Y; j is the probability distribution of response of j - subpopulation give covariates X with j as the parameter vector. In practice, many subpopulations are rarely observe, ie, j is very small. When sample size N is moderate, N j can be small, making IT infeasible to infer covariate - dependent parameters j due to lack of information. However, because Big Data is characterized by large sample size N, sample size N j for j - subpopulation can be moderately large even if j is very small. This enables us to more accurately infer about sub - population parameters j. In short, main advantage brought by Big Data is to understand the heterogeneity of sub - populations such as the benefits of certain personalized treatments, which are infeasible when the sample size is small or moderate. Big Data also allows us to unveil weak commonality across the whole population, thanks to large sample sizes. For example, benefit of one drink of red wine per night on heart can be difficult to assess without large sample size. Similarly, health risks to exposure to certain environmental factors can only be more convincingly evaluated when sample sizes are sufficiently large. Besides the aforementioned advantages, heterogeneity of Big Data also poses significant challenges to statistical inference.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

4. Integrating disparate data sources

Big Data Integration is an important and essential step in any Big Data project. There are, however, several issues to take into consideration. Generally speaking, Big Data Integration combines data originating from a variety of different sources and software formats, and then provides users with a translated and unified view of accumulated data. Managing integrated Big Data assures more confidence in decision - making and provides superior insights. The process of integrating huge data sets can be quite complicated and can present several challenges. Some challenges faced during the integration process include: uncertainty of data, management, syncing across data sources, finding insights, and skill availability. The primary purpose of Big Data implementation is to present data in new and unique ways. To gain new insights and, in business, new advantages. Recognizing needs of organization prior to organizing data is useful in a broad range of Big Data projects, including business and scientific research. Big Data Integration combines Traditional Data, social media, Data from the Internet of Things, and transactional Data. Data that is not compatible, or has not been translated / Transform, is essentially useless for such projects. John Thielens, Chief Technology Officer of Cleo, Big Data Integration Solutions service, say: lot of what is discussed concerning Big Data has to do with wonders of today's powerful Analytics Tools. But before any analytics can be perform, Data Integration has to happen. That means your data - historical, operational, and real - time - must be source, move, Transform, and provision to users, with technologies that promise security and control all along way. As Traditional Tools for Data Integration continue to evolve, they should be reevaluate for their abilities to process ever - increasing variety of unstructured data, as well as the growing volume of Big Data. Integration technologies must have a common platform to support data quality and profiling. Integration of data from different applications takes data from one environment and sends IT to another data environment. In traditional data warehouses, ETL technologies are used to organize data. Those technologies have evolve, and continue to evolve, to work within big data environments. When working with Big Data, tools supporting batch Integration process, with Real - time Integration across several sources, can be quite useful. A Pharmaceutical company, for example, may want to merge data store in its MDM system and Big Data from sources describing outcomes of prescription drug usage. When using the Cloud, data can be organized using Integration Platform - As - aService. This service is generally easy to use and can include data from Cloud - base sources, such as Software - As - aService. Organizations use MDM systems to promote collection, aggregation, consolidation, and delivery of reliable data throughout the organization. Additionally, new tools, such as Scribe and Sqoop are being used to support Integration of Big Data. There is also increasing emphasis on ETL technologies in Big Data research. Mike Tuchen, CEO of Talend, open source ETL Solutions service, say: there is a once - in - ageneration shift taking place in the industry as the entire Data Management stack gets redefine.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

7. Organizational resistance

It might seem obvious that firms need both data and expertise to be able to execute clear digital strategy. While acquiring the right technology is essential, even more significant challenge lies within management. Statements such as everybody believe that if weve managed so far and so well without a robust strategic approach to Data Analytics and unfortunately, pain of declining profits and markets will have to take lead before management will seriously look to Analytics as a source of competitive advantage are clear examples of lack of motivation within organization and inertia. It is easy to sit back and think that everything is fine, but others might not be taking a break from innovating. Companies need to keep at it if they want to stay competitive, Kodak learned that lesson the hard way. Without management onboard and a clear goal, there will not be any change. Once management sets the stage to organize for Digital progress, some managers might feel that their domain is threaten. They might refuse to collaborate and keep up siloed hierarchy structure or even push against adoption through internal politics. The recommended way to address that issue is to break down walls and create cross - functional teams. A company can shift its employee mindsets by creating teams that are mixed from operations to partner relations to help them see things in a new light and come up with new innovative ideas. This, in turn, addresses another problem that companies who are not digitally mature face. That is an issue of keeping and retaining digital talent. These companies are likely to lose their new hires or current digital talent if they do not take steps towards digital development. By focusing on collaboration and encouraging risk - taking, companies will empower their current digital talent and become desired workplace for interested applicants. Management also needs to be aware that age can be a factor in how employees perceive leadership ability to lead change through digital transformation. Management must make the roadmap clear and commit to it to combat the perception of age. The drive to change needs to be supported from the top down, and then management needs to unite the organization into that vision. To get a head start on transformation, companies should look to incentives its personnel through various tools such as promotions and bonuses. Companies that are at infancy of Digital change tend not to utilize these tools, which might slow down the process. Mcafee,. & Brynjolfsson, E. Big Data. Management revolution. Harvard Business Review, 90 61 - 68. Kiron, D., Ferguson, R. B., & Kirk Prentice, P. From Value to Vision: Reimagining the Possible with Data Analytics. Mit Sloan Management Review, 54 1 - 19. Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. Embracing Digital Technology: New Strategic Imperative. Mit Sloan Management Review, 55 1 - 12. Kane, GC; Palmer, D.; Phillips,; Kiron, D.; Buckley, N.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Win or lose?

The Big Game is the annual American football championship and one of the most celebrated events in the history of sports. It is not only one of the most - watched sports events across the globe, but also has the reputation of being the second - largest day in terms of food consumption in the US. Every fan eagerly waits for Game Day to find out if their favorite team wins. Sports betting firms have used data augmentation techniques to synthesize data set of championship outcomes of Big Game's participants and other data. Your task is to generate a model to determine and classify whether give team will win the championship or not. The Dataset consists of certain parameters such as average age of players on the team, level of experience of head coach, number of players on the team that were first round draft picks, number of injured players on the team, number of wins the team has in the ongoing season, and more. The benefits of practicing this problem by using Machine Learning techniques are as follow: this challenge will encourage you to apply your Machine Learning skills to build models that can anticipate winning or losing chances of giving Team This challenge will help you enhance your knowledge of classification as IT is one of basic building blocks of Machine Learning we challenge you To build Model that classify whether give Team will win or lose on Game Day.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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