Big Data Analytics the Challenges and Opportunities

With increasing statistical data, it is important that real-time information that is for business use can be deleted from its IT systems; otherwise business risks have been incorporated into the data flood. At the same time, competitors who use data to provide better insight for decision makers are a good opportunity to promote a complex economy and more. The goal is to be able to use make real-time decisions using real-time data for real-time business.

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Social and economic changes lead to an increase in business analysis and data, and organizations have to cope with this pressure to succeed. Mobile devices, social networks, and real-time information are running big data - is ready to handle this by developing capacity in data architecture and analytical tools (Al Aghbari, 2015). Business leaders know that the ability to get and understand competitive data is dusty gold, and they will come knocking on your door.

Understanding and taking over the business needs is the next challenge. The CIO should look passed the apps and bits and programs and work with company leaders to know how they carry out decision making and provide them with data feeds and analysis tools that are to be used (Ardagna, Ceravolo, & Damiani, 2016). As the number of data increases, the capability to gather and submit data in a way that the management can understand so that the business can make faster than competitors, will be the essence of holding a competitive business.

Instead of collecting data and storing it, decisions need to be made regarding data that is for business use. The true value of data comes from being able to communicate and understand, to provide insight into which you can trade for competitive advantage. As the economy goes, the world is becoming more finance focused and in five years, it will be important to have a business case justifiable with big data, whether to gain competitive advantage or increase revenue, the visible value can be shown on what the organization already did with the available data ("Clustering Algorithms for Big Data: A Survey," 2016).

Challenges

Big Data has a characteristic of large sample size and high dimensionality. These two components bring three peculiar challenges: (i) high dimensionality leads to noise disorders, false relationships and common homogeneity; (ii) high dimensionality that has a large sample size leads to problems such as stability algorithmic and heavy computational cost; (iii) The largest sample in the Big Data is normally linked to different sources during the time when using different technologies ("Challenges with Big Data Analytics," 2015). This leads to heterogeneity issues, a variety of experiments and statistical preferences, and calls for the developing of more efficient and effective processes.

To address Big Data challenges, there is a need for new ideas for data and computer techniques. For example, many of the most commonly practiced traditional, sophisticated methods do not exceed big data (Chokkalingam & S., n.d.). Several statistical techniques that do well for data of low dimension are faced with major challenges in analyzing data with high dimension. To develop relevant statistical monitoring data and predict the Big Data, there is need deal with major data issues like noise accumulation, heterogeneity, friction and normal endogeneity, as well as creating a balance between the accuracy of data and computer efficiency.

According to statistical accuracy, reducing the direction and selection of variants plays important role in analyzing high data. The design helps to address noise collection problems (Jamshidi, Tannahill, Ezell, Yetis, & Kaplan, 2016). Normal classification rules using all features cannot outdo random guessing due to noise collection. The higher trends combine false relations between non-related responses and covariates, which can lead to the bad accuracy of statistics and the scientific conclusion of the lie. The maximum scope also gives maximum endogeneity, which most covariates that are not involved can be linked to the remaining sounds. Endogeneity creates a statistical shortage and leads to an unprecedented selection of an image that leads to improper scientific inventions. However, many statistical processes based on unusual ideas that cannot be verified. New statistical processes and problems in mind are important.

When it comes to computer efficacy, Big Data heartens the creation of new computing infrastructure and storage of data techniques. Improvement is not a goal but usually a tool for a big data analysis. This model change has contributed significantly to the development of fast algorithms that can be achieved by large data and high levels (P. & Ahmed, 2016). This creates cross-fertilizations among diverse fields including applied mathematics, optimization and statistics. In addition to large-scale optimization algorithms, Big Data as well inspire the development of algorithms of majorization-minimization.

Opportunities

The need for Analytics aptitude is increasing gradually, but there is a big shortfall on the side of supply. This continues throughout the world and is not limited to any geographical location. Big Data Analytics is 'Hot', but there are still many unoccupied jobs around the world because of lack of the needed skills. New technologies now make it easy to make the most comprehensive data analysis on very big and different datasets (P. & Ahmed, 2016). For Big Analytics Data to give rise to a competitive edge, the level of implementation of key analytics tools has increased significantly.

From work perspective, there are many options available for me, for field issues as well as workplace origin. Since Analytics is used in different locations, there are many different names for the person I choose:

• Business Consultant
• Big Data Engineer
• Metrics and Analytics Specialist
• Big Data Analytics Architect
• Solution Architect
• Analytics Associate
• Big Data Analyst
• Business Intelligence and Analytics Consultant
• Data Analytic's extensive task is extensive, and one can choose from 3 data analysis data based on Data privacy.
• Predictive Analytics
• Prescriptive Analytics
• Descriptive Analytics

Many organizations such as IBM, Ayata, Alteryx, TIBCO, Teradata, Microsoft, Trend, Platfora, Carpass, Opera, Oracle, Datamer, Centrofuge, Pentaho, FICO, Quad, Lips, Saffron, GoodData, Jaspersoft, Tracx, Bluefin Labs, The Panorama software, and several others make use of Big Data Analytics for their company needs and they provide great potential in terms of opportunities (Nirmala, n.d.).

No matter how advanced Analytics is, it does not eliminate the requirement for human insight. On the other hand, there is a great need for skilled people to study the data, think from the business perspective and develop knowledge. As a result, technological experts that have analytical skills find themselves in great demand as businesses look at integrating Big Data power. An experienced Analyst expert can look at the Big Data ocean and become an important tool for the organization, to increase their business and career.

References

Al Aghbari, Z. (2015). Mining Big Data - Challenges and Opportunities. Proceedings of the 17th International Conference on Enterprise Information Systems. doi:10.5220/0005463803790384

Ardagna, C. A., Ceravolo, P., & Damiani, E. (2016). Big data analytics as-a-service: Issues and challenges. 2016 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2016.7841029

Challenges with Big Data Analytics. (2015). International Journal of Science and Research (IJSR), 4(12), 778-780. doi:10.21275/v4i12.nov152088

Chokkalingam, S., & S., V. (n.d.). Research Challenges in Big Data Analytics. Decision Management, 83-99. doi:10.4018/978-1-5225-1837-2.ch006

Clustering Algorithms for Big Data: A Survey. (2016). The Human Element of Big Data, 143-162. doi:10.1201/9781315368061-9

Jamshidi, M., Tannahill, B., Ezell, M., Yetis, Y., & Kaplan, H. (2016). Applications of Big Data Analytics Tools for Data Management. Studies in Big Data, 177-199. doi:10.1007/978-3-319-30265-2_8

Loshin, D. (2013). Big Data Tools and Techniques. Big Data Analytics, 61-72. doi:10.1016/b978-0-12-417319-4.00007-7

Loshin, D. (2013). Business Problems Suited to Big Data Analytics. Big Data Analytics, 11-19. doi:10.1016/b978-0-12-417319-4.00002-8

Nirmala, M. B. (2014). WAN Optimization Tools, Techniques and Research Issues for Cloud-Based Big Data Analytics. 2014 World Congress on Computing and Communication Technologies. doi:10.1109/wccct.2014.72

Nirmala, M. B. (n.d.). A Survey of Big Data Analytics Systems. Big Data, 1024-1052. doi:10.4018/978-1-4666-9840-6.ch046

P., D., & Ahmed, K. (2016). A Survey on Big Data Analytics: Challenges, Open Research Issues and Tools. International Journal of Advanced Computer Science and Applications, 7(2). doi:10.14569/ijacsa.2016.070267