The limitations of big data predictive analytical software


4.     What are the limitations of big data predictive analytical software?

Big data is seen by many to be the key that unlocks the door to growth and success. However, although big data analytics is a remarkable tool that can help with business decisions, it does have its limitations.

1. Security

As with many technological endeavors, big data analytics is prone to data breach. The information that you provide a third party could get leaked to customers or competitors.

2. Transferability

Because much of the data you need analyzed lies behind a firewall or on a private cloud, it takes technical know-how to efficiently get this data to an analytics team. Furthermore, it may be difficult to consistently transfer data to specialists for repeat analysis.

      3. Inconsistency in data collection

Sometimes the tools we use to gather big data sets are imprecise. For example, Google is famous for its tweaks and updates that change the search experience in countless ways; the results of a search on one day will likely be different from those on another day. If you were using Google search to generate data sets, and these data sets changed often, then the correlations you derive would change, too.

.

.

,

https://www.ciklum.com/blog/limitations-of-big-data-analytics/

Comments

Post a Comment

Popular posts from this blog

Technical requirements of big data

Image
2.       What are the technical requirements of big data? This data boom presents a massive opportunity to find new efficiencies, detect previously unseen patterns and increase levels of service to citizens, but Big Data analytics can’t exist in a vacuum. Because of the enormous quantities of data involved in these solutions, they must incorporate a robust infrastructure for storage, processing and networking, in addition to analytics software. 1.       Storage Solutions Often, organizations already possess enough storage in-house to support a Big Data initiative. (After all, the data that will be processed and analyzed via a Big Data solution is already living somewhere.) However, agencies may decide to invest in storage solutions that are optimized for Big Data. While not necessary for all Big Data deployments, flash storage is especially attractive due to its performance advantages and high availability.   2. Processing Power   Servers intended for Big Data analytics must have enoug
Read more

Limitations of traditional data analysis

Image
 7 .      What are the limitations of traditional data analysis? Traditional statistics is about analysing and summarizing data, and suited to processing lesser amounts of linear, repeatable data to find a solution. It’s very useful in environments where data, and the relationships between the data, are relatively stable. It is still by far the most commonly used methodology and continues to be good for mid to long term forecasting based on prior sales history, where a few hundred or even fewer data points may generate a reasonable forecast. The important limitations of statistics are:   Statistics laws are true on average. Statistics are aggregates of facts, so a single observation is not a statistic. Statistics deal with groups and aggregates only. Statistical methods are best applicable to quantitative data. Statistics cannot be applied to heterogeneous data. If sufficient care is not exercised in collecting, analysing and interpreting the data, statistical results might be misleadi
Read more

Traditional data analysis

Image
 6 .      What is Traditional data analysis (descriptive and inferential)?          Traditional data is the structured data which is being majorly maintained by all types of businesses starting from very small to big organizations. In traditional database system a centralized database architecture used to store and maintain the data in a fixed format or fields in a file. For managing and accessing the data Structured Query Language (SQL) is used. 3 Characteristic of Traditional Data: ·        Traditional analytics is static.   Traditional data analytics typically relies on dashboards composed of visualizations. These dashboards are based on common business questions and are predefined well in advance.   ·        Traditional analytics answers “what.”   Traditional analytics answers a series of “what” questions and leaves the user to determine “why” through their own analyses.   ·        Traditional analytics is driven by hypotheses.   Dashboards are typically predefined based on common
Read more