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Table of contents
1.
Introduction
2.
Working of BI
2.1.
Example
3.
Modification of BI for Big Data
3.1.
Analysing Data
3.2.
Analytical Algorithms
3.3.
Infrastructure for Big Data Analysis
4.
Frequently Asked Questions
4.1.
What kind of analytics do big data employ?
4.2.
What are the four pillars of big data?
4.3.
What role does big data analytics play?
4.4.
What is the smallest amount of big data that can be stored?
5.
Conclusion
Last Updated: Mar 27, 2024

Managing BI Products To Handle Big Data

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Speaker
Ashwin Goyal
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Introduction

In today's business environment, big data is a popular topic. The ability to acquire and analyse Big Data information is becoming significantly vital. Ransomware attacks and accidental deletion pose a hazard to large amounts of data. As a result, businesses must create a plan that ensures their data is secure and well-organized.

By showing current and historical data within the context of their companies, Business Intelligence may assist organisations in making better decisions.

This article will discuss managing BI products to handle Big Data.

Working of BI

Companies must adequately evaluate data to comprehend customer behaviour, improve operations, and optimise the supply chain, among other things. Companies can install  Business Intelligence (BI) systems that consider the variety, volume, and velocity of data to ensure that they can make the best use.

 Business intelligence combines business analytics, data miningdata visualisationdata tools, and infrastructure to provide organisations with on-demand access to accurate, intelligible, and actionable information, making better business decisions.

Example

Business intelligence is a broad word that covers the processes and methods for gathering, storing, and evaluating data from business operations or activities to improve performance. These processes include the following:
 

                                                                                             

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  • Data Mining: It discovers patterns in massive datasets by combining databases, statistics, and machine learning.
  • Reporting: Stakeholders of the company are given access to data analysis to draw conclusions and make decisions.
  • Performance metrics and benchmarking: Using customised dashboards to compare current performance data to previous data to track performance against goals.
  • Descriptive analyticsIt determines what happened to the data based on fundamental data analysis.
  • Querying: Inquiring about specific data and having BI get the responses from the datasets.
  • Statistical analysis: Taking the results of descriptive analytics and utilising statistics to investigate the data further.
  • Data visualisation: It is transforming data analysis into visual representations like charts, graphs, and histograms so that they may be consumed more readily.
  • Data Preparation: Compiling multiple data sources, defining dimensions and measures, and preparing them for data analysis are all examples of data preparation.
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Modification of BI for Big Data

BI tools can have a significant impact on any company. They can assist us in improving inventory control, better managing our supply chain, identifying and eliminating operational bottlenecks, and automating regular processes.

Traditional business intelligence software was not intended to manage big data. They were created to work with highly structuredwell-understood data, typically housed in a relational database and presented on our desktop or computer. This type of traditional business intelligence analysis is usually done on data snapshots rather than the entire amount of data.

When we begin to examine big data, we must use a new strategy.

Analysing Data

Structured, semi-structured, and unstructured data fall under the Big Data umbrella. It's common to have a lot of it, and it can be tricky to understand.

                                                             

You should be aware of the following probable aspects of your data when considering how to analyse it:

  • Define our objectives: Before We begin evaluating our data, we must establish some clear goals. Our objectives will differ based on the team we're on, the data we're gathering, and our position within the company.
     
  • Clean Data: The insights we gain from our analysis will be incomplete or misleading if our information is erroneous or inconsistent. So, once we've gathered our data, please spend some time cleaning it up to ensure that it's consistent and free of duplicate information.
     
  • Low Signal to Noise ratio: The signal-to-noise ratio is likely to be low when the signal (useful information) may make up only a tiny percentage of the data, while the noise makes up the remainder. One of the benefits of big data analytics is extracting a modest signal from noisy data, but we must be mindful that the signal may be minimal.
     
  • Patterns and trends in Data: We'll be analysing real-time data streams in many circumstances. Look for trends in the data set as a starting point. If most of the data is numerical, plotting patterns on charts and other visualisations is quite simple.

Analytical Algorithms

Big data architecture allows us to run a model in minutes rather than hours or days. This allows us to improve the model hundreds of times.

                                                                 

                                                                                                Source
Novel algorithms, programming models, and systems have been created in recent years to extract meaningful information from the analysis of such data, which address their complexity and high velocity. Data mining and Machine Learning have evolved over the last few decades as two research and technology sectors that have developed a variety of approaches and algorithms for extracting hidden, unknown, but potentially valuable information from big data sets.


On the other hand, sequential data analysis methods are incapable of deriving usable models and patterns from large amounts of data in an acceptable amount of time. MapReduceworkflow, Bulk Synchronous Parallelmessage passing, and SQL are the most prominent parallel programming methods for Big Data analysis.

Infrastructure for Big Data Analysis

We'll likely need to invest in the following critical infrastructure aspects to get started with big data and transform it into insights and business intelligence: 

                                                              

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  • Data collection: Everything from our sales records to our customer database to feedback, social media channels, marketing lists, email archives, and any data obtained from monitoring or assessing parts of our business is included. 
     
  • Data storage: As the amount of data collected and saved by businesses has grown exponentially, complex but user-friendly systems and tools have been developed to assist with this task. The main storage possibilities are a classic data warehouse, a data lake, a distributed/cloud-based storage system, and a company's server or a computer hard disk.
     
  • Data analysis: This procedure consists of three essential steps: 1. identifying, cleaning, and structuring the data to be suitable for analysis; 2. developing the analytic model, and 3. making a conclusion based on the findings.
     
  • Data visualization: This is how the information acquired through data analysis is communicated to the people who need it, i.e., your company's decision-makers. Communication must be simple and to the point, and this output can take the shape of brief reports, charts, data, and critical suggestions.

Frequently Asked Questions

What kind of analytics do big data employ?

Diagnostic, descriptive, prescriptive, and predictive analytics are the four primary forms of big data analytics.

 

What are the four pillars of big data?

The four aspects of Big Data are represented by these Vs: Volume, Velocity, Variety, and Veracity.

 

What role does big data analytics play?

Big data analytics assists businesses in harnessing their data and identifying new opportunities. As a result, more intelligent business decisions, more effective operations, higher profits, and happier consumers are the result.

 

What is the smallest amount of big data that can be stored?

Big Data is a term that describes massive data sets that are computationally analysed to discover patterns and trends related to a particular aspect of the data. There is no minimum amount of data required to be classified as Big Data, as long as it is sufficient to form strong conclusions.

Conclusion

This article extensively discussed Big Data Analytics, Business Intelligence, its working as a Big Data Analytics Solution, and the Modification of BI to handle Big Data.

We hope this blog has helped you enhance your BI and Big Data knowledge. You can learn more about Big DataBig Data vs. Data Science, and Big Data Engineers. 

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