Big Data Features and Challenges

            The process of collecting and analyzing large amounts of data across multiple data stores is an excellent technique for dealing with large amounts of data.

What Is Big Data?

Big data is highly variable, high-volume, and fast-moving information.

  • Volume - Your organization is faced with difficulties related to processing, monitoring, and storage because of the size of your data.
  • Velocity - Most businesses that use technologies like social media, the Internet of Things, and eCommerce fit this description.
  • Variety - If your data is stored in various formats, it has big data characteristics. Big data repositories typically contain word-processing documents, emails, presentations, photos, and videos.

Today's businesses have an unrivaled ability to collect and analyze vast amounts of data. From predicting market trends to improving security and reaching new audiences, companies are achieving previously impossible things.

Features of Big Data

Big data is a collection of information from multiple sources that are generally defined by five factors: volume, value, velocity, and veracity.

1. Volume

Big data is distinguished by its primary quality certainly its quantity. 

  • This is one of the best aspects of big data. 
  • Extensive data collection has many opportunities for analysis.
  • While data sets are measured in kilobytes, megabytes, and gigabytes, the accepted starting point for large data sets is the terabyte. 
  • A single computer cannot process a terabyte-sized dataset. 
  • This is a widely accepted definition of big data as it requires a distributed network such as Hadoop or MPP.

2. Variety

Another attractive feature of Big Data is its versatility. 

  • Big data is given both structured and unstructured data analytics.  
  • The nature of data is highly variable with multiple format types and multiple variables. For various reasons, new data relationships can be created. 
  • Data mining techniques can be used to find matches and patterns in data sets that we would not have found without Big Data.
  • Big Data has many applications in new business developments, but its most important applications are in medical research.

3. Velocity

The quality of speed in Big Data is related to two concepts. 

  • By-products of digital processes, the rapid collection of data sets through applied measurement techniques, and the ability to process large amounts of data in real-time or very soon after it is collected.
  • This means we are rapidly accumulating more and more useful data sets. 
  • The rate at which they are collected will help solve future problems, drive research and technology, raise the quality of life, and improve safety. 
  • Rapid data collection and processing get results to those who need them when they need them, expanding the reach of data.

4. Storytelling

  • One of the most valuable characteristics of Big Data is its ability to tell us a story based on the patterns we discover. 
  • Thinking about data as a story allows us to get to know the main characters, their goals, motivations, and outcomes.
  • An insurance company may be analyzing policyholder and claim data. 
  • Characters who may appear include health care providers, assessors, policyholders, and third-party claimants.

5. Modeling Capability

  • The availability of enormous data sets facilitates the development of models and algorithms. With the help of the model, we can determine how each element will affect the outcome and improve the forecasts.
  • Large data sets enable sophisticated modeling techniques like neural networks, which are at the heart of many machine learning applications.

Big data sets also enable flexible modeling, whereas, with non-relational databases, modeling can be done at the query or data output stage, rather than being forced at the data input stage, as was the case with traditional relational databases.

1. Managing Big volumes of Data

By definition, big data refers to vast amounts of data stored across multiple platforms and systems. 

  • According to Cubillo, the primary problem for businesses is combining the enormous amounts of data they pull from CRM and ERP systems and other data sources into a single, comprehensible big data structure.
  • It's easy to focus on insights by making small adjustments once you understand the data being collected, he said. To do that, plan a structure that supports incremental changes. 
  • Making significant changes can lead to the development of new problems.

2. Lack of Data Scientists

The mindsets of corporate leaders and data scientists rarely align, and rarely do.

  • Beginning analysts often distance themselves from the true value of enterprise data, resulting in insights that fall short of problem-solving.
  • There is also a dearth of data scientists who can add value.
  • Although studies show that all big data professionals are highly paid, employers struggle to retain top talent.

Additionally, training for entry-level technicians is very expensive.

3. Managing data integration

Big data platforms offer a solution to the challenges of collecting and storing.

  • Various types of data and quickly retrieving the required data for analytical purposes.
  • Data repositories collected in an organization must be updated frequently to maintain their integrity. 
  • This requires consistent access to multiple data sources and specialized big data integration techniques.

Some businesses use a data lake for massive data sets collected from various sources without considering how the heterogeneous data will be combined.

  • Adopting a strategic strategy for data integration is often desirable for better ROI in big data projects.

4. Data Security and Integrity

Another issue with big data is the security and integrity of data. 

  • With so many overlapping channels and nodes, hackers are more likely to exploit any system vulnerabilities.
  • Data is so important that even small errors can cause significant losses.
  • As a result, organizations must implement best security practices in their data handling systems.

5. Efficiently scaling big data systems

       If they don't have a strategy to use big data, companies can waste a lot of money storing it. Organizations must recognize that big data analytics starts with data ingestion.

  • Before deploying big data systems, data management teams must plan the types, programs, and uses of the data. 
  • However, as Travis Rehl, vice president of product at cloud management platform vendor CloudChecker, points out, this is easier said than done.

A common data lake with an appropriate data structure will facilitate efficient and cost-effective data reuse. Parquet files, for example, offer a better performance-to-cost ratio than CSV dumps in a data lake.

6. Organizational Opposition

Anti-establishment has been around for ages, even in other business sectors. 

  • Nothing new here! This is a problem that businesses can anticipate and, as a result, determine the best way to deal with it.
  • If this is already happening at your company, you should know that it is not unusual. 
  • To ensure big data success, it is important to determine the best way to handle the situation.

7. Costs of Big Data Handling

Big data management requires significant costs from the very beginning of adoption.

 

  • For example, if your business decides to use an on-premises solution, be prepared to spend money on new gear, electricity, new staff such as developers and administrators, and more.
  • Although the required frameworks are open source, you are still responsible for the costs associated with developing, installing, configuring, and maintaining the new software.