Sunday, September 19, 2021

Why Big Data Management Is Crucial In 2021: Let’s Check Out!

Introduction

Exponentially growing businesses and enterprises continuously produce and consume large amounts of unstructured data, and managing this large pile of data has been a challenge for organizations. Maybe the data they have will be outdated, it may contradict the existing data or sometimes the data will be inaccurate. According to the 2020 Global Data Management Benchmark Research Report, Organisations continuously report that more than 30% of the data they have is inaccurate. Organizations call it Data Dept.

The good news is that more than 78% of the organizations have identified data debt as a serious issue and 24% of them already implemented a plan to tackle the issue and thus opening a lot of opportunities in the field of big data management as it is the one and the only solution for this issue.

What Is Big Data Management?

Big data management is a broad concept, that involves many job titles. As we all know big data comes with a wide variety in high volume with high velocity, managing this data flow can be a tedious task, so naturally, it involves many people. Chief data officer (CDO), chief information officer (CIO), data managers, database administrators, data architects, data modelers, data scientists, data warehouse managers, data warehouse analysts, business analysts, developers are some of them.

Big data management involves the collection, storage, governance, organization, administration, and delivery of large repositories of data. It can include data cleansing, migration, integration, and preparation for use in reporting and analytics. As we can see there are many steps in big data management it plays a vital role in all businesses and organizations.

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6 Vs of Big Data Management

Challenges Faced By Big Data Management

As we have seen, Big data management is a process that involves terabytes or even petabytes of data, so naturally, there are many challenges and issues faced by companies here. Let’s see some of the major challenges.

Scattered Data Over Different Storages

Companies and organizations store their data in many storage devices, also different departments of the same company will maintain different databases. There will not be any communication between different storage devices and databases, which arises a situation where the same data is stored in different databases. It increases the amount of duplicate data and consumes large sums of storage.

Identifying The Required Data

Enterprises will have a large amount of structured and unstructured database which stores a wide variety of data with different file formats. Identifying and transferring some specific data from the whole pile of data can be a hard job.

Ensuring The Data Quality

As we have seen before more than 30% of the data that organizations have is inaccurate, which makes those data unreliable. 40% of the organizations say they don’t trust the insights made from data because of this degree of inaccuracy. So a big data management system should ensure data quality.

Inadequate staffing

As discussed before there are many job titles required for big data management. The average pay scale for those jobs is very high ( we have discussed this comprehensively in a previous article). This has made big data management a luxury for small organizations, which is only affordable for established enterprises. Clearly, there is also a lack of skilled employees, currently, the demand for big data skills exceeds the supply.

Need And Benefits Of Big Data Management

As we have seen the major challenges faced by organizations in the previous part, now let’s take some time to explain the need and benefits of big data management.

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Benefits Of Big Data Management

Increase In Revenue, Efficiency And Customer Service

More than 60% of the organizations say that they experience a tremendous increase in revenue after doing big data management, 57 percent of organizations said maintaining high-quality contact data helped them increase efficiency and 56% of those organizations said that they have improved customer service through big data management.

Marketing Advantage And Competitive Advantage

More than 39% of organizations say that they have enhanced the marketing with the implementation of big data management and 57% of them also reported competitive advantage.

Improved Accuracy In Analytics

When compared with previous traditional methods of data analytics which were mainly pen and paper-dependent big data and data analytics using computer proves to be more reliable and efficient and also cheap. Previously the decisions were mainly taken on a gut feeling but with data analytics, more curated decisions can be taken.

Enabling New Applications

Organizations that trust data have reported that they are using big data analytics to do more innovations and that is enabling them to make new products and applications also they are helping to improve the current products by upgradation.

Big Data Management Tools For Professionals

According to 2020 Big Data & Analytics Survey Results more than 33% of the organizations say that they are investing more in free open source big data platforms like Hadoop. Let’s see some of the tools used for big data management.

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Big Data Management tools
NameFeatures
1HadoopHadoop is absolutely free of cost.
It is one of the best big data tools designed to scale up from single servers to thousands of machines.
It has big data technologies and tools that offer a robust ecosystem that is well suited to meet the analytical needs of the developer.
It brings Flexibility In Data Processing.
It allows for faster data Processing.
2HPCC HPCC is a Free big data tool developed by LexisNexis Risk Solution.
It delivers on a single platform, a single architecture, and a single programming language for data processing.
It is one of the Highly efficient big data tools that accomplish big data tasks with far less code.
It is one of the big data processing tools which offers high redundancy and availability.
It automatically optimizes code for parallel processing
Provide enhance scalability and performance
3QuboleQubole has 30 days free trial and late affordable paid plans.
Single Platform for every use case.
It is an Open-source big data software having Engines, optimized for the Cloud.
Comprehensive Security, Governance, and Compliance.
4CassandraApache Cassandra is a free open-source project.
Support for replicating across multiple data centers by providing lower latency for users
Data is automatically replicated to multiple nodes for fault-tolerance
It one of the best big data tools which are most suitable for applications that can’t afford to lose data, even when an entire data center is down
Cassandra offers support contracts and services are available from third parties
5PentahoPentaho is developed by Hitachi Vantara it has both a free edition and an enterprise edition.
Data access and integration for effective data visualization
It is a big data software that empowers users to architect big data at the source and streams them for accurate analytics
Seamlessly switch or combine data processing with in-cluster execution to get maximum processing
6FlinkApache Flink is another free opensource platform for big data
Provides results that are accurate, even for out-of-order or late-arriving data
It is stateful and fault-tolerant and can recover from failures
It is a big data analytics software that can perform at a large scale, running on thousands of nodes
Has good throughput and latency characteristics
7Informatica Big Data ManagementInformatica is a GUI-based integrated development tool. It comes with one month trial and later on the paid version. More about them in the later part of the article.

Also read : 21 amazing big data tools that lead your business to the next level.

These are the most common big data platforms used by enterprises, there are many more like hive, Cloudera, rapid miner, etc. As we can see almost all the platforms are either free or come at a cheap price this has made these tools available for everyone including small-scale business owners. But the challenge that we discussed before is making big data a hurdle for them.

Where To Learn Big Data Management

As we have seen there is a huge demand for skilled data scientists to work under this segment, adequate knowledge and experience will help you to land a job. In this segment let’s discuss some famous online courses on this topic.

NameDescription
1SimplilearnSimplilearn has a wide range of online courses that are affiliated with market-leading companies like IBM. Their course catalog can be found here. Simplilearn’s courses offer one of the most comprehensive training schemes of any provider on this list.
2CourseraOffered in partnership with the University of California, San Diego, Coursera’s online training is as good as what you’d find on college campuses. Each course begins with the basics, and learners can take them one at a time, or do a Big Data Specialization.
3edxedx has collaborations with top universities across the globe and presents top-quality content their certificates also have good value when it comes to job interviews.
4Big Data UniversityWith backing from IBM, the Big Data University offers courses at the beginner and intermediate levels. Their e-learning content and videos can be consumed at the learner’s desired pace and difficulty level.
5ClouderaCloudera is probably the most familiar name in the field of Big Data training. Their CCP Spark and Hadoop Developer certification is recognized around the world and is conducted in both virtual and physical classrooms.
YouTube video

How Big Data Management Is Carried Out : System And Techniques Involved

Big data management is closely related to the idea of data lifecycle management (DLM). This is a policy-based approach for determining which information should be stored within an organization’s IT environment, as well as when data can safely be deleted.

Following are the Steps involved in big data management

  • Data cleansing: finding and fixing errors in data sets, removing duplicate and inaccurate data.
  • Data integration: combining data from two or more sources.
  • Data migration: moving data from one environment to another, such as moving data from in-house data centers to the cloud.
  • Data preparation: readying data to be used in analytics or other applications.
  • Data enrichment: improving the quality of data by adding new data sets, correcting small errors, or extrapolating new information from raw data.
  • Data analytics: analyzing data with a variety of algorithms in order to gain insights.
  • Data quality: making sure data is accurate and reliable
  • Master data management (MDM): linking critical enterprise data to one master set that serves as the single source of truth for the organization.
  • Data governance: ensuring the availability, usability, integrity, and accuracy of data.
  • Extract transform load (ETL): moving data from an existing repository into a database or data warehouse.

Practical Applications Of Big Data Management

We have covered almost all the theories related to this topic, now it’s time for us to look into some practical applications of big data management in the real world. We are taking examples of supply chain management, Health care, and traffic management but there are many other applications of it the reason why we are not discussing all of them is self-explanatory.

Big Data Management In Supply-Chain And Logistics

Usage of big data in the supply chain is often called Big Supply Chain Analytics. It uses data and quantitative methods to improve decision-making for all activities across the supply chain. Many practical problems like choosing the right supply chain model can be tackled using big data. This primarily involves 2 steps.

First, it expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems.

Second, it applies powerful statistical methods to both new and existing data sources. This creates new insights that help improve supply chain decision-making.

Big Data Management In Healthcare

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18 Examples of Big Data In Healthcare Image: Datapine

It becomes challenging to perform healthcare data analysis based on traditional methods which are unfit to handle the high volume of diversified medical data. In general, the healthcare domain has four categories of analytics: descriptive, diagnostic, predictive, and prescriptive analytics

Lets brief each one of them

Descriptive Analytics: It consists of describing current situations and reporting on them.

Diagnostic Analysis: It aims to explain why certain events occurred and what the factors that triggered them are.

Predictive Analytics: It reflects the ability to predict future events; it also helps in identifying trends and determining probabilities of uncertain outcomes.

Prescriptive Analytics: Its goal is to propose suitable actions leading to optimal decision-making.

Several techniques are employed in this field to generate desired outcome.

Big Data In Traffic Management

Clearly, traffic has a major impact on liveability and efficiency in cities. Effective use of data and sensors will be key to managing traffic better as cities become increasingly densely populated. The increasing population and an increasing number of vehicles running on the road have made traffic management a risky job for traditional methods to handle.

Steps involved in traffic management are

Enable centralized management of traffic data: Establishment of centralized system access to image and video, traffic data stored in the data centers of different divisions, traffic management facilities, equipment, and application system.


Optimize utilization of massive data: Storage of massive amount of vehicle data to help other departments like public security, criminal investigation, economic investigation, etc.


Improve traffic flow across the city: Enhance dispatch capability for dealing with various
kinds of emergencies and accurately forecast traffic patterns.

What is Informatica Big Data Management?

Informatica Big Data Management (BDM) product is a GUI-based integrated development tool. This tool is used by organizations to build Data Quality, Data Integration, and Data Governance processes for their big data platforms.

YouTube video

Informatica BDM helps to accelerate your data engineering productivity with an easy-to-use visual interface that can be up to 5x faster than hand-coding. Informatica comes with a one-month trial period and charges you different money according to the plan you choose. Rates are shown below

Professional$1,000per month
Basic$2,000per month
Advanced$4,500per month

Apart from these plans there is also a premium plan, to know more about it visit

Big brands like Asian paints, AT&T, American Airlines, BMW Group trust and uses Informatica for their big data needs.

Role Of Statistics In Big Data Management

Probability and statistics are the core elements of data analysis and big data. All the learning models and predictive models depend on statistics and probability, big data problems require a multidisciplinary team, including subject experts, computational experts, machine learning experts, and statisticians. It is evident that statistical methodology is absolutely critical in making inferences.

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Lets discuss the major roles of statistics in big data management

  • Statistics is fundamental to ensuring meaningful, accurate information is extracted from Big Data.
  • Statisticians help translate the scientific question into a statistical question.
  • Statistics will help in carefully describing the underlying system that generated the data (data structure).
  • It also helps us to understand the parameters we wish to estimate or predict.
  • Statistics are widely used in the field of data visualization also.

These are some of the benefits of statistics in big data management, as we have seen statisticians play a monumental role in the process. If you are learning statistics for big data and data analytics following are the key terminologies that you should focus on

  • Population: It is an entire pool of data from where a statistical sample is extracted.
  • Sample: It is a subset of the population, that is, it is an integral part of the population that has been collected for analysis.  
  • Variable: A value whose characteristics such as quantity can be measured, it can also be addressed as a data point, or a data item.  
  • Distribution: The sample data that is spread over a specific range of values.  
  • Parameter: It is a value that is used to describe the attributes of a complete data set. For example Average, Percentage  
  • Quantitative analysis: It deals with specific characteristics of data- summarizing some parts of data, such as its mean, variance, and so on.  
  • Qualitative analysis: This deals with generic information about the type of data, and how clean or structured it is.

Conclusion

With 2.5 quintillion bytes of unstructured data produced a day the industry is facing a tremendous need for management of this huge amount of data. Many organizations have identified this uncategorized data as a major problem and have opened a lot of job opportunities in this field.

In this article, we have addressed some common questions like what is meant by big data management, why companies need to do it, benefits and challenges faced by tools used for it, and practical applications of big data in our daily life.

Hope you guys learned something from this article, if so please consider sharing it with your friends and read other informative articles about data science, data analytics, and big data.

FAQs

Who Manages Big Data?

Organizations that own the data manage the data. Organizations will appoint skilled professionals to do the job of Chief data officer (CDO), chief information officer (CIO), data managers, database administrators, etc. These skilled professional deals with the tedious task of big data management.

How Would The Big Data Management Meet The Technology Requirements Of Your Business?

A data-driven culture will enrich the productivity of an enterprise. Technologies like Predictive Analytics, NoSQL Databases, Knowledge Discovery Tools, Stream Analytics have made business easier to run. Data visualization and statistics enabled businesses to identify the areas where more concentration is needed.

What Are The Other Technologies That Are Used To Manage And Analyze Huge Data Apart From Big Data?

Big data can sometimes be a hurdle to jump over, in such cases some big data alternatives can be used to manage and analyze huge data. Keatext, Litmus Edge, Statgraphics, Centurion, Splunk Enterprise, Phocas Software are some of the big data alternatives In the market.

Why Big Data Management Is Crucial In 2021: Let's Check Out!

Hi, I am Sandyagu r, a Kerala-based freelance content writer and web developer. Currently, I am doing my bachelor's degree in electrical and electronics engineering from the college of engineering Trivandrum. My interests include Data science and related fields, computer vision, financial technology, battery management systems. My skill set includes web development, literature, video editing, and photo editing.

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Hi, I am Sandyagu r, a Kerala-based freelance content writer and web developer. Currently, I am doing my bachelor's degree in electrical and electronics engineering from the college of engineering Trivandrum. My interests include Data science and related fields, computer vision, financial technology, battery management systems. My skill set includes web development, literature, video editing, and photo editing.