What is Big Data Management?

What Is Big Data? 

Big data refers to the practice of collecting data in huge volumes. This data also grows exponentially with time. Such data is so vast and complex that it cannot be managed with traditional data management tools. It cannot be stored or processed efficiently. 

In this scenario, big data is defined as data that contains a great deal of variety, comes in ever-increasing volumes, and with a lightning velocity. This is called the three Vs of big data. 

This data is collected because data has intrinsic value, but it is of no use until that value is discovered. 

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What Is Big Data Management? 

Big data management refers to the organisation, administration and governance of large volumes of unstructured and structured data. A high level of data quality and accessibility for business intelligence and big data analytics applications is the aim of big data management. Businesses, enterprises, and governments use big data management strategies to tackle the vast and rapidly expanding data pools that typically have hundreds of terabytes or even petabytes of data stored in various file formats. Facebook, for instance, gets over 500 terabytes of new data into their databases daily.

A company's ability to locate valuable information in extensive stacks of unstructured and semi-structured data from a variety of disparate sources, such as call records, system logs, images, social media sites, and sensors, is aided by effective big data management.

Big data management includes the following processes: 

• Using a centralised interface or dashboard to monitor and ensure the availability of all big data resources
• Maintaining the database to get better outcomes.
• Monitoring big data analytics, big data reporting and other similar solutions and implementing them
• Efficient design and implementation of data cycle processes
• Control access and security of big data repositories 
• Data visualization to reduce volume and improve big data operations 
• Data visualization techniques allow multiple users to use it simultaneously. 
• Capturing and storing data from all resources. 

Big Data Management Challenges

Here we look at the significant challenges big data management faces:

• Data Silos: A data silo refers to the event where data is isolated from other departments in an organisation that consists of many such departments. It leads to duplicate information and wastage of storage space. 
• Growing data storage: The sheer size and the scale of data involved makes it difficult for the company to manage. It slows down the systems, affecting performance. 
• Data Complexity: The kind of data that comes through may be structured or unstructured. It could be of different formats as well. This immensely complex kind of data coming in at huge volumes can be challenging to sort. 
• Maintaining Data quality: The data quality is also affected due to the volume of the data. In the case of data silos, there is also difficulty in synchronising the data. It affects the overall quality of the data. 
• Inadequate manpower: The size of the big data is directly proportional to the expert staffing required to manage the data and its tools. It increases the cost to the company in the form of salary. 
• Shifting to a Data-friendly Culture: Transitioning from a manual to a data-driven decision-making culture is long and hard. Doing it effectively is a challenge.

Big Data Management Benefits 

As much as there are challenges to implementing big data management, there are numerous benefits. Let us take a look at some of them. 

• Higher Revenue: When data is managed correctly, organisations have increased revenue. With enhanced data quality solutions, there is an increase in revenue as well. 
• Better customer service: Big data initiatives almost always state customer service as the primary objective. Big data management gives the benefit of better customer service. 
• Better Marketing: With timely and personalised customer communications, the marketing quality also has a big increase from big data management. This is primarily due to better data quality. 
• Cost Effective: Big data management increases the efficiency of efforts to decrease expenses. With big data implementation, processes become more cost-effective. 
• Accurate Analytics: The accuracy and dependability of big data analytics can be improved by big data management practices. When well-formed data enters the analytics solution, the organisation is prepared for the solution's high-quality business insights.
• Competitive advantage: Big data management gives businesses a competitive edge because it enables analytics, which gives them an advantage over their rivals. 

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Big Data Management Best Practices 

With the swift and vast adoption of big data management across industries, there is a need for good practices that will ensure consistency and trust in the analytic results. 

1. Doing Big Data Management On Your Own

With permission for data discovery, users can independently peruse the data. Powered by adequate data preparation tools and the information from the numerous data sets, users can present it for analysis. This way, employees can do self-service in terms of big data management.

2. Do Away With a Predefined Data Model

The large variety of data sets means there is no point in using predefined data models. The sorting of raw data or metadata is guided by solid procedures for documenting the business glossary, maintaining a collaborative environment of sharing insights, and mapping business terms to data elements. 

3. Methods to Manage User Transformations of Raw Data

Since there is no cleaning and data standardisation, the raw form of the data is used. It is the user's responsibility to apply necessary transformations. Since these may differ from person to person, there is a need for methods to manage the different transformations to avoid conflict. 

4. Understanding the Architecture Improves Performance

You can create reasonably high-performing data applications by comprehending how the big data architecture organises data and how the database execution model optimises queries.

5. Managing the Exploding Data. 

The core issue of big data management is an explosion of data. Data is generated from everything, and it is streaming in. Managing this must include technology to support stream processing that scans the data, filters it and selects meaningful information for capture, storage, and later access. 

Top Five Big Data Management Tools 

There are different tools out there that are useful for managing big data. There is better time management of data analytical tasks and cost efficiency. Here are some of them. 

1. IBM Infosphere Information Server: This data management tool from IBM is the go-to data integration platform for understanding, cleansing, monitoring, and transforming data.
2. SAS Data Management: This data management tool allows business users to update data, tweak processes and analyse results, with capabilities like third-party metadata management, lineage visualizations etc. 
3. PowerCenter Informatica is an enterprise extract, transform, and load (ETL) tool used to gather data from sources and create data warehouses.
4. Pentaho Business Analytics: It is a BI system with a platform for data integration and analytics that is intended to assist businesses in making data-driven decisions.
5. Tableau: To produce graph-style data visualizations, Tableau assists in querying relational databases, online analytical processing cubes, cloud databases, and spreadsheets.

Big Data Management Services 

Big data management tools provide different types of features. Here are some common types of big data management solutions in the market.

• Data cleansing tool to find and fix errors in a data set
• Data migration tool  to move data around to different environments
• Data integration tool to combine data from different sources
• Data enrichment tool to improve the quality of the data
• Data analytics tools to analyse data and gain insights from algorithms
• Data quality tools to make sure data is reliable and accurate 
• Data preparation tool  to prepare data for analytics

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Summing It Up

The world of big data is expanding and constantly opening up new opportunities. It is a dynamic field, but it’s here to stay. If you are looking for an entry in or a career shift to data management, a course from a trusted source like Simplilearn can be a solid stepping stone. When you begin your journey in the field with a Professional Certificate Course in Data Engineering in partnership with Purdue University and IBM, it’s a winning start. With a widely-recognized certificate upon completion, you will be ready to enter the field of big data management with a bang. Enrol today to get started!

FAQs

1. What do you mean by big data management?

Big data management refers to the different practices organisations apply to use a large amount of raw data to extract value from it. 

2. What are the three types of big data?

The three types of big data refer to structured, semi-structured, and unstructured data. 

3. What are the four components of big data?

The four components of big data are denoted by the 4 Vs. Volume, which refers to how much data is collected, Veracity refers to how reliable the data is, and Velocity refers to how fast data can be generated, gathered and analysed. Finally, Variety refers to the different formats of the data. 

4. How do we manage big data?

Big data management requires using efficient data management tools and implementing data management best practices. 

5. What are the four types of data management?

The four types of data management refer to CRM or customer relationship management system, marketing technology system, data warehousing system and analytics tools system.