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Data Management : Tools, Techniques, Storage Platforms, Challenges and Benefits

The Tech Platform presents Talks with Shubham Dumbre speaking on the Topic "Data Management".

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What is Data?

data is information that has been translated into a form that is efficient for movement or processing. Relative to today's computers and transmission media, data is information converted into binary-digital form. It is acceptable for data to be used as a singular subject or a plural subject. Raw data is a term used to describe data in its most basic digital format.

Computers represent data, including video, images, sounds and text, as binary values using patterns of just two numbers: 1 and 0. A bit is the smallest unit of data, and represents just a single value. A byte is eight binary digits long. Storage and memory is measured in megabytes and gigabytes.

What is Data Management:

Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. Effective data management is a crucial piece of deploying the IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers and other end users. The data management process includes a combination of different functions that collectively aim to make sure that the data in corporate systems is accurate, available and accessible.

Why it is important?

Data management is a crucial first step to employing effective data analysis at scale, which leads to important insights that add value to your customers and improve your bottom line. With effective data management, people across an organization can find and access trusted data for their queries. Some benefits of an effective data management solution include:


Data management can increase the visibility of your organization’s data assets, making it easier for people to quickly and confidently find the right data for their analysis. Data visibility allows your company to be more organized and productive, allowing employees to find the data they need to better do their jobs.


Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.


Data management protects your organization and its employees from data losses, thefts, and breaches with authentication and encryption tools. Strong data security ensures that vital company information is backed up and retrievable should the primary source become unavailable. Additionally, security becomes more and more important if your data contains any personally identifiable information that needs to be carefully managed to comply with consumer protection laws.


Data management allows organizations to effectively scale data and usage occasions with repeatable processes to keep data and metadata up to date. When processes are easy to repeat, your organization can avoid the unnecessary costs of duplication, such as employees conducting the same research over and over again or re-running costly queries unnecessarily.

Data Management Tools and Techniques

Data Management is as successful as the tools used to store, analyze, process, and discover value in an organization’s data. In essence, these tools are heterogeneous multi-platform management systems that harmonize data.

The most widely used data management tools belong to the industry’s biggest software groups whose experience guarantees a high degree of performance, security, efficiency, effectiveness, elimination of data redundancy, and privacy that is necessary for companies that are leaving the entire organization’s information in the care of external vendors.

Below are some of the Data management Tools and Techniques:

Database management system

Relational databases organize data into tables with rows and columns that contain database records; related records in different tables can be connected through the use of primary and foreign keys, avoiding the need to create duplicate data entries. Relational databases are built around the SQL programming language and a rigid data model best suited to structured transaction data. That and their support for the ACID transaction properties -- atomicity, consistency, isolation and durability -- have made them the top database choice for transaction processing applications.

Big Data Management

NoSQL databases are often used in big data deployments because of their ability to store and manage various data types. Big data environments are also commonly built around open source technologies such as Hadoop, a distributed processing framework with a file system that runs across clusters of commodity servers; its associated HBase database; the Spark processing engine; and the Kafka, Flink and Storm stream processing platforms. Increasingly, big data systems are being deployed in the cloud, using object storage such as Amazon Simple Storage Service (S3).

Data Warehouses and Data lakes

Two alternative repositories for managing analytics data are data warehouses and data lakes. Data warehousing is the more traditional method -- a data warehouse typically is based on a relational or columnar database, and it stores structured data pulled together from different operational systems and prepared for analysis. The primary data warehouse use cases are BI querying and enterprise reporting, which enable business analysts and executives to analyze sales, inventory management and other key performance indicators.

Data Integration

The most widely used data integration technique is extract, transform and load (ETL), which pulls data from source systems, converts it into a consistent format and then loads the integrated data into a data warehouse or other target system. However, data integration platforms now also support a variety of other integration methods. That includes extract, load and transform (ELT), a variation on ETL that leaves data in its original form when it's loaded into the target platform. ELT is a common choice for data integration jobs in data lakes and other big data systems.

Data Governance, Data Quality and MDM

Data governance is primarily an organizational process; software products that can help manage data governance programs are available, but they're an optional element. While governance programs may be managed by data management professionals, they usually include a data governance council made up of business executives who collectively make decisions on common data definitions and corporate standards for creating, formatting and using data.

Another key aspect of governance initiatives is data stewardship, which involves overseeing data sets and ensuring that end users comply with the approved data policies

Data Modeling

Data modelers create a series of conceptual, logical and physical data models that document data sets and workflows in a visual form and map them to business requirements for transaction processing and analytics. Common techniques for modeling data include the development of entity relationship diagrams, data mappings and schemas. In addition, data models must be updated when new data sources are added or an organization's information needs changes.


A data platform is an integrated technology solution that allows data located in database(s) to be governed, accessed, and delivered to users, data applications, or other technologies for strategic business purposes.

A data platform is a complete solution for ingesting, processing, analyzing and presenting the data generated by the systems, processes and infrastructures of the modern digital organization. While there are many point solutions and purpose-built applications that manage one or more aspects of the data puzzle effectively, a true data platform provides end-to-end data management.

Below are some of the Data Platforms :

Microsoft Azure

Microsoft Azure is Microsoft's public cloud computing platform. It provides a range of cloud services, including compute, analytics, storage and networking. Users can pick and choose from these services to develop and scale new applications, or run existing applications in the public cloud.


Cloudera, Inc. is a US-based company that provides an enterprise data cloud. Built on open source technology, Cloudera’s platform uses analytics and machine learning to yield insights from data through a secure connection. Cloudera’s platform works across hybrid, multi-cloud and on-premises architectures and provides multi-function analytics throughout the edge to AI data lifecycle.


Sisense Fusion is the AI-driven embedded analytics platform that infuses intelligence into your workflows, processes and applications to enhance the customer experience and transform your business from the inside out.


Collibra Software is an enterprise-oriented data governance platform for data management and stewardship. It empowers businesses to find meaning in their data and improve business decisions. With one shared platform, business users and IT can collaborate to form a data-driven culture using the Collibra product suite.


Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data in a very easily understandable format. Tableau helps create the data that can be understood by professionals at any level in an organization. It also allows non-technical users to create customized dashboards.


MapR Technologies is a distributed data platform for AI and analytics provider that enables enterprises to apply data modeling to their business processes with the goal of increasing revenue, reducing costs and mitigating risks. Complexities of high-scale and mission critical data can be processed across a combination of channels and deployments with MapR.


Oracle big data services help data professionals manage, catalog, and process raw data. Oracle offers object storage and Hadoop-based data lakes for persistence, Spark for processing, and analysis through Oracle Cloud SQL or the customer’s analytical tool of choice.


MongoDB is an open-source database that uses a document-oriented data model and a non-structured query language. It is one of the most powerful NoSQL systems and databases around, today. MongoDB Atlas is a cloud database solution for contemporary applications that is available globally. This best-in-class automation and established practices offer to deploy fully managed MongoDB across AWS, Google Cloud, and Azure.


Datameer Professional, is a SaaS big data analytics platform targeted for department specific deployments. Bigdata Analytics Platform. Sub Category. Data Preparation Software, Data Discovery Software, Data Analysis Software, Analytics Platform, Bigdata Analytics Platform, Bigdata Platform

Data Storage Platforms

Data Storage is a collection of digital information - the bits and bytes behind applications, network protocols, documents, media, address books, user preferences, and more. Data storage is a central component of big data.

There are different types of Data Storage which are listed as below:

  • Software defined storage

  • Cloud Storage

  • Network attached Storage

  • Object Storage

  • File Storage

  • Block Storage

Software Defined Storage

Software-defined storage (SDS) is a storage architecture that separates storage software from its hardware. Unlike traditional network-attached storage (NAS) or storage area network (SAN) systems, SDS is generally designed to perform on any industry-standard or x86 system, removing the software’s dependence on proprietary hardware.

Cloud Storage:

Cloud storage is a cloud computing model that stores data on the Internet through a cloud computing provider who manages and operates data storage as a service. It’s delivered on demand with just-in-time capacity and costs, and eliminates buying and managing your own data storage infrastructure. This gives you agility, global scale and durability, with “anytime, anywhere” data access.'

Below are top Cloud Storage Platforms:

  1. IDrive : IDrive provides Online Backup to Cloud for PCs, Macs, iPhones, Android and other Mobile Devices all into ONE account for one low fee.

  2. Google Drive : Google Drive is a cloud-based storage solution that allows you to save files online and access them anywhere from any smartphone, tablet, or computer.

  3. NextCloud : Nextcloud is open-source software, first developed in 2016, that allows you to run a personal cloud storage service. It has features that are comparable to other services such as Dropbox.

  4. pCloud : pCloud is your personal cloud space where you can store all your files and folders. It has a user-friendly interface that clearly shows where everything is located and what it does. The software is available for almost any devices and platforms – iOS and Android devices, MacOSX, Windows OS, and all Linux distributions.

  5. Box : Box is a cloud-based file storage and file sharing service that provides individuals and business easy-to-use cloud storage solutions and collaboration tools.

  6. Microsoft OneDrive : OneDrive is the Microsoft cloud service that connects you to all your files. It lets you store and protect your files, share them with others, and get to them from anywhere on all your devices.

  7. SpiderOak One : SpiderOak is a US based online backup tool to back up, share, sync, access and store data using an off-site server. SpiderOak is accessible through an app for Windows, Mac and Linux computer platforms, and Android, N900 Maemo and iOS mobile platforms. SpiderOak allows the user to back up any given folder of his/her computer.

  8. iCloud : iCloud securely stores your photos, videos, documents, music, apps and more — and keeps them updated across all your devices. With iCloud, you can easily share photos, calendars, locations and more with friends and family. You can even use iCloud to help you find your device if you lose it.

  9. MEGA : MEGA is a cloud storage service focused on security that offers users excellent end-to-end encryption and a great free plan with tons of free storage. However, its history has been marked by controversy, and its zero-knowledge-encryption makes collaboration difficult.

Network Attached Storage

An NAS (Network Attached Storage) device is a storage device connected to a network that allows storage and retrieval of data from a central location for authorised network users and varied clients. NAS devices are flexible and scale out, meaning that as you need additional storage, you can add to what you have. NAS is like having a private cloud in the office. It’s faster, less expensive and provides all the benefits of a public cloud on site, giving you complete control.

Object Storage

Object storage, also known as object-based storage, is a strategy that manages and manipulates data storage as distinct units, called objects. These objects are kept in a single storehouse and are not ingrained in files inside other folders. Instead, object storage combines the pieces of data that make up a file, adds all its relevant metadata to that file, and attaches a custom identifier

File Storage

File storage—also called file-level or file-based storage—is a hierarchical storage methodology used to organize and store data on a computer hard drive or on network-attached storage (NAS) device. In file storage, data is stored in files, the files are organized in folders, and the folders are organized under a hierarchy of directories and subdirectories. To locate a file, all you or your computer system need is the path—from directory to subdirectory to folder to file.

Block Storage

Block storage is a storage scheme in which each volume acts as a separate hard drive, configured by the storage administrator. Data is stored in fixed-size blocks. A unique address serves as the metadata describing each block. Block storage management software is usually controlled by the server operating system.

Data Management Risks and Challenges

The current business landscapes require all the companies to provide secure Data Management System and Applications anytime and anywhere. While providing requirements, Data management challenges arise as below:

  1. Storing and utilizing accumulating volumes of data without crushing systems

  2. Keeping databases running optimally to ensure applications perform productively and remain available

  3. Complying with stricter regulatory mandates, forcing modern security practices and access control measures

Challenge 1: Amount of Data Collected

Introduction of big data, risk managers and other employees are often overwhelmed with the amount of data that is collected. An organization may receive information on every incident and interaction that takes place on a daily basis, leaving analysts with thousands of interlocking data sets.


There should be Data System that automatically collects and organize information. Manually it is possible but its Time-Consuming. The Automated System will allow to use the time in other act.

Challenge 2: Meaningful and real-time Data

It is difficult to access the data which we need the most. Sometimes, employees may not fully analyze data or they focus on those measures which are easier to collect. Manually, it is impossible for the employee to gain real-time insight on what is currently happening. outdated data can give negative impact on Decision Making,


A data system that collects, organizes and automatically alerts users of trends will help solve this issue. Employees can input their goals and easily create a report that provides the answers to their most important questions. With real-time reports and alerts, decision-makers can be confident they are basing any choices on complete and accurate information.

Challenge 3: Visual representation of Data

Data should always be visually presented in graphs or charts. It is difficult to do manually. It takes lots of time to collect the data from multiple data and to put in reporting tools.


Strong data systems enable report building at the click of a button. Employees and decision-makers will have access to the real-time information they need in an appealing and educational format.

Challenge 4: Data for Multiple sources

To analyze data across multiple, disjointed sources is difficult task. Different pieces of data are often housed in different systems. Employees may not always realize this, leading to incomplete or inaccurate analysis. Manually combining data is time-consuming and can limit insights to what is easily viewed.


With a comprehensive and centralized system, employees will have access to all types of information in one location. Not only does this free up time spent accessing multiple sources, it allows cross-comparisons and ensures data is complete.

Challenge 5: Inaccessible Data

Moving data into one centralized system has little impact if it is not easily accessible to the people that need it. Decision-makers and risk managers need access to all of an organization’s data for insights on what is happening at any given moment, even if they are working off-site. Accessing information should be the easiest part of data analytics.


An effective database will eliminate any accessibility issues. Authorized employees will be able to securely view or edit data from anywhere, illustrating organizational changes and enabling high-speed decision making.

Challenge 6: Poor Data Quality

Without good input, output will be unreliable. A key cause of inaccurate data is manual errors made during data entry. This can lead to significant negative consequences if the analysis is used to influence decisions. Another issue is asymmetrical data: when information in one system does not reflect the changes made in another system, leaving it outdated.


A centralized system eliminates these issues. Data can be input automatically with mandatory or drop-down fields, leaving little room for human error. System integrations ensure that a change in one area is instantly reflected across the board.

Challenge 7: Pressure

As risk management becomes more popular in organizations, CFOs and other executives demand more results from risk managers. They expect higher returns and a large number of reports on all kinds of data.


With a comprehensive analysis system, risk managers can go above and beyond expectations and easily deliver any desired analysis. They’ll also have more time to act on insights and further the value of the department to the organization.

Challenge 8: Lack of Support

Data analytics can’t be effective without organizational support, both from the top and lower-level employees. Risk managers will be powerless in many pursuits if executives don’t give them the ability to act. Other employees play a key role as well: if they do not submit data for analysis or their systems are inaccessible to the risk manager, it will be hard to create any actionable information.


Emphasize the value of risk management and analysis to all aspects of the organization to get past this challenge. Once other members of the team understand the benefits, they’re more likely to cooperate. Implementing change can be difficult, but using a centralized data analysis system allows risk managers to easily communicate results and effectively achieve buy-in from multiple stakeholders.

Challenge 9: Confusion

Users may feel confused or anxious about switching from traditional data analysis methods, even if they understand the benefits of automation. Nobody likes change, especially when they are comfortable and familiar with the way things are done.


To overcome this HR problem, it’s important to illustrate how changes to analytics will actually streamline the role and make it more meaningful and fulfilling. With comprehensive data analytics, employees can eliminate redundant tasks like data collection and report building and spend time acting on insights instead.

Challenge 10: Budget

Risk is often a small department, so it can be difficult to get approval for significant purchases such as an analytics system.


Risk managers can secure budget for data analytics by measuring the return on investment of a system and making a strong business case for the benefits it will achieve. For more information on gaining support for a risk management software system, check out our blog post here.

Challenge 11: Shortage of Skills

Some organizations struggle with analysis due to a lack of talent. This is especially true in those without formal risk departments. Employees may not have the knowledge or capability to run in-depth data analysis.


This challenge is mitigated in two ways: by addressing analytical competency in the hiring process and having an analysis system that is easy to use. The first solution ensures skills are on hand, while the second will simplify the analysis process for everyone. Everyone can utilize this type of system, regardless of skill level.

Challenge 12: Scaling Data Analysis

Analytics can be hard to scale as an organization and the amount of data it collects grows. Collecting information and creating reports becomes increasingly complex. A system that can grow with the organization is crucial to manage this issue.


While overcoming these challenges may take some time, the benefits of data analysis are well worth the effort. Improve your organization today and consider investing in a data analytics system.


1. Improve Data Quality

Data management eliminates bad data. Users can work with current data which is of better quality and is more usable. If the Data is stored in multiple locations like in different format, spreadsheets or in isolated applications, it can reduce the usability of the data . This ensures consistency and uniformity across the data which makes it more efficient and effective.

2. Reduce Time and Cost

It is difficult to manage the data in Big companies because of increasing volume of data. The Complexity of the data makes manual processing difficult which takes lots of time to process data accuracy. The Data Management Tools and Techniques helps companies to reduce the Data Management and Processing cost.

3. Avoid Data Duplication

Redundancy is the major issue with decentralized data applications. Data duplication leads to confusion and errors in the data . The Data Management builds single data source that eliminates duplication of the data and increase the efficiency of the business process.

4. Increase Data Accuracy

Data Management reduces the risk of data inaccuracy. With this , it also provide proper structure without any confusion while retrieving the data from the application.

5. Better Data Compliance

Efficient data storage and management are critical to any business dealing with data. Data Management techniques decrease the chance of security breaches and regulatory non-compliance.

6. Informed Decision Making

Incomplete and wrong information would allow management to make misinformed decisions that would impact the long-term growth of the company. Access to updated and quality data would assist the managers to develop effective strategies. This helps the leadership, senior management, and middle management to make informed decisions.

7. Handling Change Requests

As data is the single source of crucial data that is used by the departments across the organization, it is vital to protect the data from misuse. Restricted access to change data can help ensure data security and enable data consistency.

8. Enables Easy Data Edits

Any changes made to the data would not only remain isolated but would also create major data inconsistency issues. With Data Management any changes made to the master data will reflect across all the relevant data destinations.

Sofia Sondh

The Tech Platform

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