The Tech Platform presents Talks with Shubham Dumbre speaking on the Topic "Data Management".
Watch the video on YouTube - https://www.youtube.com/watch?v=hUUSb7BDBt0
What is Data?
Data refers to information that has been transformed into a format conducive to efficient movement or processing. In the context of contemporary computers and transmission media, data takes the form of binary-digital representation. It is grammatically acceptable to use "data" as both a singular and a plural noun. Raw data refers to information in its most fundamental digital state.
Computers encode various types of data, such as video, images, sounds, and text, into binary values, utilizing patterns of only two numbers: 1 and 0. The smallest unit of data is a bit, representing a single value, while a byte consists of eight binary digits. Storage and memory capacities are measured in megabytes and gigabytes.
What is Data Management:
Data Management involves the systematic processes of ingesting, storing, organizing, and maintaining the data generated and collected by an organization. Effective data management plays a pivotal role in implementing IT systems that operate business applications and furnish analytical insights for operational decision-making and strategic planning.
Corporate executives, business managers, and other end users rely on data management to ensure that the data within organizational systems remains accurate, available, and accessible. The data management process encompasses a combination of functions aimed at achieving these objectives.
Why Data Management is important?
Effective data management is crucial for large-scale data analysis, leading to valuable insights that benefit customers and enhance profitability. It enables easy access to trusted data across an organization, offering benefits such as:
Visibility: Improved visibility of data assets enhances organizational efficiency, enabling quick and confident data retrieval for analysis. This organization boosts productivity, helping employees find the necessary data to enhance their job performance.
Reliability: Data management establishes processes and policies, minimizing errors and building trust in the data used for decision-making. Reliable, up-to-date data allows companies to respond efficiently to market changes and customer needs.
Security: Protecting against data losses, theft, and breaches, data management employs authentication and encryption tools. Strong security ensures vital company information is backed up and retrievable, especially when dealing with personally identifiable information subject to consumer protection laws.
Scalability: Data management facilitates efficient scaling of data and usage with repeatable processes. This scalability prevents unnecessary costs associated with duplicative efforts, ensuring that research and queries are not needlessly repeated, saving time and resources for the organization.
Benefits of Data Management
Data Management plays a crucial role in optimizing the utilization of data within an organization. Here are the key benefits associated with effective Data Management:
1.Improve Data Quality:
Data Management eliminates poor-quality data by centralizing and organizing it. This ensures that users work with current, high-quality, and more usable data. Centralizing data prevents inconsistencies and variations in format, improving its efficiency and effectiveness.
2. Reduce Time and Cost:
Managing data in large companies, especially with increasing data volumes, can be challenging. Manual processing becomes complex and time-consuming. Data Management Tools and Techniques help reduce the time and cost associated with data management and processing.
3. Avoid Data Duplication:
Decentralized data applications often suffer from redundancy issues, leading to confusion and errors. Data Management establishes a single data source, eliminating duplication and enhancing the efficiency of business processes.
4. Increase Data Accuracy:
Data Management minimizes the risk of data inaccuracies by providing a structured and clear framework for retrieving data from applications. This ensures that the data is accurate and reliable.
5. Better Data Compliance:
Efficient data storage and management are essential for businesses dealing with data. Data Management techniques decrease the likelihood of security breaches and non-compliance with regulations, ensuring better data compliance.
6. Informed Decision Making:
Access to updated and high-quality data enables informed decision-making at all levels of the organization. This prevents misinformed decisions that could impact the long-term growth of the company, empowering leadership and management to develop effective strategies.
7. Handling Change Requests:
Data Management safeguards crucial data from misuse by controlling access to modify data. This ensures data security and consistency, particularly when dealing with change requests from various departments across the organization.
8. Enables Easy Data Edits:
Data changes made in one part of the organization can have far-reaching effects. With Data Management, any modifications to master data are reflected consistently across all relevant data destinations, preventing isolated changes and data inconsistency issues.
Data Management Tools and Techniques
Data Management Tools and Techniques play a crucial role in the success of handling, analyzing, processing, and extracting value from an organization's data. These tools, essentially heterogeneous multi-platform management systems, aim to streamline and harmonize data processes.
The industry's leading software groups provide widely used data management tools, offering a wealth of experience that ensures high performance, security, efficiency, and effectiveness. Eliminating data redundancy and maintaining privacy is particularly essential for organizations entrusting their entire information portfolio to external vendors.
Key Data Management Tools and Techniques include:
Relational databases organize data into tables with rows and columns, using primary and foreign keys to establish connections between related records.
SQL programming language is integral to relational databases, making them well-suited for structured transaction data.
ACID transaction properties (Atomicity, Consistency, Isolation, Durability) contribute to their popularity in transaction processing applications.
Big Data Management:
NoSQL databases are prevalent in big data environments for managing diverse data types.
Open-source technologies like Hadoop, HBase, Spark, Kafka, Flink, and Storm are commonly employed in big data systems.
Cloud deployment, often utilizing services like Amazon Simple Storage Service (S3), is becoming increasingly common.
Data Warehouses and Data Lakes:
Data warehousing, traditionally based on relational or columnar databases, gathers structured data from operational systems for analysis.
Data warehouses are essential for Business Intelligence (BI) querying and enterprise reporting on key performance indicators.
Data Integration:
Extract, Transform, Load (ETL) is a widely used data integration technique, pulling data from source systems, transforming it, and loading it into a target system.
Data integration platforms also support other methods, such as Extract, Load, and Transform (ELT), suitable for data lakes and big data systems.
Data Governance, Data Quality, and MDM (Master Data Management):
Data governance is primarily an organizational process, supported by optional software products.
Data stewardship involves overseeing data sets and ensuring compliance with approved data policies.
Data Modeling:
Data modelers create conceptual, logical, and physical data models that visually document data sets, and workflows, and map them to business requirements.
Techniques include entity relationship diagrams, data mappings, and schemas, with regular updates to accommodate new data sources or changing information needs.
BIG DATA ANALYTICS PLATFORMS TO KNOW - Data Platforms
A data platform serves as an integrated technology solution, facilitating the governance, access, and delivery of data from databases for strategic business purposes. It encompasses a complete solution for ingesting, processing, analyzing, and presenting data generated by the systems of a modern digital organization.
Here are some notable Data Platforms:
Microsoft Azure:
Microsoft Azure is a comprehensive public cloud computing platform, offering a range of services such as computing, analytics, storage, and networking. Users can select services to develop and scale applications in the public cloud.
Cloudera:
Cloudera provides an enterprise data cloud built on open-source technology. Its platform utilizes analytics and machine learning for insights, working seamlessly across hybrid, multi-cloud, and on-premises architectures.
Sisense:
Sisense Fusion is an AI-driven embedded analytics platform that enhances customer experiences and transforms businesses. It infuses intelligence into workflows, processes, and applications.
Collibra:
Collibra Software is an enterprise-oriented data governance platform for data management and stewardship. It empowers businesses to derive meaning from data, fostering collaboration between business users and IT.
Tableau:
Tableau is a rapidly growing data visualization tool in the Business Intelligence Industry. It simplifies raw data into easily understandable formats, allowing professionals at all levels to create customized dashboards.
MapR:
MapR Technologies provides a distributed data platform for AI and analytics, enabling enterprises to apply data modeling to enhance revenue, reduce costs, and mitigate risks. It processes high-scale and mission-critical data across various channels and deployments.
Oracle:
Oracle big data services assist data professionals in managing, cataloging, and processing raw data. The offering includes object storage, Hadoop-based data lakes, Spark for processing, and analysis through Oracle Cloud SQL or preferred analytical tools.
MongoDB:
MongoDB is a powerful open-source database with a document-oriented data model and a non-structured query language. MongoDB Atlas, a cloud database solution, offers fully managed deployment across AWS, Google Cloud, and Azure.
Datameer:
Datameer Professional is a SaaS big data analytics platform designed for department-specific deployments. It includes features for data preparation, discovery, and analysis, making it a comprehensive solution for big data analytics.
Data Storage Platforms
Data Storage is the accumulation of digital information, encompassing the bits and bytes that underlie applications, network protocols, documents, media, user preferences, and more. It is a fundamental component of big data.
Different types of Data Storage include:
Software Defined storage
Cloud Storage
Network Attached Storage
Object Storage
File Storage
Block Storage
Software Defined Storage
Software-defined storage is a storage architecture that decouples storage software from hardware, allowing it to operate on industry-standard or x86 systems. Unlike traditional NAS or SAN systems, SDS is designed for compatibility with various hardware, reducing dependence on proprietary solutions.
Pros: Flexibility on industry-standard hardware, reduced dependence on proprietary hardware.
Cons: Potential complexity in implementation.
Cloud Storage:
Cloud storage is a computing model that provides data storage as a service over the internet. It is managed by a cloud computing provider, offering on-demand storage capacity with the advantages of agility, global accessibility, and cost-effectiveness. Users access and manage their data without the need to maintain their storage infrastructure.
Pros: On-demand capacity, cost-effectiveness, global accessibility.
Cons: Dependency on internet connectivity, and potential security concerns.
Top Cloud Storage Platforms:
IDrive:
IDrive provides comprehensive online backup to the cloud for PCs, Macs, iPhones, Android devices, and other mobile devices, all consolidated into a single account with a cost-effective fee.
Google Drive:
Google Drive is a cloud-based storage solution that allows users to save files online, providing accessibility from any smartphone, tablet, or computer. It seamlessly integrates with various Google services.
NextCloud:
Nextcloud, an open-source software developed in 2016, enables users to run a personal cloud storage service. It boasts features comparable to other services such as Dropbox, offering flexibility and control over data.
pCloud:
pCloud serves as a personal cloud space for storing files and folders. With a user-friendly interface, it offers clear organization and accessibility across various devices and platforms, including iOS, Android, MacOSX, Windows OS, and Linux distributions.
Box:
Box is a cloud-based file storage and sharing service, that provides individuals and businesses with easy-to-use cloud storage solutions and collaboration tools. It supports efficient file management and collaborative workspaces.
Microsoft OneDrive:
OneDrive, Microsoft's cloud service, seamlessly connects users to all their files. It offers file storage, protection, and sharing capabilities across all devices, ensuring accessibility from anywhere.
SpiderOak One:
SpiderOak, a US-based online backup tool, facilitates backup, sharing, syncing, and access to stored data using an off-site server. It is accessible through applications on Windows, Mac, Linux, Android, N900 Maemo, and iOS platforms, offering versatile backup options.
iCloud:
iCloud securely stores photos, videos, documents, music, and apps, ensuring synchronization across all user devices. It facilitates easy sharing of various content, including photos, calendars, and locations, and aids in finding lost devices.
MEGA:
MEGA is a security-focused cloud storage service offering robust end-to-end encryption. It provides a generous free plan with ample storage. However, its history has been marked by controversy, and its zero-knowledge encryption can pose challenges for collaboration.
These cloud storage platforms cater to diverse user needs, offering a range of features, security measures, and collaboration tools. Users can choose based on preferences, requirements, and the level of security they prioritize.
Network Attached Storage
Network Attached Storage (NAS) is a storage device connected to a network, enabling authorized users and clients to store and retrieve data from a central location. NAS devices are flexible, and scalable, and provide a private cloud on-site, offering control and faster access at a lower cost compared to public cloud solutions.
Pros: Centralized storage, scalability, cost-effectiveness.
Cons: Limited performance for certain applications.
Object Storage
Object storage is a data storage strategy managing data as distinct units known as objects. Objects are stored in a single repository and are not embedded in files within folders. Object storage consolidates data pieces, and metadata, and assigns a unique identifier to create an efficient and scalable storage solution.
Pros: Efficient management of distinct data units, and scalability.
Cons: May not be optimal for transactional data, potential retrieval delays.
File Storage
File storage, also known as file-level or file-based storage, organizes and stores data on a computer hard drive or NAS device in a hierarchical structure. Data is stored in files, organized in folders, and structured under directories and subdirectories. File storage simplifies data retrieval using a path-based system.
Pros: Organized hierarchical structure, easy file location.
Cons: May face challenges with scalability and performance for large datasets.
Block Storage
Block storage is a storage scheme where each volume functions as a separate hard drive, configured by a storage administrator. Data is stored in fixed-size blocks, and each block is described by metadata containing a unique address. Block storage allows for efficient space utilization and is typically managed by the server operating system.
Pros: Efficient use of storage space, flexible configuration.
Cons: Requires careful management, which may lead to wasted storage space.
Data Management Risks and Challenges
The current business landscapes require all companies to provide secure Data Management Systems and Applications anytime and anywhere.
While providing requirements, Data management challenges arise as below:
Storing and utilizing accumulating volumes of data without crushing systems
Keeping databases running optimally to ensure applications perform productively and remain available
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 daily, leaving analysts with thousands of interlocking data sets.
Solution
There should be a Data System that automatically collects and organizes information. Manually it is possible but it's Time-Consuming. The Automated System will allow us to use the time in other acts.
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, the employee can't gain real-time insight into what is currently happening. outdated data can hurt Decision Making,
Solution
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.
Solution
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 a 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.
Solution
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, but it also 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 who need it. Decision-makers and risk managers need access to all of an organization’s data for insights into what is happening at any given moment, even if they are working off-site. Accessing information should be the easiest part of data analytics.
Solution
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, it is outdated.
Solution
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.
Solution
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.
Solution
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.
Solution
To overcome this HR problem, it’s important to illustrate how changes to analytics will 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.
Solution
Risk managers can secure a 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.
Solution
This challenge is mitigated in two ways: by addressing analytical competency in the hiring process and by 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.
Solution
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.
Conclusion
We've explored how to handle data with tools, storage methods, and dealing with challenges. It's clear that using the right tools and storage helps, but we also face challenges like dealing with lots of data and keeping things organized.
The good news is, when we manage data well, it brings many benefits. It makes our information better, saves time and money, and helps us make smarter decisions. While there are challenges, using the right technology and getting everyone on board can overcome them.
Understanding that handling data is a key part of our strategy, organizations continue on a journey to get the most out of their data in this digital age. It's an ongoing adventure to use data wisely and make it work for us.