The Latest Guide to Data Management Solutions and Why You Need Them

What is data management solution?

Today, organizations are generating and using data at unprecedented rates. As per a report, nowadays organizations generate more than 2.5 quintillion bytes of information every day, therefore managing data has become essential. Statistics also show businesses that manage their data properly have see 12% increase in their revenue.
The practice of collating, organizing, storing and protecting data for future data analysis is called data management and organizations need good data management solutions to make sure they have reliable and up to data to make data driven decisions.
A good data management solution should provide an efficient way to manage diverse data and collate it in an unified data tier. Most of the data management software provide features that will manage your data and help you with data preparation, data cataloguing and governance to allow people to find the information they need quickly.

The benefits of data management solutions

A Data Management solution helps process and manage modern data and also provides new business opportunities. Management systems can be defined as a set of tools which access and process a collection of related data and have proper measures in place to secure the data. A good management solution will provide the following advantages to any organization.

• Better data transfer- When you have a management system in place, users get access to better-managed data in one place, making it possible to respond quickly to changes.
• Better security- A good data management platform will have features to prevent any data security breaches and save data from hackers or malicious attacks.
• Reduced inconsistency- If data files have different versions and are stored in different places, there will be inconsistency. A software solution will make sure that data is deduplicated and all data is stored in one place, hence reducing incidents of inconsistencies.
• Faster access- The system helps users respond quicker and more accurately to any changes as they can access data faster.
• Better decision making- Due to quicker access to data, users are able to provide better quality information, which leads to better decision making.
• Increased productivity- The data available in the management system, along with the tools to transform it into useful information, helps users to make better decisions and also increase their productivity.
• Simple- The data management solution will give you a clear overview of the data. It also makes operations such or creating or deleting files easy to implement.

How does data management solution works?

Management solutions combine unstructured data from multiple data sources and clean, transform and integrate that data without changing its integrity, so end users can access it in an easy to use format. The management tools are used by an organization to refine data as per business requirements. One use of a management solution is pulling data from in-house and third parties to create customer profiles for targeted marketing and ad campaigns. A management solution is a bridge between data sources and content delivery.
A data platform gives you control over data so you can identify if there are any alterations in the data and also allows you to create alerts to be notified of any changes. The management platform lets an organization deploy processes to access, integrate and store data to minimize any risks. They also automate repeat tasks like job scheduling to streamline workflow and optimize the management process. Most management tools have a user friendly and single interface so that anyone can use them. The solution should allow easy integration to simplify data management.

Data management solutions currently in vogue

Data Management is a process which receives, validates and refines data to ensure high quality data for users. Most data management solutions have a range of functions such as data storage, analysis, distribution, and synchronization of data and can be used for a variety of use cases. The solutions can be broadly classified into the following categories-

• Cloud data management solutions, which are created for use on cloud, can be connected to and integrated with multiple data sources.
• Data integration solutions help define data transformation and load data from multiple sources into a database or data warehouse.
• Data analytics and visualization solutions are used to help organizations visualize, analyze and explore big data and provide reports to guide business decision making.

1 . Database management

Database management are the steps taken by an organization to control enterprise data to meet required conditions through the data lifecycle. As the volume of business data grows, database management becomes crucial as rapid growth poses a lot of problems such as poor application and risk of compliance.
Database management is a task that protects the data, keeps applications at their peak performance and reduces the risk of non compliance. Managing a database includes tuning and monitoring performance, planning for data storage, backup and recovery, data archiving, data partitioning and retirement.
Controlling data through its lifecycle can reduce the occurrence of events that comprise data efficiency and can boost data integration for better business intelligence.

2. Master data management

Master data is the core enterprise data that describes the objectives around which the business is operated. The data is not transactional in nature, rarely changes and includes reference data crucial to operating a business. Most businesses will define master data as a commonly agreed upon item list which has information on products, customers, employees, location and assets.
Master Data Management ensures uniformity, accuracy, stewardship, consistency and accountability of the master data assets. The management software consolidates data from multiple sources into a standard format to provide users with a single view for use in all applicable systems. This enables organizations to delete duplicate records with mismatched data, giving users a complete picture.

3. Data quality

As more businesses opt for a data driven decision making process, the quality of data being collected, collated, stored and used will decide the success of the decisions. Bad data quality leads to a lack of actionable knowledge and improper application, which can result in risky outcomes. Data quality has two main definitions- high quality data, which is fit for the intended purpose and high quality data which accurately represents real-world situations it defines. And you need to find the right balance between the two definitions. Most bad quality data is due to human errors (59%). The only way to ensure high data quality is to check for these five traits accuracy, reliability, completeness, relevance, and timeliness.

4. Data integration

A business receives data from a lot of sources, both internal and external. There is a need to consolidate this data and the process of combining data from multiple sources into a single view is called data integration. The process of data integration of data across the organization involves cleaning the data, mapping it, and then transforming it. Data integration enables data analytics tools to provide effective business intelligence. Most integration processes have some common elements like a network of data sources, a master server for storing data and clients who access that data. The client will send a request for data, the master server will extract data from various sources and consolidate it in a single data set and send it to the client.

5. Data governance

As the use of data grows, so does the risk of data breaches. Organizations need to adopt data governance to protect their data. Data governance is when you manage the availability, usability, security and integrity of the enterprise data as per the company's policies and procedures. Effective data governance makes sure that the data is reliable, consistent and cannot be misused. A well managed data governance framework-

• Ensures top management gets an overview of data assets, their value and impact on business operations and market.
• Safeguards accurate and consistent financial reporting.
• Provides insights into customer preferences and behavior.
• Supports cost reduction and operational efficiency initiatives, and
• Meets the regulations for legal compliance.

6. Data virtualization

In an organization, there are many employees who need to access data and the company needs an approach that allows for retrieval and manipulation of data without need for technical details. This process is known as data virtualization and the Data Management software acts as a nexus between diverse data sources to collate decision making data in one place to enable data analytics. Data virtualization gives organizations a modern data layer that allows a user to access, transform and deliver data faster and with reduced costs. The process, along with data governance, ensures high quality, secure and consistent data which is easy to find, understand and use. Data virtualization ensures data is agile and can be modified as required and has a high performance runtime.

7. Data warehousing

The best way to ensure quick access to data is to collate data and store it at one place like a data warehouse. Data warehousing is a system that enables and support business intelligence activities, especially data analytics. Data warehousing is used for consolidating and centralizing large amounts of data from diverse sources and allows organizations to derive valuable insights to improve decision making. A data warehouse is often called an organization's single source of truth'. Data warehousing allows organizations to analyze data, gain value from it and keep a record. Data warehousing allows analysis of data about a particular area as it creates consistency in data from multiple sources. A data warehouse delivers high output and provide flexibility to manipulate data.