Data Management – Significance and Challenges

Did you know that every person in the world created 1.7 MB per second in 2020, as per a DOMO report? That data has become an integral part of businesses today. Without data, it is impossible to succeed in the face of ruthless competition, as it forms the basis of making intelligent decisions.

However, not all of the data generated is beneficial for the business. The data created is way more than what companies need. Companies depend on efficient data management for the most relevant, clean, and reliable data. Data management solutions improve the available data's reliability, presentation, and relevance while facilitating quick decision-making.

In this article, we have discussed everything about data management, including its importance and challenges.

What is Data Management?

The data management process has four parts. These are collection, storage, organization, and protection of data. The primary purpose of doing these things is to analyze the data and make better business decisions. Since the data is getting created at an unprecedented rate, it has become more and more integral to manage it properly.

In other words, data management is the process of collecting, storing, securing, and using an organization’s data. While organizations have several different data sources today, they have to analyze and integrate the data to derive business intelligence for strategic planning. Data management includes all the policies, tools, and procedures that improve data usability within the bounds of laws and regulations.

The data management system ensures that the data available to businesses is safe, reliable, easily accessible, and accurate. These systems are typically managed by the companies' data management team and IT team. Data users are also a part of the process since they use it.

• Data management practices commonly include:
• Collection of data and regularly updating it in an accessible environment
• Data storage on multiple levels
• Giving businesses access to relevant data
• Ensuring the safety and security of collected data
• Archiving and destroying the data in line with compliance requirements

The data collected initially is unstructured and bad data, which is then cleaned and checked for its relevance. Only the relevant data and analysis based on them are presented to decision-makers.

Why is data management important?

Data is considered to be a valuable resource for modern organizations. With access to large volumes and different data types, organizations invest significantly in data storage and management infrastructure. They use data management systems to run business intelligence and data analytics operations more efficiently. We give some benefits of data management below.

Increase revenue and profit: Data analysis gives deeper insights into all aspects of a business. You can action these insights to optimize business operations and reduce costs. Data analysis can also predict the future impact of decisions, improving decision making and business planning. Hence, organizations experience significant revenue growth and profits by improving their data management techniques.

Reduce data inconsistency: A data silo is a collection of raw data within an organization that only one department or group can access. Data silos create inconsistencies that reduce the reliability of data analysis results. Data management solutions integrate data and create a centralized data view for improved collaboration between departments.

Meet regulatory compliance: Laws like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) give consumers control over their data. Individuals can seek legal recourse if they perceive that organizations:

·         Capture data without consent

·         Exercise poor control over data location and use

·         Store data in spite of erasure requests

Hence, organizations require a data management system that is fair, transparent, and confidential while still maintaining accuracy.

Types of data management:

There are several different types of data management. Some of the common ones include:

Master data management

Master data management ensures that the shared data of an organization is accurate, uniform, and consistent. It reduces duplication and improves the visibility of the stored data. It also drives revenue growth, productivity, and compliance and increases customer satisfaction.

Data security

Data security ensures your data is safe and protected from any cyber attack or malicious person. Data security specialists add security checks at all levels of data management and maintain access control.

Data governance

Data governance is the process of governing data in an organization. It lays down a framework consisting of rules, regulations, and policies for all data-related procedures. It also protects the data.

Data warehousing

Data warehousing is responsible for storing and analyzing raw data into easily understandable insights. It stores all types of data an organization collects, such as structured, unstructured, and semi-structured.

Data integration

Data integration combines data from different sources into a single view to give clarity to the users. It improves efficiency and helps businesses stay ahead of their competition.

Why is data management important?

There is no doubt that data is an organization’s most powerful asset. It creates an overall impact on the business running and can make or break it. Data is pertinent everywhere, from making sound business decisions to achieving higher customer satisfaction to deciding marketing campaigns. With the right data and data management system, businesses can grow at an unprecedented rate. But with the wrong one, the same business can make multiple mistakes.

Along with this, data management also ensures regulatory compliance. This increased reliance on data has also increased data privacy, security, and storage regulations. Data management software also comes with the means to keep up with these compliances and ensure companies don’t get into trouble with the law over data. 

Some of the other reasons why data management is important are:

Visibility

One of the reasons why companies need data management is because it ensures better visibility of the data. For example, let us assume that you have three product lines. You want to see the customer response to all three products separately. Without data management, you might get a convoluted and confusing view of the data of three product lines. But a data management software will monitor, analyze, and display data as per their sources and relevance. So the employees can straight away check what data they want to see.

This example is simple. As mentioned above, the data companies collect daily is substantial enough that it is impossible to use without data management. A robust data management system with real-time reporting will ensure greater data visibility.

Security

With great data comes great responsibility. Data security is at the heart of good data management. It is crucial to create a backup of your data. Data management systems provide access control to data. Employees at different levels receive access per their needs, requirements, and position.

Two ways of improving data security are

Backup and recovery: In case of hardware failure or any unforeseen incident that results in data loss, companies should keep a backup of their data. Backup also helps in maintaining historical records and facilitation comparisons. It also helps in ensuring regulatory compliance.

data encryption: data breaches and cyber crimes are at an all-time high. data encryption is encrypting data to protect against misuse or disclosure.

Decision making

The most crucial function of data is to facilitate better decision-making. Data management makes good decision-making possible by providing accurate and real-time data. These can be used to identify trends, track performance indicators, get customer feedback, and identify areas needing improvement. High-quality data provides more accurate analysis and helps make quick decisions in areas such as marketing campaigns, customer relationship management, etc.

Reliability

Data management minimizes the potential for errors through different processes and policies. Reliable data ensures accurate decision-making and quick adaptation to changes in the market. Companies can efficiently manage their clients and increase customer satisfaction. Increased customer satisfaction is the key to improving customer retention and customer acquisition. This can only be possible with up-to-date and accurate data availability.

Scalability

Data management also allows businesses to scale their data and keep it updated. It automates mundane and repeatable processes and ensures high performance through scalable data management. This also resolves the problem of data duplication and reduces the cost of data management while ensuring high operational efficiency.

Removes redundancy

Data management also removes redundancy by implementing a framework for data governance. It sets up data procedures and establishes a control framework. It also cleans up data automatically by eliminating duplication.

Data management systems also manage metadata by organising and creating metadata repositories to make it easily accessible. Several processes, such as data transformation and quality checks are automated to eliminate redundancy.

Improves operational efficiency

Data management improves operational efficiency by reducing employees' time searching for relevant data. Since almost all the processes are automated, making decisions and achieving tasks is quicker. Improved operational efficiency also contributes to lower costs and reduced employee workload.

Which business challenges does data management address?

Data management helps businesses survive and succeed in the competition. Without data management, a company cannot thrive, much less survive. Some of the challenges it addresses are:

Data quality

Poor quality data is unreliable, more inaccurate and incomplete. Using such data results in losses and drastically damages the company’s reputation. It can hinder the company’s growth by making strategies based on inaccurate data. Data management ensures data quality and accuracy and ensures that the company grows.

Unreliable data analysis

Since data is the first step towards effective decision-making, its poor-quality ruins the entire process. For example, data is collected from different sources and analysed to make it useful for executives. Executives design strategies based on the analysis and those strategies are implemented. But if the data analysed is inaccurate and contains irrelevant variables, the whole thing will fall apart. Proper organization and management of data are integral to good decision-making.

Data organization

Without structure and organised data, data collection is useless for organizations. Accessing relevant data is impossible if it is scattered and unstructured. Data management ensures data is organised at different levels. Thus, it would be easier to check its relevance and determine which data is beneficial and which is not.

Which challenges does data management need to provide solutions?

Business challenges continue to evolve every day. Therefore, it is crucial for data management also to evolve to address those challenges. Some of the rising challenges data management addresses are:

Increasing volume of data

The amount of data created every day is huge. Every department in a company needs relevant data for its processes. Even with a stellar data management system, it is easy to forget which data is available and how it can help. Data management solutions need to address the problem of increased data volumes.

Compliance

With cybercrime incidents rising daily, regulatory bodies are bringing in new laws and compliance requirements that ensure the safety of people’s privacy and data safety. organizations need to be on their tips and learn which data is suitable for use. they must ensure that personally identifiable infor mation is maintained and destroyed per government regulations.

Data is easily available, yet one of the most important assets of an organization. It can best utilise with an effective and excellent data management strategy. Identify your goals, ensure your data is of good quality, get stellar data security, and ensure access is controlled to make the most out of your data. An effective data management system can help you achieve your goals and increase your profitability significantly.

How can organizations get started with data management?

There may seem like a million and one pieces to planning a data management initiative, but don’t get bogged down in the weeds: Planning to integrate data management into your organization is just like any other business transformation project.First, make sure your data management initiative has a clear goal: To what end are you trying to organize your data? A business that wants to use data to make internal changes, for example, will have different data management needs than a company that wants to use its data to increase sales.Once you have a stated goal, it’s time to think about what will be needed to make it happen. If your data exists entirely as unstructured files and documents, you’re going to have a different starting point than an organization with large Hadoop databases filled with well-organized records.

Consider all the possible needs: Reassignment of employees, new hires, training, software platforms, budget, timeframe, the types of data already on hand, the kinds of data that are needed, and more. Having all these elements in mind will help you when you actually start planning in earnest. Next, it’s time to put your talent in place. Hire new employees, reassign those who are going to start working on your data management project, and get the team acquainted with your data management goals.Once your data management team is in place, it’s time to start the planning phase. Outside of how the team is going to accomplish its goals, this is when a data management platform is chosen, training can be undertaken, and the whole model starts to come together.After this, your data management team should be well on their way to building, testing and implementing a full data management model. When all of those prerequisites are in place and data management is an integrated part of your business, it’s time to start thinking about what comes next: How all of that well-organized data can help transform your organization, internally and externally.The entire process of building a data management system can take a long time, and even then, data management is just the groundwork for further use of big data. Data management isn’t an end in and of itself: It’s the house in which an organization’s data lives. It’s up to that organization to make use of the house it built by putting that data to work.