What is Data Management? 2024 Review

The process of obtaining, storing, and utilizing data is known as data management, and it is frequently made easier by data management software. It enables you to be aware of the data you possess, its location, its owner, its users, and the methods by which it is accessed. Organizations may implement vital systems and apps securely and affordably and make strategic decisions with the help of data management. Enterprise Data Management (EDM) is a specific field that falls under the larger umbrella of data management. EDM is the process of managing and inventorying a company’s data and making sure that the organization follows this procedure.

The goal of the data management process is to ensure that the data in business systems is correct, readily available, and easily accessible. It involves a variety of distinct functions. Business users usually engage in some aspect of the process to make sure the data fulfills their needs and to get their support for the regulations controlling its usage, but IT and data management teams do the majority of the necessary work.

Data is becoming more and more viewed as a corporate asset that can be utilized to increase revenue and profits by enhancing marketing efforts, optimizing business processes, cutting expenses, and making better-informed business decisions. However, improper data management can leave companies with unworkable data silos, inconsistent data sets, and issues with data quality that hinder the use of business intelligence (BI) and analytics applications and, worse, produce inaccurate conclusions.

Due to the growing amount of regulatory compliance requirements that businesses must comply with, including data privacy and protection legislation like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR), data management has also become more and more important. Furthermore, businesses are collecting an increasing amount of data and a diverse range of data kinds, which are characteristics of the big data platforms that many have implemented. These kinds of environments can become cumbersome and challenging to use without effective data management.

What is Data Management?

In order to improve business outcomes, data management involves absorbing, analyzing, safeguarding, and storing an organization’s data and using it for strategic decision-making. The past ten years have seen advances in edge computing, hybrid cloud, artificial intelligence, and the Internet of Things (IoT) that have caused big data to increase exponentially and become increasingly difficult for businesses to handle. Consequently, the discipline of data management has gained substantial importance inside a company as a result of the issues this expansion has produced, including security threats, data silos, and general decision-making bottlenecks.

Teams use a variety of data management technologies to clean, unify, and secure data in order to tackle these issues head-on. Leaders are then able to make well-informed business decisions by using dashboards and other data visualization tools to extract insights. Additionally, it gives data science teams the ability to look into more complicated issues and use more sophisticated analytical tools, like machine learning, for proof-of-concept projects. If they are effective in meeting and surpassing business objectives, they can collaborate with pertinent departments to use automation techniques to spread those insights throughout their company.

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Data Management vs. Master Data Management

Master data management, which concentrates on transactional data, such as sales records, is more narrowly focused than data management as a whole. Information on customers, sellers, and products is commonly included in sales statistics. Businesses can identify their most lucrative customers as well as their most successful items and marketplaces with the use of this kind of data. Personally identifiable information (PII) is included in master data, which complies with more stringent laws like the GDPR.

Benefits of Data Management

Companies who implement and maintain data management programs gain a lot of advantages:

1. Reduced Data Silos

Most, if not all, companies experience data silos within their organization. Data silos and reliance on data owners are reduced by using various data management frameworks and tools, such as data lakes and fabrics. Data fabrics, for example, can help identify possible integrations across different datasets from different departments, including sales, marketing, and human resources. Conversely, data lakes absorb unprocessed data from the same services, eliminating singular owners and dependencies within a particular dataset.

2. Enhanced Security and Compliance

Governance councils help set barriers to shield companies from penalties and bad press that can result from breaking laws and rules set forth by the government. Errors in this area can have serious consequences for your brand and finances.

3. Improved Customer Experience

Although this benefit won’t be seen right away, a successful proof of concept can help teams better understand and tailor the customer journey by doing more comprehensive studies.

4. Scalability

Businesses can benefit from data management as they grow, but this mainly depends on the technology and procedures already in place. Cloud solutions, for instance, provide data owners with greater flexibility by letting them scale up or down compute power as needed. Furthermore, as a corporation expands in size, governance councils can assist in making sure that stated taxonomies are implemented.

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Types of Data Management

The discipline of data management has a wide range of applications, and in order to optimize strategy and operations across an organization, a good data management plan usually incorporates the following elements:

1. Data Processing

In the data processing stage of the data management lifecycle, unprocessed data is gathered from various sources, including web APIs, mobile applications, Internet of Things (IoT) devices, forms, surveys, and more. Next, it is typically processed or loaded using data integration techniques, like extract, transform, load (ETL) or extract, load, transform (ELT). While ETL has been the conventional approach to combining and organizing data from disparate datasets, ELT has gained traction with the rise of cloud data platforms and the growing need for real-time data.

During the data processing stage, the data is typically filtered, merged, or aggregated to fit the criteria for its intended purpose, which can range from a business intelligence dashboard to a predictive machine learning algorithm, regardless of the data integration technique employed.

2. Data storage

Although data can be kept both before and after processing, the kind of data and its intended use will typically determine which storage repository is used. To satisfy particular data analytics requirements for data outputs, such as dashboards, data visualizations, and other business intelligence tasks, data warehousing, for instance, needs a defined schema. Business users typically work with data engineers to drive and document these data requirements, which the data engineers will then execute against the specified data model.

A data warehouse’s fundamental organization is usually designed as a relational system, obtaining its data from transactional databases and presenting it in a structured data format. On the other hand, certain storage systems—like data lakes—combine information from relational and non-relational sources, acting as a testing ground for creative data initiatives. Because they enable the integration of both structured and unstructured data into their projects, data lakes are very helpful to data scientists.

3. Data Governance

Data governance is a set of standards and business processes which ensure that data assets are leveraged effectively within an organization. This often include procedures for data security, usability, quality, and access. To guarantee that metadata is added uniformly across different data sources, data governance councils, for example, usually agree on taxonomies. It would be beneficial to better document this taxonomy using a data catalog in order to increase user accessibility and promote data democratization among businesses. Teams responsible for data governance also assist in defining roles and duties to guarantee that access to data is granted appropriately; maintaining data privacy is especially dependent on this.

4. Data Security

Data security sets guardrails in place to protect digital information from unauthorized access, corruption, or theft. Modern firms’ security procedures are under closer scrutiny as digital technology permeates more aspects of our lives in an effort to safeguard consumer data from fraudsters and disaster recovery mishaps. Any organization can suffer greatly from data loss, but data breaches in particular can have expensive effects on both the company’s finances and reputation. By using encryption and data masking in their data security plan, data security teams can improve the security of their data.

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Data Management Tools and Techniques

A vast array of technology, instruments, and methodologies can be utilized in the data management procedure. The following choices can be made for various data management needs.

Database Management Systems

A relational database management system is the most common type of database management system. Tables with rows and columns containing database entries are how data is arranged in relational databases. Primary and foreign keys can be used to connect related records in various tables, preventing the need for repeated data entry. The foundation of relational databases is the SQL programming language and a strict data architecture that works best with organized transaction data. They are the best database option for transaction processing applications because of this as well as their support for the ACID transaction attributes, which are atomicity, consistency, isolation, and durability.

Big Data Management

Because NoSQL databases can store and handle a variety of data types, they are frequently utilized in big data deployments. Open source technologies like Hadoop, which is a distributed processing framework with a file system operating across clusters of commodity servers, its companion database HBase, the Spark processing engine, and the Kafka, Flink, and Storm stream processing platforms are also frequently used in the construction of big data environments. Cloud-based big data systems are being implemented more often, and object storage like Amazon Simple Storage Service (S3) is being used for this purpose.

Data Warehouses and Data Lakes

Data lakes and data warehouses are the two repositories that are most frequently used to manage analytics data. The more conventional approach, known as a data warehouse, usually starts with a relational or columnar database and houses structured data that has been aggregated from various operational systems and ready for analysis. The two main use cases for data warehouses are enterprise reporting and business intelligence querying, which let executives and business analysts examine KPIs like sales and inventory control.

Data from all of an organization’s business systems are included in an enterprise data warehouse. Within large corporations, autonomous business divisions and subsidiaries may construct their own data warehouses. Another alternative for warehousing is a data mart, which is a scaled-down version of a data warehouse that holds portions of an organization’s data for particular departments or user groups. One deployment strategy uses a pre-existing data warehouse to build various data marts, while another builds the data marts first, then uses them to fill a data warehouse.

Data Integration

Extract, transform, and load (ETL) is the most popular method of data integration. It involves obtaining data from source systems, transforming it into a standard format, and then loading the integrated data into a target system, such as a data warehouse. Platforms for data integration, however, now support a wide range of additional integration techniques. This includes extract, load, and transform (ELT), an ETL variant that loads data into the target platform without altering it. In data lakes and other large data systems, ELT is a popular option for data integration.

Data Modeling

Data modelers bridge business needs for transaction processing and analytics to visual documentation of data sets and workflows through the creation of a variety of conceptual, logical, and physical data models. The creation of entity connection diagrams, data mappings, and schemas in various model types are common methods for modeling data. When new data sources are introduced or an organization’s information demands change, data models frequently need to be changed.

Data Governance, Data Quality and MDM

Although software products are available to assist in managing data governance initiatives, they are really an optional component of data governance, which is largely an organizational activity. Data management experts may oversee governance initiatives, but they typically involve a data governance council comprised of business executives who decide on corporate guidelines for data creation, presentation, and use as well as common data definitions.

Data stewardship, or monitoring data sets and making sure end users follow authorized data policies, is another essential component of governance activities. Depending on the organization’s size and the extent of its governance program, the role of data steward may be full- or part-time. Both the IT department and business operations can produce data stewards; in either case, a thorough understanding of the data under their supervision is typically required.

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Data Management Best Practices

Here are some best practices to assist an organization’s data management procedure stay on course.

Make Data Governance and Data Quality Top Priorities: Effective data management techniques require a robust data governance program, particularly in firms with distributed data environments that comprise a variety of platforms. Prioritizing data quality is also essential. However, data management and IT teams cannot succeed in either situation on their own. To ensure that their data demands are satisfied and issues with data quality are not repeated, business executives and users must be involved. Projects involving data modeling are no different.
Be Smart About Deploying Data Management Platforms: Designing an architecture and assessing and choosing technologies demands careful consideration because there are so many databases and other data platforms available. IT and data managers need to make sure the data management systems they put in place are appropriate for the job at hand and will provide the analytics and data processing power needed for an organization’s daily operations.
Be Sure you Can Meet Business and User Needs, Now and in the Future: Data environments are dynamic—new data sources are added, current data sets are updated, and the needs of businesses for data vary over time. Data management needs to be flexible enough to change with the times. For instance, to make sure that data pipelines are continuously updated and contain all necessary data, data teams must collaborate closely with end users during pipeline construction. A DataOps methodology, which combines lean manufacturing, agile software development, and DevOps, is a collaborative way to building data systems and pipelines. It could be helpful. Data managers and users collaborate through DataOps to streamline processes, enhance communication, and hasten the supply of data.

Notable Data Management Trends

Here are some notable data management trends worth knowing about:

1. Cloud Data Management Technologies are Becoming Pervasive

According to Gartner’s projection, in 2022, cloud databases will generate half of all DBMS sales. Organizations are “moving rapidly” to use developing data management solutions in the cloud, according to the Hype Cycle research. Hybrid cloud architectures, such as hybrid data warehouse environments, which blend cloud and on-premises systems, offer another choice for businesses that aren’t quite ready to make the full transition.

2. Augmented Data Management Capabilities also Aim to Help Streamline Processes

In order to automate repetitive processes, identify problems, and provide solutions, software suppliers are expanding the capability for data quality, database management, data integration, and data cataloging. These features leverage AI and machine learning technology.

3. The Growth of Edge Computing is Creating New Data Management Needs

Some manufacturers are also creating edge data management capabilities for endpoint devices, as businesses employ IoT devices and remote sensors more frequently to gather and analyze data as part of edge computing environments.

Data Management Challenges

Only when data is able to be processed, stored, and used will it be considered valuable. Making the most of your data is rewarding but difficult. Enterprises are becoming more and more reliant on data, yet there are obstacles to overcome.

Volume: It’s easy to lose track of what you have and where it is because your data is coming in at larger sizes and in more forms.

synchronization and Integration of Data: It gets more difficult to effectively and strategically combine data from various sources as it gets more sophisticated.

Silos: Unintegrated data cannot cooperate, resulting in lost opportunities and resource waste.

Storing and Processing Data: To have the biggest impact, IT teams must decide where data should go and how to process it.

Costs: Whether data is managed on-site or in the cloud, processing and storing it incurs additional costs. It’s critical to consider the worth of your data as well as business objectives while evaluating these expenses.

Observance: Failure to adhere to industry and data privacy requirements may lead to penalties, compromised data, revocation of accreditation, or other negative effects on your company.

Data Gravity: Applications and services might be attracted to data based on its mass. Over time, moving large datasets and the components they draw becomes more difficult.

Conclusion

The establishment of a unified information source for decision-making within the company is contingent upon an efficacious strategy to data management. Siloed data is a typical problem in many larger firms; managers need organizational intelligence to make choices, but it can take weeks to gather this information for analysis. There is no shared version of the truth when data is siloed. Because conversations are centered on each person’s interpretation of the data rather than the issue or its solution, decision makers find it difficult to come to a consensus on crucial problems.

Data silos are not just a technological issue; they are also an organizational one. By centralizing the administration and management of data, a cross-functional data management team may foster understanding and consensus while gaining insight into the organization’s data concerns. The group can assist standardize the data so that it may be used by many systems and users without concern for its accuracy or origin by utilizing contemporary data management tools.

Organizations can also benefit from data management by using vast volumes of raw data to extract important information. Organizations nowadays are inundated with a firehose of data due to the sheer number of devices and applications available. You can filter data early in the process by understanding which data has the greatest value with the aid of data management tools and strategy. By doing this, the data may be processed and analyzed at a smaller, more manageable scale.

These days, data is seen as an important resource for businesses. Organizations make huge investments in data storage and management infrastructure because they have access to a wide variety of data types in large volumes. To conduct business intelligence and data analytics activities more effectively, they make use of data management solutions.