Data Management | 10 mins read

Dissecting Data Management

dissecting data management
Lauren Christiansen

By Lauren Christiansen

What is Data Management?

Data management is the process of generating and sustaining a foundation for collecting, storing, extracting, and saving information that is essential to an organization. It is the glue that connects each aspect of the data supply chain.

Data management works in conjunction with handling business processes by ensuring each choice made is based on the highest quality, most accurate information. This requires an analyst to monitor fluctuations and patterns in real-time.

Data management helps a business pinpoint and minimize weak areas and improve customer relationships. It offers a mechanism to measure data from every sector and application within the business, such as software functionality, sales performance, security policies, or product defects.

Data management provides a comprehensive overview of an organization so decision-makers can improve problem-solving and correct inefficiencies.

  • Public information at 29%
  • Internal information at 30%
  • Propriety - 15%
  • Confidential - 33%
  • Highly Confidential - 18%
  • Restricted - 25%

7 Types of Data Management

Data management specialists usually focus on 7 management best practices within their line of work. These cover every aspect of information and process management and ensure that data within an organization's central database is reliable, obtainable, and easily accessible.

Effectively managing data is critical to running the systems that maintain different business software. It is also responsible for providing access to in-depth insights that assist business executives in problem-solving and strategic planning. The 7 types of data management include

1. Master Data Management

Master data management exists to optimize the accuracy and quality of an organization's data resources. It ensures a business is regularly making choices based on evidence rather than guesswork.

Collecting information from various sources and compiling them into one centralized location requires expertise in master data management and an in-depth understanding of data mining management software.

2. Data Stewardship

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Data stewardship is not responsible for creating policies about managing information, but instead distributes and administers them across an organization. A steward watches over the entire process of data gathering to make certain best practices are carried out and policies are complied with.

Primarily, a data steward is accountable for implementing the policies of daily operational systems such as ingesting, storing, processing, and transferring information to other databases.

3. Data Quality Management

Quality managers combine data from different sources and then search for any intrinsic concerns such as duplicate information, irregularities, or inaccuracies. It requires expertise in quality management and an in-depth understanding of which information is most important for an organization's needs.

Its purpose is to achieve the desired business outcomes that require high-quality information. Data quality managers typically prioritize customer relationship management systems because having accurate client information is essential to creating a good customer experience.

4. Data Security

It is critical to ensure customer and company information is secure to prevent fraud, theft, and litigation. Security specialists are responsible for managing encryptions, averting hacking, and ensure information isn't moved or deleted by accident.

Though many security measures are implemented from the onset, a data security manager is responsible for regularly monitoring and updating protection mechanisms to ensure information remains secure.

5. Data Governance

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Data governance establishes the policies for an organization's handling of data. It serves as a foundation that clarifies procedures for the gathering, flow, and security of critical company information.

A data governor supervises a system of data stewards, quality managers, and other IT units to ensure everything functions properly and each unit carries out its requirements.

6. Big Data Management

Big data refers to the collection, analysis, and utilization of large quantities of data to optimize and streamline business processes.

It focuses on intaking the correct raw data, ensuring the data procedures are satisfactory and maintaining storage security measures. The information stored in a management platofrm and is utilized by other areas of the business to make better decisions and optimize problem-solving.

7. Data Warehousing

Data is the foundation for an organization, but enterprise leaders aren't always certain how to handle all of the large quantities of unsorted information at their disposal.

A data warehouse specialist supervises the physical and online data framework that is responsible for codifying, cleaning, and drilling down into raw data to generate valuable insights that help make better business decisions.

Benefits of Data Management

Data management assists with identifying and eliminating inefficiencies to optimize business processes, resolve inefficient workflows, and generate more profit.

It allows an organization to measure the large quantities of information they collect and ensure that all aggregated information is accurate and reliable. Once data is managed, it is sorted through to extract valuable insights and gain business intelligence. The benefits of carrying out this process include

1. Intelligent Marketing

Data management provides insight into customer behavior so organizations can curate their advertisements based on consumer needs. For example, grocery stores use collected customer information to pinpoint which products each customer tends to buy. They then send out coupons to those customers to maintain brand loyalty and enhance their experience.

2. Enhanced Security

Data management provides a comprehensive approach to securing data through encryption management and preventing unauthorized access. Effective data security eliminates any potential risks that result in financial loss or litigation. This can save an organization money and maintain its reputation by ensuring that sensitive consumer and company information is secure.

3. Aligned with Company Standards

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Depending on the industry, most organizations have a set of data regulations to comply with. For example, healthcare providers must follow HIPAA laws and do everything possible to mitigate data leaks or hacks. Through data stewardship, data security measures, and big data management, an organization can rest assured that its data policies comply with any industry or company-related requirements.

4. Optimized Machine Learning

Machine learning uses algorithms to pinpoint trends in large quantities of data. It is an aspect of effective data management and is required for businesses that mine from various data sources. Machine learning has become more sustainable and requires less power, which can save money for an organization. Learning capabilities have become more extensive as time evolves, allowing organizations to develop further insights into relationships between data sets.

5. Decreased Costs

Effective data management reduces overall costs by utilizing only the required amount of storage and resources to ensure quality standards are complied with. It also assists in pinpointing inefficiencies and operational problems, which can be eliminated before they become problematic. This helps to save money and ensure resources are allocated properly to increase the bottom line.

6. Better Customer Experience

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By drilling down into consumer behavior and patterns, it's easier to provide the services and products needed by the consumer. Businesses can curate advertisements that target their audience while ensuring each POS is secure and easy to use. This improves the overall customer experience and increases their brand loyalty, which generates more profits.

Data Management Challenges

Though data management provides several benefits to an organization, it doesn't come without its challenges. Because every data management platform is constantly evolving, information managers need to regularly reassess their processes to ensure they comply with new requirements.

Large quantities of collected data are cumbersome to sort through, cleanse, store, and analyze. To extract the highest quality of insights, data managers must overcome the following challenges

1. The Quantity of Data is Overwhelming

The amount of data collected by larger organizations is incomprehensible. Even smaller businesses have to sift through multiple data streams to find the correct information they need.

Data managers must take a comprehensive approach in handling different data sources while developing networks and processes. Finding third-party stakeholders who can assist in data integration helps to optimize this process and remove portions of the workload from the IT team.

2. Businesses May Silo Information

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Developers often perform work based on one set of data while other areas of the business use their own data sets. Effective data management requires access to every single piece of data in an organization to extract valuable insights that help make better business decisions. Outside platform services can assist in cleaning and sharing data between business units from a centralized database.

3. Raw Data to Valuable Data

Organizations collect raw data from different sources at different times, which can be chaotic and unstructured. Data cannot turn into valuable business intelligence until it is properly prepared and cleansed by a qualified specialist. Often referred to as ETL (Extract, Transform Load), data managers must employ an outside party or utilize internal tools to ensure raw data turns into valuable data.

4. Understanding Why Data is Needed

Many businesses are proud of their data collection efforts but aren't gaining the valuable insights that help create business intelligence.

Understanding why data is useful and knowing which information to collect and store requires knowing exactly what the desired outcomes are. Does the organization want more money? Is it looking to target a different market? Knowing the answers to these questions assists in developing the correct data management policies that generate value for the company.

3 Best Practices for Data Management

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Though data needs vary depending on the company and industry requirements, certain practices apply across the board. Implementing the following strategies will ensure the proper data is collected, stored, cleansed, and monitored throughout its lifecycle. By utilizing a data management strategy, businesses can use their information to their advantage and make better business decisions.

Best practices include -

1. Create a Strategy

The IT team and any other involved participants must write a data management plan to understand how much data will be used, how it is accessed, and who owns it. This information should be referenced and adjusted when necessary. DMPs can also be given to investors and other stakeholders who need to understand how the company plans to manage data within its market. Effective DMPs include the following

  • Favored format of files
  • How applications are named
  • Authorized access parameters
  • The process to back up and archive data
  • Third-party participants and their requirements
  • Recording of all policies and procedures

2. Store Information

Implementing an effective data storage plan is critical to managing data properly. Data managers should determine whether the organization requires a data warehouse or if all information will be uploaded into the cloud. The procedures for identifying and listing folders, catalogs, and users should be clearly outlined to serve as a framework for all of the organization's parameters.

This will lay the foundation for discerning the way future information is stored and how inaccuracies or duplicate data will be handled. It's also critical to outline specific security measures to prevent hacks and make certain sensitive information is accessible to authorized individuals. Everything must be documented so it can be easily accessed in the event individuals outside IT need to review these procedures.

3. Share Information

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After storage and security strategies are documented, data managers should share this information with applicable stakeholders. Before individuals can access sensitive data, the following questions must be addressed-

  • Who claims ownership over all of the information?
  • Can the information be replicated?
  • Are all the data owners comfortable sharing this information with others?
  • Which individuals can access information and when?
  • Are there any compliance concerns? If so, what are they?
Once these questions are answered, data managers can begin sharing the information with the necessary individuals by implementing an infrastructure for software solutions and other business intelligence technologies.

  • 48% of company employees have access to too much company data
  • 80% of companies don't know where their data is located or how it moves across various networks
  • 12% of businesses say their employees have access to all company data, even if it is sensitive

Key Takeaways

In conclusion, here is what to know about data management

  • The 7 types of data management include master data management, data stewardship, data quality management, data security, data governance, big data management, and data warehousing.
  • Benefits of data management include intelligent marketing, enhanced security, alignment with company standards, optimized machine learning, reduced costs, and better customer relationships.
  • The top data management challenges include handling large quantities of data, information silos, difficulty processing raw data into valuable information and ensuring people know how to use the information collected.
  • Best practices for data management include creating a strategy and DMP, storing information, and sharing the data with the correct people.