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A Data Management Primer For Beginners

Mike Lampa

Table of Contents:
  1. What is Data Management?
  2. The Importance of Data Management
  3. Types of Data Management
  4. Data Warehouses
  5. Data Governance
  6. Data Architecture and Modeling
  7. Data Quality Management


“The ability to take data – to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it – is going to be a hugely important skill in the next decades.” 

Hal Varian, Chief Economist, Google

Every company generates a significant amount of data about its product lines, facilities, procedures, operations, Key Performance Indicators (KPIs), and a multitude of other metrics specific to their business. Data is key in many procedures and practices in today’s technological and economic world, and the ability to control this data is a critical part of the entire system.

Data Management is the act of keeping track of an organization’s data and making sure that it is precise and easily accessible to those who require it. While Data Management may seem to be a minor task, it can have a significant impact on how a company operates on a daily basis.

The organization must implement various pieces of Data Management Software (DMS) such as Enterprise Resource Planning (ERP), Business Intelligence (BI), Customer Relationship Management (CRM), Marketing Automation,  etc.

What is Data Management?

Data Management is the ongoing practice of using all available, gathered, organized, and saved company data by performing analysis to gain insight into business performance. Data Security is a key component of Data Management and should be a primary focus all throughout the process, especially as data concerns about privacy and ransomware attacks are becoming more common.

As companies rely more on data-driven insights and decisions, it is essential to employ proper Data Management Solutions to make sense of the data available. The most sophisticated Tools available today ensure that dependable, updated data is constantly used to guide decisions.

The software helps with everything from data pre-processing to cataloging, search, and governance, and allows users to effortlessly retrieve the information they require for analysis.

Business applications and databases differ in size, so each company should approach their Data Management practice differently while keeping the specifics of their particular technology ecosystem in mind. Now you know it is, let’s have a look at why it is important.

The Importance of Data Management

Data Management is among the world’s fastest-growing industry segments, with IT leaders commonly declaring it their top priority in corporate settings over the past 5 years. It is a crucial first step to employing effective data analysis at scale, which leads to important insights that can add great value to your customers and drastically improve your bottom line. People across an organization can locate and access dependable data for their questions with effective Solution.

Data Management can boost the visibility of your organization’s data assets, making it easier for people to find the right data quickly and with confidence. It also aids in reducing possible mistakes by defining policies and procedures for data consumption and fostering trust in the data being used to make choices throughout your organization.

Data Management uses verification and encryption tools to safeguard your organization and employees from data losses, thefts, and breaches. Strong Data Security ensures that vital company information is saved and retrievable in the event that the primary source fails.

Furthermore, if your data contains any personally identifiable details (and it often does) proper data security processes can help your company meet consumer protection laws, which is an extremely important aspect of doing business today.

When a company employs poor Data Management practices, it is vulnerable to making poor decisions based on misleading or inaccurate information. This can result in subpar services or products and, eventually, the failure of their business plan.

Data Management Software uses and handles data from multiple sources. After the data has been extracted, cleaned, converted, and combined, it can be accessed intuitively without having to sacrifice trustworthiness.

It enables organizations to successfully scale data and usage events by leveraging repeatable methods to keep metadata and information updated. When procedures are simple to replicate, your organization can avoid the extra expenses of duplication, such as employees conducting the same research multiple times or re-running expensive queries.

Types of Data Management

Data Management allows for the acquisition, handling, storage, and data retrieval in order to gain insight into operations, help decision-making, and maintain a competitive edge. The various types of Data Management are listed below.

Four Types of Data Management

 1. Data Warehouses

A Data Warehouse Management System (DWMS) enables you to store and collect data from a database for use in business analytics. These are programs that generate storage areas for your data. They are typically connected to existing hardware within your company and do not encompass management processes that are specialized based on the data type. They are storage tools, but they do not provide data structure or intelligent analysis.

2. Data Governance

Data Governance seeks to ensure that all business-critical data is accessible and functional for authorized users via a system of internal rules and processes that help maintain data security and integrity. Data Security refers to a series of internal policies that help prevent unauthorized access, adjustment, or destruction of essential data.

3. Data Architecture and Modeling

Data Architecture and Modeling refers to a set of guidelines and standards that are employed to structure, store, and access business-critical information throughout the organization. The method of adapting your data to the layout required for its storage in the company’s database is referred to as Data Modeling. This category of Data Management tools allows users to develop conceptual models and set the guidelines for quality and reliability.

4. Data Quality Management

Data Quality Management is a collection of practices and methods for ensuring that business-critical data is accurate, complete, and appropriate for its destined usage. This focus on data quality happens via a search for data duplications and errors in an ongoing review of all a company’s collected data.

To Conclude

Big Data has ramifications for almost every business today. While the concept of a Data Management Tool may appear overwhelming initially, there are tools available on the market that are personalized to any given company background. There are scalable solutions available to satisfy the data requirements of small, medium, and big corporations.

Proper Data Management is a key step in maintaining overall data health and getting the most value from your data. A centralized and automated database must be an underlying focus of the business, no matter how new or established. As technology continues to advance, it lessens the need for highly specialized resources to enable these tools.

Depending on your company’s requirements, you can enjoy all of the features you need from a unified platform.

If you have any questions or need assistance getting your Data Management toolkit set up, we welcome the opportunity to discuss this with you. Please contact us to start the conversation.