The Ultimate Guide to Enterprise Data Management

This guide provides an overview of key elements of enterprise data management (EDM), including integration, cataloging & preparation; policies & regulations; security & governance.

The Ultimate Guide to Enterprise Data Management

Data management is a critical component of any successful business. It involves the collection, storage, and analysis of data to help organizations make informed decisions. Enterprise data management (EDM) is the process of managing data across an organization, from its collection to its storage and analysis. This guide will provide an overview of the key elements of EDM, including data integration, data cataloging, data preparation, data policies and regulations, data security, and data governance. Data Integration: Enterprise data integration is the process of moving and consolidating a company's diverse data into one accessible place.

The first step in the data management journey is to complete a comprehensive data audit. A data management leader should list or graph all the data produced, used, and eliminated in a business process. This type of cataloging project is essential for gaining an overview of the data. It's important to make sure that everything is cataloged as completely as possible, including emails and notes. Once the data has been cataloged, it needs to be cleaned and transformed into a standard format.

Unfortunately, projects such as cataloging and data preparation can be challenging, intensive, and complex. But once these projects are completed, you're much closer to successful data management. You can't just create data policies and government regulations; in the end, you'll still be sitting on piles of data. Data integration brings together different forms of data in one place for the purpose of making them accessible. An organization needs to consolidate the data it receives from a variety of sources and make it accessible to all departments in a variety of formats.

It involves introducing and implementing policies, methods, and technologies to ensure data integrity. It also involves making sure that data use, storage, and integration practices comply with international standards such as GDPR, PCI DSS, etc. With cybercriminals regularly testing the integrity of data security in companies around the world, it's essential to protect customer data. Jeopardizing PII (Personally Identifiable Information) can be a death sentence for a growing data-driven company. It's best to stay ahead of a potential data breach by putting security measures in place early. At this point, you need a dedicated team to help implement data strategies, introduce regulations and policies related to data, ensure data integration, and ensure your security.

The modern and cost-effective way to address this challenge is to leverage an on-demand data director.

Components of Enterprise Data Management

There are several components of enterprise data management; we'll discuss each in more detail below:
  • Data Cataloging: Cataloging is the process of creating an inventory of all the company's available datasets. It's important to keep track of all datasets because if they are not monitored regularly, their validity decreases considerably and they become useless for your company.
  • Data Integration: Data integration brings together different forms of data in one place for the purpose of making them accessible. An organization needs to consolidate the data it receives from a variety of sources and make it accessible to all departments in a variety of formats.
  • Data Preparation: Data preparation involves cleaning up datasets so that they can be used for analysis. This includes removing duplicate entries or correcting errors in the dataset.
  • Data Policies & Regulations: Organizations need to create policies and regulations related to their use of customer or employee data.

    These policies should include guidelines for how customer or employee information is collected, stored, used, shared, or destroyed.

  • Data Security: Organizations need to ensure that their customer or employee information is secure from unauthorized access or misuse. This includes implementing measures such as encryption or two-factor authentication.
  • Data Governance: Data governance involves creating a structure for external and internal accountability when it comes to managing business data. This includes developing processes for how customer or employee information is collected, stored, used, shared or destroyed.
The benefits of enterprise data management are numerous. Well-maintained datasets make it easier for organizations to comply with financial regulations by improving the quality of the data they receive which in turn allows them to make key business decisions with confidence.

It also enables organizations to leverage their existing datasets consistent with internal governance plans linked to all relevant databases and monitored for compliance, quality control, and security. According to an IDC report, volume of managed by companies around the world will grow by an average of 61% (175 zettabytes) by 2025. As such it's important for organizations to stay ahead of this growth by implementing effective EDM strategies early on. EDM is a challenge for organizations because it requires alignment between multiple stakeholders (including IT operations finance strategy end users) and relates to an area (creating using common datasets) that traditionally has not had a clear “owner”.Other benefits of enterprise data management include improved system collaboration and unification time savings minimization of repetition errors assurance of valuable datasets for decision making.

Conclusion

In this quick guide we've answered some commonly asked questions about enterprise data management and pointed you towards some resources that can help you learn more about EDM. By ensuring proper EDM organizations can leverage their existing datasets consistent with internal governance plans linked to all relevant databases monitored for compliance quality control security. Next develop document clear succession plan who will maintain if current manager leaves company. Extract Transform Load (ETL short) defines processes used pipeline obtain recreate system replicate target system such warehouse. Popularly known ETL Extract Transform & Load process extracting source not based analysis moving storage where analysis performed.