Data management is a collection of concepts and practices used to manage data produced in organizations. Data management has grown more complex while the notion that data as a valuable resource to be refined has grown in popularity. In many cases, enterprise business operations without data analytics are simply not a feasible strategy to remain competitive.
A general view of data management shows it to have two layers: a technical layer, where the actual processing of raw data occurs by computer processes associated with data collection, transformation, and analytics, many of which can be automated; and a non-technical layer, where non-technical personnel are engaging more and more with data to draw insights that assist in their everyday tasks.
Data management as a discipline has many sub-categories, and in many cases requires their own specialization.
These subcategories address the forces and factors that influence an organization’s data management strategy:
Data management is the practice of collecting, storing, analyzing, and using data securely, efficiently, and cost-effectively. From a consumer protection standpoint, data governance refers to the efforts of businesses that collect and use data to remain compliant with regulations that protect the personally identifiable information of consumers. From a political point of view, data governance becomes both a national and international point of concern in protecting citizen information.
Data governance is a data management concern that focuses on preparing and keeping data that is regulated by various laws aimed at protecting consumer information. The GDPR (EU General Data Protection Regulation) and CCPA (California Consumer Privacy Act) are two sets of laws that regulate organizations that collect and use data.
The GDPR is a European Union act, and so U.S. companies will need to adhere to it when collecting and using data from EU residents.
The GDPR, which came into effect in 2018, regulates data controllers and processors, and protects personal data of residents, essentially any information that can be used to identify a person. While this legislation doesn’t extend beyond protecting EU residents, but does apply to foreign companies interacting with EU residents, the GDPR has provided a roadmap for many other countries and regions to protect personal information.
The CCPA is similar to the GDPR, but is more specific in what data is linked to personal information, for example, information at a household or device level. A second significant distinction is those who are regulated. The CCPA is more narrow in who meets its regulation requirements. For example, a for-profit entity must meet one of three requirements to be regulated.
There are many facets of data management, as defined by its subcategories, however, the overarching benefit is to increase control and visibility into data assets within an organization. Without any form of data management, companies flounder in operations and growth, falling behind in competitiveness, and eventually out of the market. Modern data management systems, though, are ready made for many organizational cases, and provide several benefits that branch from improved data control.
Data management systems are inherently domain specific. In many cases, an organization will maintain multiple data management systems each serving a specific domain of their business. Having multiple systems leads to data siloing, which can result in less data transparency as data is locked up in these silos, however, by exploiting data siloing data architects may better protect certain sensitive data.
The following array of data management examples illustrates the need for master data management practices and system integrations.
Cloud data management combines the advantages of cloud services with the power to manage data across cloud platforms. Cloud advantages include resource scaling, disaster recovery, anytime anywhere access, backup and long-term storage, and cost controls. Cost controls are particularly beneficial for business, either small or enterprise, and grant both of them the ability to pay for resources as needed. Typically cloud providers will be responsible for maintenance in the cloud relieving those worries from businesses.
In other instances, organizations can integrate their own resources.
Multi-environment compatibility is another important capability of most cloud data management vendors. Data can be shared and integrated across private and public clouds, providing access to on-premise storage.
Master data management (MDM) is a solution intent on bridging the gaps between multiple domain specific data management applications within an organization. Today, businesses small to enterprise can use tens to hundreds of these types of data applications with little common ground between them to make easy meaningful connections. MDM platforms do the work of tying these applications together.
Essentially, MDMs do this by describing core entities in a business, data that other data management applications can draw upon, knowing that it is the master record and the most relevant and accurate. While core entities are chosen specifically for the business profile, some of the most commonly described entities are customers, prospects, suppliers, products, locations, etc. A master record of these core entities ensures accurate data throughout every system.
Data management requires a thorough look at the data requirements of a specific domain. However, general best practices, like the ones below, can help circumvent potential challenges.
Data management is the foundational step in effective data analysis. Organizations implement data management techniques when they view data as an asset to the business and an opportunity to find actionable insights. To this end, proper data management is important for several reasons: