3 Common Data Management Practices in the Business World

As personal data is collected, transferred, and disclosed by both businesses and individuals around the globe with each passing day, this information must be managed effectively in order to prevent it from being stolen and used for criminal purposes. For this reason, organizations that handle the personal information of customers must ensure that they adhere to a data management strategy that will allow them to keep track of all of the information in their possession at any given time, while simultaneously using this data to serve their respective customers. With all this being said, while there are a number of ways that a company can manage its personal data, three of the most prominent techniques that are being used within the business world today include data warehousing systems, customer relationship management (CRM) systems, and data architecture and modeling.

Customer relationship management

Customer relationship management (CRM) systems are perhaps the most common data management practice that both consumers and businesses alike will be most familiar with. Due to the sheer number of customers that many large-scale businesses serve on a daily basis, CRM systems provide businesses with the ability to respond to the individual needs of their customers. What’s more, businesses can also use the personal data within their CRM systems to gain additional insights into the purchasing decisions of their customers, with the goal of providing these customers with better products and services where possible.

To illustrate this point further, one of the most prominent features of many CRM systems is real-time customer assistance. While customers that were looking to purchase a product from a particular business in the past would have to physically enter the business in order to gain more information about their products and services, or read about these products and services in a newspaper or magazine, live customer assistance means that consumers can call a phone number or visit a website and receive information immediately. Moreover, many businesses have also implemented AI chatbots and automated customer service features that allow customers to receive answers to their questions 24/7.

Data warehousing

On the other hand, data warehousing is another commonly used data management practice within the current business environment. To this end, a data warehouse provides a business with a central repository of information that can be analyzed in accordance with a multitude of different business processes or outcomes. For example, a business could use a data warehouse to identify why they have been losing customers over a given period of time, or analyze which of their products are selling the best during a specific season within a fiscal year, among other things.

On top of this, data warehouses are also one of the most effective ways that a business can protect the personal information of its customers. For instance, many multinational companies have been faced with the inevitable reality of complying with privacy laws such as the EU’s General Data Protection Regulation (GDPR), due in large part to the interconnectedness of business around the world today. Consequently, a business that serves customers within many EU nation-states will be required to track this data at all times, or be faced with hefty fines and monetary penalties.

Data architecture and modeling

Finally, data architecture and modeling are two other forms of data management that have become widely used around the globe today. Likewise, while the terms data architecture and data modeling are used interchangeably, they in fact denote two different approaches to data management. In saying this, data architecture refers to the tools and techniques that are used to store and analyze data, while data modeling instead focuses on the representation of the data that is contained within this architecture. For example, a business could choose to hold its personal data within the confines of a cloud storage system, and then create a graph based on this data in order to better represent the information.

Due to the inherent cohesion between data architecture and modeling, these data management techniques give businesses the ability to create a roadmap before developing a software application, deploying an automated tool, creating a new product or service, etc. Moreover, this roadmap can also help businesses save valuable time and resources, as the insights that can be gained through the utilization of data architecture and modeling structures would be very costly and time-consuming to implement using other similar methods.

While there are a number of varying data management strategies available to businesses and organizations within all sectors of industry, CRM systems, data warehouses, and data architecture and modeling systems are undoubtedly the most popular techniques that are being used in the business landscape currently. Nevertheless, any business that is looking to implement any form of data management strategy will invariably be providing its customers with an enhanced level of service, as the collection and process of information will only continue to increase in the years to come.