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Four Common Data Management Use Cases You Need to Know

Four Common Data Management Use Cases You Need to Know

Four Common Data Management Use Cases You Need to Know

One of the most important factors in selecting the best data management solution is to ensure that it meets your needs and business goals. A use case is a list of actions or event steps that defines the interaction between an actor and a system. In data management, this means honing in on what you need to manage your data for, as well as the methods and technologies required to achieve the desired outcome. You’ll want to consider both current and future use cases for the software, and how well a data management solution can help you relative to cost and functionality.

Once you map out the use cases that are most important, you can begin to shop around for software that meets the needs of your technology environment. There are a wide variety of different data management models which you will need to consider. Do you need something that in-house users have control over, or is your organization growing and you require data management as a managed service? Things can get even trickier if you are attempting to integrate traditional technologies with more modern ones.

In an attempt to help you identify the platform that’s best for you, below we will outline four common data management use cases. While this may be common knowledge to some, it may not be to you, so we recommend a firm understanding prior to vendor selection.

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Data Management for Data Warehousing

Data warehousing encompasses the largest share of the data management use case pie. Traditional data management includes mostly structured data that is loaded through bulk and batch data integration processes. This prepares data for use in business intelligence reports and dashboards. New wrinkles have been added to allow for real-time analytical processing so users can reduce the time between when data is captured and when it can be analyzed.

Modern data management for data warehousing involves support for advanced functionality like forecasting, predictive modeling and data mining. It also enables the use of non-traditional data types and sources, though expert users are required to query these data layers. The most recent innovations in data warehousing solve for both structured and unstructured data, as well as IoT and machine-created information.

Data Management for Analytics

Data management for analytics is the most buzzy of any of the use cases mentioned here, and is quickly becoming a top priority for data and analytics leaders. The difference between data management for analytics and simple data warehousing is that software developed for this use case is specifically designed to support analytical processing, as well as the use of machine learning and data science programming languages. Solution providers are investing heavily in managing data for analytics, and some of the fastest-growing startups are rapidly innovating in the space.

Data management for analytics is so important that a sub-section of more niche use cases has developed as a result. Industry analyst Gartner highlights this in its most recent Magic Quadrant on the subject. These sub-categories include traditional data warehouse, real-time data warehouse, context-independent data warehouse and logical data warehouse. The researcher also notes that organizations may properly manage data for analytics with a combination of technologies. However, these technologies must join to provide access to the data under management by open access tools.

Data Management for Governance

Not only is data governance one of the most common data management use cases, it’s also the most difficult to solve for. Data governance is perhaps the most important factor in modern data management, and bridges the gap between data quality and democratization. In order for organizations to enable cross-enterprise data access (which is a major pain point in and of itself), data needs to be overseen in the correct fashion using industry-standard best practices.

While many of the industry’s best data management platforms offer capabilities that support data governance, the process really is up to the organization. Data governance is commonly made up of a set of frameworks developed to ensure consistent and quality data. Implementing a governance procedure often involves defining data stewardship roles as well. Those in these positions then decide how data will be stored and protected, and are to follow a strict set of guidelines to do so. Data governance is so important there’s even a collection of dedicated books and YouTube videos on the topic, and we encourage you to explore them.

Data Management for Compliance

Data management software has become a key cog in regulatory compliance, especially in the age of GDPR. The EU-centric legislation that was introduced in May 2018 has put data privacy and protection into the spotlight and on a global scale. As a result, organizations are looking for data management solutions that can help them maintain regulatory compliance in an environment where governments are increasingly introducing new data privacy laws.

Compliance is not a new topic, however, and thousands of vertical-specific organizations have been dealing with it for years. Companies in finance and healthcare, insurance and consumer goods have used data management to organize and monitor the data they store, send and receive for a long time. The hype for data management compliance has grown since dedicated tools have been introduced at an enterprise level, and given the current state of data privacy and protection, we expect this trend to continue. That makes this one of the most important use cases to consider during your search for the best solution.

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