In today’s digital world, the ability to collect, store, and process data are key in order to meeting the future requirements of an organization. Disrupting Data modeling helps you create a blueprint for your company’s data so that it can be accessed, stored, and managed more efficiently. However, this is not always an easy task for many businesses. In fact, there are numerous challenges posed by data models that enterprise companies need to overcome in order to stay competitive.
Digital transformation is changing the way businesses operate. It’s no longer just about increasing efficiency, streamlining processes and reducing costs. Enterprises are evolving to become more connected, collaborative, cloud-ready and data-driven. To achieve this digital transformation, businesses must re-think their data models. Data models, which are the model of your data, are the first step to the digital transformation. They lay the foundation of the entire data management operation. If your data model is not optimized, it will be difficult to implement data management practices such as real-time information, AI and ML, and GDPR compliance.
A great example of how data management practices can disrupt data models is how enterprises are implementing real-time information. Real-time information is used by many industries, especially retail, to respond to customers’ needs in real-time, in a fraction of a second. For example, if a shopper places a product in their shopping cart but then decides not to purchase it, the system will remove the item from their cart. If that shopper later decides to purchase that item, the item will instantly be added to their cart. This is only possible through real-time information, which is an integration of data insights, automation, and AI. A data model that supports real-time information requires data to be refreshed as often as every second. This requires a different approach. It’s no longer about storing data in a database and then accessing it later. It’s about having the data stored in databases, but also in other storage devices such as the cloud, or even in the sensors themselves.
As we’ve seen above, real-time information requires keeping data close to the source of the data. That is, keeping data in the databases that are meant to hold the data but also keeping data in other devices such as the cloud. This approach is called hybrid data management. Hybrid data management is a data management practice where data is stored in a combination of databases and other storage devices, such as the cloud. No one type of device is a database, and no one type of device is the cloud. Instead, a hybrid data management strategy uses both databases and the cloud to store data. A hybrid data management strategy might use a combination of databases, data lakes, and data warehouses. Data lakes store unstructured data, such as data from IoT sensors. Data warehouses store structured data, such as customer data. Hybrid data management is a combination of both.
In the same way that real-time information requires a different approach to data management, so does AI and ML. While data management practices such as real-time information require integrating data, AI and ML require distributing data. Data models that support data distribution are different from those that support data integration. To distribute data, the data model should have data that is accessible by different people around the world and in different devices. It should be available in real-time and at the right granularity. Therefore, the data model must be decentralized. Distributed data models are decentralized data models that are distributed across multiple databases, servers, and other devices. They are accessible by different people and in different devices, and they offer real-time data. In order to distribute data, the data model must be decentralized—this means the data is distributed across multiple databases, servers, and/or other devices. Distributed data models are decentralized data models that are distributed across multiple databases, servers, and other devices.
As we’ve seen, real-time information and AI and ML technologies require a different approach to data management. Therefore, if businesses want to keep up with the pace of digital transformation, they need to re-think their data models. GDPR compliance, which is necessary for most organizations, requires re-thinking data models as well. GDPR compliance requires businesses to be transparent about how they collect, store, and use personal data. They must put in place policies and procedures to protect this information from being stolen or compromised in any way. Businesses must put in place policies and procedures to protect this information from being stolen or compromised in any way. GDPR compliance requires businesses to ensure that data is accurate, complete, and updated at all times. They must also be able to quickly rectify any inaccuracies or omissions.
The digital transformation also means that data models will change, as they are not static. To succeed in this new world, enterprises need to invest in data modeling processes and systems that can support their future needs. A disruptive data model will give you a competitive edge by enabling you to leverage your existing data assets and take full advantage of the opportunities presented by digital transformation.