Everyone wants a leg up on the competition, whether it is through a superior offering, better pricing, or high-quality customer service. But to achieve those things, organizations need to make business decisions that allow them to compete and thrive. How? By accessing, analyzing, and applying their data in real time to make informed, critical business decisions. This can be an incredibly difficult task, especially as the number of data sources, data heterogeneity, and overall volume of data increases as organizations across industries undergo a digital transformation.
As access to data and data sources continues to explode, businesses are being forced to rethink their data strategies to consider more information and power real-time intelligent decisions. That is, however, easier said than done. With the amount of data available that is pouring in from a myriad of sources, it can be difficult to identify what provides value, and what is just noise. Increased data and cloud growth has led to data integration challenges. The data is often stored in disparate silos and in different forms and formats.
Some approaches organizations should consider adopting include:
- Interoperability: A comprehensive integration and open analytics data platform provides database administrators with streamlined data coordination, enabling simpler connections between data sources and business processes. This also enables organizations to eliminate “siloed” data by connecting people, processes, and applications.
- Integrated multi-model: This approach allows organizations to store data in its native format and apply transformations on demand as required by the composite application. Users can also store the normalized data for forensics and use by the data science team.
- Multi-workload and analytics platform: Utilizing this approach will allow organizations to apply real-time analytics to business processes. They can be based on the prior work of the data scientists who have built and tuned models on the available data, and the real-time incoming information that needs to be processed immediately (such as credit card transactions or customer activity). Unlike incorporating different systems for integration, database management, and analytics, this approach provides a simpler architecture with fewer moving parts to integrate and maintain that ensure there is no latency.
A current challenge facing data integration is the shift to the cloud. According to Gartner, by 2023, 75% of all databases will be on a cloud platform. While cloud infrastructures can make data more readily available, they can also create new stressors as organizations look for ways to make their data and workloads cloud-agnostic—that is, able to execute on a variety of public and/or private cloud environments to increase flexibility and minimize the risk of vendor lock-in.
Organizations also need to keep in mind the more than 750 compliance and regulatory requirements they must address on the global (GDPR), country (U.S.-based SOX), and industry level (HIPAA). For example, let’s look at SOX. It primarily impacts financial department operations but also might influence what the marketing department can do in terms of hosting corporate events and promotional activities. Organizations need to be ready across multiple departments for an audit at a moment’s notice, which means constantly managing and securing data to meet compliance requirements and avoid hefty fines. They must understand how regulations interact with each other and how they affect the organization as a whole. Integration is critical to maintain data compliance with these requirements and to maintain a single, accurate version of the truth.
Organizations must understand the data they collect, the meaning of it, why they collect it, and where it lives. Once that is achieved, they can determine how they want to use the data. They can start to look at modern data platform technologies that not only eliminate data silos but also eliminate silos of data management technologies based on modern data requirements, including resource efficiency, data durability, scalability, and ease of management. To effectively manage increasing data volumes in real-time, organizations should consider adopting an integrated approach that balances the need for connected data to produce real-time insights while keeping compliance in mind. n