Data management used to center on static reports, delivering periodic updates based on batch processing. The drive for up-to-the-second data is shaping the ways data is handled, with instantaneous updates providing real-time streaming for data.
This means that at every level, from a CEO making a capital investment decision to a consumer wanting the best possible price on a pair of stylish shoes, streaming data allows for the most informed, up-to-date decisions. Streaming data to create a highly personalized end-user experience is a prerequisite for remaining competitive. Before a company changes their data management strategy to incorporate streaming, partners should advise them on how the following considerations can affect success:
When it comes to storage, not all data is equal. Data storage is an important consideration when implementing streaming capability. It’s necessary to have a system in place that can distinguish between data that needs to be saved and data that can be read and then discarded, such as streams of readings from sensors that are registering as normal.
Keep alternative databases in mind. A good portion of the data that is sought across an enterprise is of the unstructured, non-relational type, such as videos and log data. An alternative database that can process these data streams at a faster rate than a relational data system should be considered.
Use analytics that are embedded with the data. It may help to embed analytics with data solutions that are the solutions for basic queries. This not only creates a faster response time, but cuts down on the drag on performance.
Consider in-memory options. Back-end systems and applications must perform seamlessly in order to provide continuous, fast data delivery. In-memory technologies remove delays that may only measure in nanoseconds, but that time is critical in remaining competitive.
Innovate and automate using machine learning. Cut down on manual interactions with your streaming data management by employing machine learning. Predictive data relies on algorithms that allow systems to anticipate future outcomes or measure preferences to cut down on manual interactions with data.
Investigate cloud options. There are many options in the cloud for facilitating data management in a streaming, real-time environment. These options provide the machine learning algorithms that make streaming an automated, hands-off process.
Be ready to invest in data management skills. Whether this means training existing IT personnel or hiring new talent, your clients may need to beef up skills in the IT department.
Examine strategies for lifecycle management. Customers may need to rethink their approach to storage. Different storage options may need to be designated based on the age of the data or on other relevant factors.
eXemplify offers a unique level of support for partners. Find out how you can get up to speed on streaming through extensive training and product certification by contacting us today.