I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.
Ventana Research recently announced its 2020 research agenda for analytics, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from their technology investments and improve business outcomes.
It’s been exciting to follow the emergence of innovative capabilities in the analytics market, but for businesses it can be challenging to stay on top of all these changes. To help, we craft our research agenda using our firm’s knowledge of technology vendors and products and our experience with and expertise on business requirements.
Organizations’ use of data and information is evolving as the amount of data and the frequency with which that data is collected increase. Data now streams into organizations from myriad sources, among them social media feeds and internet-of-things devices. These seemingly ever-increasing volumes of devices and data streams offer both challenges and opportunities to capture information about a business and improve its operations.
The emerging internet of things (IoT) is an extension of digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere. This innovation means that virtually any appropriately designed device can generate and transmit data about its operations, which can facilitate monitoring and a range of automatic functions. To do this IoT requires a set of event-centered information and analytic processes that enable people to use that event information to make optimal decisions and take act effectively.
Organizations now must store, process and use data of significantly greater volume and variety than in the past. These factors plus the velocity of data today — the unrelentingly rapid rate at which it is generated, both in enterprise systems and on the internet — add to the challenge of getting the data into a form that can be used for business tasks.