Most solution providers would agree that the value of their solution lies at least partially in how it processes and handles data. However, solutions are processing more data than ever, thanks to many sources. That's where the proper hardware and data management partner can create the environment to satisfy your clients while your solution and its data grow.
Modern data management priorities
Organizations are asking for more of their data than ever. And therefore, the traditional KPIs such as sales and revenue, profit margins, and lowered costs are no longer enough. Today, companies are looking at more complex measures like customer satisfaction, environmental impact, and employee satisfaction.
Yet newer KPIs can't add business value until they can be fed relevant data. So even as new initiatives win executive buy-in, solution providers must create a clear line of sight through the enterprise to the data requested.
Finally, newer practices lift data management from its roots as an IT-owned task to a business discipline. However, an organization's current data management challenges must be addressed to achieve this goal.
Modern data management priorities
Diagram any medium to large organization's data architecture, and you'll likely see a picture reminiscent of a Rube Goldberg machine. For example, in Dell Technologies' recent white paper, The Ugly Truth About Data Management and the Journey to Unleashing the Economic Value of Data, they explore the following data difficulties:
Poor data visibility - From data scientists to senior leadership, anyone in the enterprise who needs to generate a report and use and understand data is having trouble getting the information they need.
Wasted time and effort - Getting to the insights that data consumers need takes precious time and energy that could be used elsewhere. Additionally, ongoing data growth expands the need for tedious ETL processes that consume human and electronic resources.
Data silos - Isolated storehouses of data throughout the organization make for data that is hard to identify when moved across the enterprise.
AI / ML compatibility - An ever-growing list of AI and ML algorithms require access to the data management framework to prove their value.
Data duplication - Due to the above problems, data consumers can be forced to use duplicated data - a practice that can eventually call the integrity of analytics into question.
Edge-based data growth - Thanks to low-latency connectivity, robust Edge processing, and IoT devices, all data challenges are intensifying.
Next-gen data management
Your next data management solution needs to be comprehensive, intelligent, and feature-rich to manage your data and a steady stream of new sources.
Some ideal quality qualities in the next-gen data management solution are:
Automated catalog population - using metadata on a wide range of sources. The idea is to make data available to the entire enterprise as quickly and painlessly as possible. That way, data consumers and newer solutions can leverage it as soon as possible.
Intelligent data pipelines - that utilize AI/ML and event streams to update data catalogs to rid the enterprise of ETL processes that add cost and complexity to existing operations.
Data silo acceptance - Instead of moving siloed data elsewhere, Edge Computing can process it locally, making the data more accessible across the organization. In addition, this strategy enhances your environment's ability to accept more data from IoT devices.
3rd party and feature store support - With support for 3rd-party tools, analytics engines, and feature stores, your next-gen data management solution will keep your data open and ready for your latest solution's use.
Next-gen data management - the role of hardware
As mentioned, the dominant strategy in the past was to move data from remote silos to core or cloud locations for processing, backup, and recovery. Today, your data can stay where it resides and still be leveraged, thanks to the power of Edge Computing and enhanced data management.
Such initiatives can leverage newer, intelligent Edge architecture like Dell's PowerEdge series of servers running 3rd Generation Intel Xeon Scalable processors. And when it comes to scale-out storage, Dell's OneFS OS can help simplify management.
Systems like these enable you to deliver solutions as sophisticated as AI and ML that run locally to the end-users and grow with your solution's needs. In addition, they can provide things like hardware-based security and the ruggedness necessary to perform safely in unconventional settings.
UNICOM Engineering - your data management hardware partner
Look to UNICOM Engineering to provide the systems integration expertise you need to use your data best. As an Intel Technology Provider and Dell Technologies OEM Partner, they can supply, build, and support the best hardware to meet or exceed the needs of your application and help you bring it to market faster. Schedule a consultation today to learn more about how UNICOM Engineering can keep you moving forward.