In an article from Gartner (1), augmented data management was identified as a trend to watch in data analytics. This is the concept of using Machine Learning (ML) and Artificial Intelligence (AI) concepts around not only the actual data but also the associated metadata. Using the metadata allows the creation of very efficient robotic processes. Automation becomes proactive as opposed to reactive. Once analyzed and rules are properly deployed, real-time optimization and tuning of this data can be achieved.
As Gartner pointed out, data and analytics continue to collide. As a result, metadata has become more and more useful. In a simple file transfer process, metadata is the information that describes an asset such as the file name, size, source folder name, and the IP address of the host or destination. There are plenty of other data points that can be used for any given data exchange. In many cases, logical rules are easily deployed based on the name or other object without actually opening up the data. Rules can be built to route processing appropriately and to prioritize or alert as needed.
Industry experts suggest over the next few years, our IT human resources will be less engaged in managing and preparing repetitive and low-impact data. Remember the days of the first computer jobs, the Data Entry Clerk? Those jobs turned into data analysts who now sift, sort, and prep vast amounts of data for further automated processing. We have also created business analysts who manage and deploy the systems used to move and store all of this data.
These jobs will remain. However, with ML and AI concepts on top of the metadata, they will gain more focus as to what tasks they are required to complete. More advanced search engines have developed a “Googlesque” approach and capture and store the associated metadata concerning a certain topic. In Google’s case, traditionally they focused on publicly accessible web site data. Other’s have followed this model, but now apply the ML and AI concepts. This provides insight like never before. With that insight, we can build rules to just handle it (what does juat handle it mean?), and thus allowing our IT support teams to be much more proactive and manage by exception.
Better Data Quality Management can be attained using augmented data. Using this data, patterns and trends are more easily identified. You begin to turn the page on decision support from just simply possessing actionable data, to having robotic processes take action. This is true automation, not just staging the data from one silo to the next, but predicting and being proactive to identify and resolve issues without human intervention.
There are a few simple steps you can take to get started with augmented data. First, start with a project that can actually be useful. Often times organizations will undertake a pilot as a test. Make it count. Next, take a look at the tools you have available for data management and data quality. Organize your team and project and build your prototype. Lastly, bring this effort forward for evaluation to the stakeholders in your organization. Refine, rinse and repeat.
This approach is starting to take hold more often due to the fact that while the amount of data across organizations is mind numbing today. it is necessary to the business. Consider your document repository and just how unorganized it really is when you are trying to find something. If you are not taking this into consideration in your roadmap, no matter your business, you are losing a competitive advantage to those who are. Our automation tools allow us to use all of this metadata to facilitate some quite extraordinary workflows. Augmented data does not have to be a scary concept. From reality to data, augmentation is simply enriching your world in ways to help organize and streamline your processes and perspectives. Integration tools that easily take advantage of this type of data to create very robust, robotic automation are available.
At Vorro, we provide a framework for companies looking to leverage todays technologies. Our data modeling and processing techniques help streamline activities within your company. Does the current environment have you looking for ways to improve, scale or increase current productivity and processes? Take advantage now and start building for the future. Speak with the data integration specialist at Vorro. Feel free to request a consultation or demo on our website at https://vorroconnect.com/. #IntegrateNow
Billy Waldrop is the Chief Operations Officer for Vorro, Inc. Billy has dedicated his career to managing and developing complex systems for the manufacturing and healthcare industries. He spent 10 years at the Mayo Clinic, where he supervised and directed teams responsible for the development and support of critical Patient Financial Services systems. He holds an MBA and a B.S. in Professional Management, along with many certifications from the Mayo Clinic.