How Structured Is Your Tagging, Topics, And Vocabularies?

Do you recognize how important your structure for tagging, topics and vocabularies is for your API journey?

Streamdata.io provides technology for augmenting existing web APIs with real-time streams. Turning a request and response API call into a persistent stream of data. Depending on the request structure of the API, you can craft extremely broad or precise responses by changing the parameters you pass with each API call. Allowing you to take a very specific set of values and turn it into a precise topical based stream of data. However, to be able to do this you must have well designed, meaningful API resources, as well as possessing a robust vocabulary and tagging structure for being able to orchestrate with your API infrastructure, allowing you to turn them into topical streams that will make the desired impact.

How tag our data and the APIs we use to access our data resources will directly impact how efficiently we are able to quantify and execute our streaming and other event-driven infrastructure. All of this will ultimately define how coherently and effectively we are able to articulate what our enterprise capabilities are. Without a structured vocabulary for describing our data, and organizing, querying, and distributing data, we can’t coherently make valuable data resources to consumers in real-time, and as things change. Without a common vocabulary, we will just be turning on the fire hose of data for our consumers, blasting them with information, instead of providing meaningful, valuable, usable streams of topical data for them to use in web, mobile, and other applications.

It is easy to overlook something as simple as how we tag our data and API resources. It is something that gets lost in the shuffle of delivering API resources across the web, mobile, device, network, and machine learning applications we are developing and operating each day. However, it is an area that we can achieve much more flexibility, efficiency, and agility when it comes to organizing and distributing data resources across the enterprise. By aggregating, refining, structuring, and then educating teams about common tags that can be applied across our APIs, we can slowly begin to make expansive numbers of digital assets more discoverable, accessible, and more easily used across applications. Allowing us to better understand what our enterprise capabilities are, and allow us to more effectively apply these capabilities across internal, partner, and public use cases.

When first engaging, we ask all of our customers to describe where all of their digital assets reside. To help articulate what their enterprise capabilities are, when it comes to their digital transformation. Very few can do this effectively and coherently. Resulting in many enterprise organizations lacking a voice when it comes to effectively doing business across their web and mobile properties, and unable to make the move to voice, bot, Internet of things, and other emerging channels. Simply because they have failed to properly tag their digital assets, and step back to regularly organize these tags into meaningful topical channels for delivering data to consumers. Making a lack of vocabulary the biggest challenge the enterprise will face when it comes to doing business in the digital economy in coming years, leaving many large organizations unable to effectively articulate what is they wish to accomplish as part of their digital transformation.

Machine Learning

**Original source: streamdata.io blog

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