October 30, 2015
You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.
It begins with a seemingly innocent set of questions:
"Is there a better way to master my data?"
"Is there a better way to understand the data I have?"
"Is there a better way to bring data and content together?"
"Is there a better way to personalize data and insight to be relevant?"
Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.
Data relevance has always mattered. In today's hyperclimate, where customer and business success is measured in seconds and minutes, data relevance is measured in microseconds. Results of data relevance, or the lack of it, can be magnified. Think about the reaction to a retailer's stock and reputation when there is a security breach of customer credit cards. Consider how an ill-thought-out tweet by an executive of a clothing company alienates customers, bringing down sales and revenue as it speeds across social media and the news. Consider how a recommendation to purchase a book that's the same book the customer downloaded a month ago misses out on an opportunity because there's a low likelihood the book would be purchased again. What you know, and when, ensures a timely and effective response as well as recovery.
With increasingly complex and dynamic ecosystems to help our customers shop, travel, cook, or invest, no organization can standardize and model data to meet everyone's interaction. Our systems have to be smarter, evolving, and responsive. This is where semantics comes in. Semantics technologies compose data by recognizing actors, interactions, and need to dynamically compose and deliver relevant data for actionable insight. We experience this through Google web searches, Apple Siri, and Amazon Alexa. We are now able to harness semantics in the enterprise without the geeky overhead — and companies are doing it for security and fraud, commerce recommendations, safety, infrastructure planning, and energy producing.
It's time for us to cast off data practices that aim to manufacture insights and introduce semantic hubs that compose data for insight at the edge. By combining search, graph, machine learning, APIs, and a training methodology to govern data, business stakeholders will get what they need from analytics. For those that aspire to create true systems of insight, a composable data hub will be mandatory to harness all the data in context of decisions and action.
For more, check out our latest report: Compose Data To Create A Symphony of Insight.
- artificial intelligence (AI)
- big data
- enterprise architecture
- machine learning
- master data management