Keyword tags:
Big Data
Extra tags:
Cognizant
data lake
business analytics
By Jayajyoti Sengupta, Vice President and Head of APAC, Cognizant
Building semantically-rich information architecture is becoming increasingly crucial to help businesses see hidden patterns in the fast-proliferating data pools that surround people, organizations and devices. Indeed, to cut through the clutter and compete in the market, companies need a fish-finder to catch business insights from the volumes of data continuously generated by people, products, processes and organizations, which produce unique digital identities.
Draw meaning from the bits and bytes
Coming soon, to an industry near you, are billions of devices in the Internet of Things (IoT). When everything from smart fitness wristbands to smart cars to jet engines and shipping pallets are constantly and automatically generating information about their operation and their users’ activities, the term “big data” will seem quaint. As the number of connected devices grows ten-fold to 100 billion, data volume is expected to double every two years to 44 zettabytes or 44 trillion gigabytes, by 2020.[1]
This data growth is not only inevitable, but also essential to creating and improving all-important algorithms to create better, more personalized experiences for customers through managing and deriving actionable insights from the sheer bulk of data. This data might fuel a mobile app that guides customers to parking spaces near your store rather than a competitor’s, based on historical turnover at Internet-enabled parking meters. Or it might underpin a corporate app that orders inventory for neighborhood drugstores based on usage reports from local smart insulin monitors, combined with area Web searches for cold remedies.
Semantic technology lets you build on and extend your data warehousing and big data investments to drive much more powerful insights from a much broader data set more quickly.
Navigating the growing lake
Think of a data warehouse as a dusty, expensive building filled with papers in static file folders, all organized in a rigid classification system that was obsolete as soon as it was created. That’s often your classic data model, which doesn’t let you fully exploit the digital information that can help build new digital revenue streams and new commercial models.
Think instead of all the data from all your sources, internal and external, old and new, flowing into a massive “data lake”. As the lake gets bigger and bigger, with more and different types of data, how do you effectively identify and gather the data you need?
The data has to tell you itself. What you need is the data equivalent of a fish-finder that can peer into the dark depths of the data lake and tell an old sunken tree from a school of prized game fish.
This “fish-finder” for business insights is here, in the form of a smart, semantic model that captures the meaning of data, as well as the related domain expertise from data (whether it is structured, unstructured or semi-structured). The building blocks for such a model are standards and technologies such as the Resource Definition Framework (RDF), which organizes data in a graph structure, reducing development time and cost while delivering business value more quickly, and the Web Ontology Language (OWL), which provides a comprehensive model of data definitions and relationships that is human- and machine-readable. There are others like the SPARQL Query Language, which is a SQL-like query language for semantic data that can leverage ontological relationships to execute smarter questions across multiple databases in a single query, while inferencing is a technology that makes it easier for users to construct queries by capturing and embedding expertise in the ontology model.
A smart model that generates meaning
More importantly, generating meaning is essential—we need to know what the data means and what it represents before we can use it in algorithms that deliver business and user benefits.
An intelligent semantic model can deliver meaning and intelligence that empowers better decision-making. A conventional business intelligence system might describe “PPM” as “defects per parts per million”. That’s a good start, but it doesn’t deliver the full business meaning. Try, instead, a fuller semantic-enabled explanation, such as, “PPM or defects per parts per million, used by our specialty components line to justify premium pricing for our XL line of products.” That gives business users a richer idea of what the data means to them.
This semantic model also helps identify the data needed to craft more personalized customer experiences. An electric utility, for example, might use the model to find and combine a customer’s name, address, account number and service area with data from his Web-connected thermostat and smartphone location to offer a smart-home service for adjusting the air-conditioning temperature 20 minutes before he arrives home.
A semantic model makes it easier to embed domain expertise — field-based insights into how your customers, products or markets work — into the data. An ad placement application on a music streaming site might, for example, “learn” that listeners who prefer classical or jazz respond more often to detailed, fact-oriented ads, while those who like popular music respond better to simpler, more emotional appeals. Now you’re talking targeted ads, a win for both the advertiser and the consumer — if it’s done right.
No time to waste
Building this “fish-finder” — an intelligent semantic model that sits on top of your current information architecture — probably sounds daunting. But it can be done, starting with important but gradual changes that include prioritizing the onboarding of data, and balancing legal and compliance needs for security, with the imperative to improve the customer experience through analytics.
Suggestions could be aplenty, but the most important one is: Don’t wait. Start building your private semantic Web now to understand your customers, markets and industry before your competitors. Get started now to build a fish-finder to reap the insights that enable your organization to deliver the next great user experience, product or service.
[1] The Digital Universe in 2020," IDC and EMC, April 2014, http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf