Primed to make a huge entrance in 2015, Data-as-a-Service (DaaS) empowers companies with real-time data to overcome tough challenges with data. DaaS is allowing companies to generate real-time insights and revenue from Big Data. Companies commonly report feeling overwhelmed solely by the mere size of big data, not to mention the processes necessary to use the data. This no longer has to be a reality. With DaaS using big data is no longer a couple month long process.
Most companies and risk systems still source data like it was pre 2003. Yet, the opportunity to refine strategic and operational decision making by taking full advantage of Big Data is compelling.
Within this vast amount of information is valuable and available data. This is the new world of Big Data and the information being created can be used in real-time to generate previously unimagined opportunities.
Many organizations still struggle internally with connecting all the dots within this myriad of data. This is where the thinking behind DaaS comes into play.
DaaS is a service approach in which unique and Hard-to-Find Data (HTFD) assets are sourced and structured to deliver a constant stream of qualified prospects, including a company’s own customers, who are actively searching for what they are selling. Distinctly different from list buying, these data sources are a highly customized marketing asset versus disconnected, one-time use prospect lists.
In-market prospects and customers are delivered directly to a company’s channel systems or digital marketing platforms, allowing marketers to can send real-time messaging, personalized recommendations, and highly targeted content.
DaaS combines three types of data which are uniquely customized to each company:
1st party data combined with 3rd party and HTFD. These specialty HTFD sets have been aggregated from hundreds of Big Data sources and go well beyond third party lists. As an example, these may be highly specialized sources of furniture or fashion interests, or spend data on specific businesses by categories.
Offline data transformed into addressable online identities. Onboarding provides new opportunities to reach customers and prospects in the digital universe. For example, targeted display campaigns can be displayed to specific customer and prospect segments. A financial company may want to target key customer groups with display ads that cross-sell another product. Or an auto company may show ads to people whose leases are up for renewal.
Real-time behavioral data. Fast Data aggregates event and behavioral-driven data to determine purchase intent as it occurs. Examples may include social purchase signals, such as People posting to social networks such as “Excited about the new baby” or “Taking a family vacation.” Or these may be discretionary purchase power signals, such as customers and prospects who are securing new credit sources, selling and buying cars or planning to move residences.
To really understand the potential of unique data sets sourced through DaaS – both HTFD and fast data, it is important to understand where all this data is coming from. The information being generated from Big Data can be segmented into six specific categories:
Web Mining: Data compiled by mining the open web. This includes automated processes of discovering and extracting information from Web documents and servers, including mining unstructured data. This can be information extracted from server logs and browser activity, information extracted about the links and structure of a site, or information extracted from page content and documents.
Search Information: Data available as a result of browser activity tracking search and intent behavior. This data also identifies digital audiences through onboarding (matching consumers to their online IDs).
Social Media: The average global Internet user spends two and a half hours daily on social media. A vast array of data is available on personal preferences, likes, “check-ins”, shares, and comments users are making.
Crowd Sourcing: This is collective intelligence gathered from the public. Data is compiled from multiple sources or large communities of people, including forums, surveys, polls, and other types of user-generated media.
Transactional: Data that is created when organizations conduct business, and can be financial, logistical or any related process involving activities such as purchases, requests, insurance claims, deposits, withdrawals, flight reservations, credit card purchases, etc.
Mobile: Mobile data is driving the largest surge in data volume. It isn't only a function of smartphone penetration and consumer usage patterns. The data is also created by apps or other services working in the background.
DaaS mines these Big Data sources to deliver highly customized data assets. Some examples include:
For years, organizations have been reliant on their internal data or data enhancements from list brokers. This is stagnant data compiled from third parties. DaaS on the other hand is transformational in nature - a revolutionary way of mining today’s massive data sets to find qualified prospects in the market now for what a company is selling.
So why model families who may be interested in family vacations when you can send campaigns to consumers who just booked plane tickets? Or why try to figure out who to target for a retail campaign when you can receive daily streams of prospects who are actively searching online for products you (or your competitors) sell? The possibilities are endless.
Rather than focusing on developing and managing an intricate network of data, companies can focus on the business outcomes and marketing advantages of Big Data. Generating immediate revenue from Big Data is a universal goal for most companies- and DaaS makes this possible for businesses across any type of industry.