Now, well into 2013, the concept of Big Data is already becoming an outdated non sequitur. As data increases rapidly, storing huge amounts of data in uncorrelated, separated silos (in database or data warehouse storage) that need to be constantly queried can’t drive any new, intelligent change in a business. In fact, this approach creates even greater challenges. Big Data by itself can’t drive change because it is just a more efficient, more technological way of doing business as usual. Databases that store transaction history are a practice as old as a shop keeper maintaining a ledger of purchases and sales. How is simply scaling that same idea into the millions of entries going to drive any real change in business? That old approach is Big Data 1.0 and it can’t compete with correlated, referential Big Data. Integrating varied information in an individual context, in the moment of customer’s engagement is fundamental to move business forward in any way and has to be the foundation of any conception of Big Data 2.0.
The Process of Storing and Using Big Data is Inherently Limiting
If data is stored and siloed on a system-by-system basis, like transaction history in its own isolated database, all the petabytes in the world won’t give any real business advantage. Trying to gain understanding of customers, suppliers, or partners from transaction data in isolation, even if it’s every piece of transaction data from a company’s founding, is a one-dimensional approach with one-dimensional results.
When businesses step outside of the paradigm of storing data on a system-by-system basis they can begin to correlate transaction history, with browsing history, with loyalty data, and mobile and social data from all their systems to meet their customer in the moment they are engaged.
What Becoming Integrated Means
Our vision of what it means to become a fully integrated enterprise is found in our Integration Maturity Model, which gives an assessment of a company’s integration maturity. Based on governance and organization, infrastructure and operations, event enablement, and connectivity, the Model helps organizations better understand their integration goals and needs.
Integration Makes Big Data Useful Again Through Contextualization and Correlation
Companies wonder why they get the same poor adoption rates from blast email offers to thousands of so-called “segmented” customers. It’s because these companies aren’t fully integrated and aren’t even beginning to imagine automated, personalized engagement. When all systems are connected through an Integration backbone, these same companies won’t dream of sending a blast email again.
The Holy Grail of Sales: Affecting the Customer at the Moment of Their Purchasing Decision
The Holy Grail of the sales process, both B2C and B2B, is to affect the purchaser at the exact moments that matter most. With fully integrated systems, an automated offer, reward or even just helpful information can be sent in real time to a customer who is currently using an online shopping cart system. Smart, integrated systems know the customer’s identity, which is correlated with their browsing history to show they looked at a certain item’s webpage five times in the last two weeks, but it isn’t currently in their shopping cart. Engagement at that moment matters enormously.
This above example combines data at rest (their browsing and purchasing history correlated with loyalty profile) with data in motion (this current, real-time browsing session) to affect change in the moment of the purchase or other decision. Big Data alone could never give a business any of these capabilities by itself. Any forward-thinking, fundamental shift in how we do business is going to have to come through integrating our systems, through using this Big Data (all this stored data at rest) with data in motion.
Integration as the Logical Conclusion of Modern Business Practices With the Traditional Individualized Customer Relationship
To this day, the traditional model of a local general store is a more efficient, more integrated offer engine with much higher rates of adoption than even the most technologically advanced modern conglomerations. The general store owner would know every local customer, their family, their history, and purchasing habits over time and contextualized within their major life events.
When the store owner’s neighbor’s cousin entered their store, the owner could engage in a way that was completely tailored to the individual. The store owner can affect the customer at the moment of query, purchase and service to ultimately sell more than the customer would have bought without relevant engagement. Not only does this increase the store’s revenue, but also increases the loyalty of the customer who sees how the store understands and appreciates him.
With the modern mass proliferation of virtual store fronts, mobile, web and numerous other touch points for a single brand, we have lost this hyper-individualized relationship. It’s structurally impossible for a business that serves millions to have a one-to-one personal relationship with their customers. Personalized customer relationships have been lost through industrialized growth, but by taking the ideas behind Big Data to the logical conclusion of integration, we have the technology capable of emulating that one-to-one customer relationship. Integration brings these two methods of business (one-to-one personal relationships and mass industry) together as dialectic of old-world service and modern business infrastructure.
For more on our Integration Platform, take a look at this white paper.