5 Key Ways to Monetize Big Data For Your Business

by September 18, 2015 0 comments

– Arvind Purushothaman, Practice Head, Information Management & Analytics, Virtusa

The new age consumers of the digital era leave a lot of footprint or data in the online realm every time they shop or access an app over the internet. This voluminous data is a goldmine for companies to extract, interpret and leverage to design new products, services and cross sell. But alas, most businesses lack in their data strategies. Enterprises have to realize that data translates directly into business opportunities provided the right data architecture is in place. Big Data technologies create ground to unlock business potential in ways we can never imagine. Consider the example of the entry bands that theme parks use as tickets for access control. This same band could also be leveraged over an IoT platform to collate real time data about consumer preferences, say most time spent by a customer shopping for merchandize to offer customized deals or location based services. Real time data analytics not only enables unraveling newer opportunities to sell but also helps create elevated user experience.


Arvind Purushothaman Practice Head, Information Management & Analytics, Virtusa


“Organizations tend to have multiple apps and data assets starting with mainframe based ones, client server, web applications and some newer cloud based applications all co-existing together. This results in them struggling to find the right people to support the apps especially for the older apps.”


Key challenges in Big Data deployment
While most businesses and their CIOs talk about Big Data technology deployment, it is important to have the right data architecture in place to maximize monetization. It is vital to build a modern data architecture that is not only open, flexible and scalable but should also be compatible with existing and potential new data assets. This is easier said than done as there are several challenges in identifying and designing truly modern data architecture. Some key challenges faced by CIOs are:
Legacy systems are the biggest impediment. Typically, enterprises continue to work on apps that are dated and in many instances beyond 20 to 25 years. In such instances, often businesses are unaware about the kind of leverage that insights from each application are providing and what kind of monetization they are able to drive.
Compliance also leads to roadblocks in creating effective data architecture. Organizations would have built multiple data assets including Data Warehouses and Data Marts. This while power users collate data from multiple sources and worse creating reports using MS Excel. Consistency is the victim in such scenarios.
Integration of old and new apps is another major challenge. Organizations tend to have multiple apps and data assets starting with mainframe based ones, client server, web applications and some newer cloud based applications all co-existing together. This results in them struggling to find the right people to support the apps especially for the older apps.
Integration of technologies – those organizations that had a head start in deploying Big Data technologies like NoSQL and Hadoop have also stumbled in integrating these with the traditional Data Warehouse technologies.
Knowledge Management which was a concern in the IT services companies is now affecting enterprises deploying IT as well. CIOs today are concerned about having to find an army of programmers for populating Hadoop-based data repositories. Leveraging existing SQL skills acquired over the years by the employee base is also a critical question facing the CIOs.
Five key strategies to monetize BigData
While the above mentioned challenges may or may not be encountered by all businesses, given the ultimate need to be able to better monetize data and modernize technology platforms, it is important to have a strategy based on the following approach:
Data Asset Inventory – Collate comprehensive list of data assets including legacy systems, data warehouses, data marts, data islands. Identify the data flows between these assets, and the usage patterns.
Data Asset Rationalization – Organizations should have an overall asset rationalization strategy in place that should identify the strategic applications, their expiry date and their need in the future. This strategy should also encompass the Cloud strategy for the organization including how they want to approach SaaS, PaaS and IaaS.
Data Lineage – Rolling out a data lineage exercise to identify data flows, creating detailed documentation especially for the legacy applications is mandatory. This greatly reduces the risk of dependency on key personnel and also makes it easier to migrate to a future state architecture.
Data Infrastructure – Put in place a Big Data and Cloud strategy to bring in newer technologies on a pilot mode – start with non-legacy apps to understand the technology and move them over in conjunction with data asset rationalization. Cloud is significant component of modern architecture particularly when dealing with IoT data.
Data Technology – It is pertinent to understand the options available in the crowded and rapidly evolving marketplace, and select the right data technologies that will contribute to the right architecture. For instance, using a data integration tool with Big Data connectors will eliminate the need for people who can write Map Reduce code.
Above all, CIOs can also grow business by leveraging Big Data analytics to unravel consumers on social media and enhance productivity and streamline processes. Business Intelligence extracted from data is the potent weapon in a CIO’s armory to re-imagine products and services in the highly competitive markets. But it is architecture which is the key to monetization of data.

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