The billion dollar question facing executives everywhere: How do I monetize my data? What small data or big data monetization strategies should I adopt? Which analytical investments and strategies really increase revenue? What pilots should I run to test data monetization ideas out?
Data Monetization is the process of converting data (raw data or aggregate data) into something useful and valuable – help make decisions (such as predictive maintenance) based on multiple sources of insight. Data monetization creates opportunities for organizations with significant data volume to leverage untapped or under-tapped information and create new sources of revenue (e.g., cross-sell and upsell lift; or prevention of equipment breakdowns).
But, data monetization requires a new IT clock-speed that most firms are struggling with. Aberdeen Research found that the average time it takes for IT to complete BI support requests, with traditional BI software, is 8 days to add a column to a report and 30 days to build a new dashboard. For an individual information worker trying to find an answer, make a decision, or solve a problem, this is simply untenable. For an organization that is trying to differentiate itself on information innovation or data-driven decision making, it is a major barrier to strategy execution.
To speed up insight generation and decision making (all elements of data monetization) business users are bypassing IT and investing in data visualization (Tableau) or data discovery platforms (Qlikview). These platforms help users ask and answer their own stream of questions and follow their own path to insight. Unlike traditional BI that provides dashboards, heatmaps and canned reports, these tools provide a discovery platform rather than a pre-determined path.
Also companies like Marketo which create marketing automation software are getting into the customer engagement and data monetization game. Their focus is to enable marketing professionals find more future customers; to build, sustain, and grow relationships with those buyers over time; and to cope with the sheer pace and complexity of engaging with customers in real time across the web, email, social media, online and offline events, video, e-commerce storefronts, mobile devices and a variety of other channels. And in many companies, marketing knits these digital interactions together across multiple disconnected systems. The ability to interact seamlessly with customers across multiple fast-moving digital channels requires an engagement strategy enabled by data and analytic insights.
Underutilization and the complexity of managing growing data sprawl have motivated several trends during the last several years. Data-as-a-Service (DaaS) represents an opportunity improving IT efficiency and performance through centralization of resources. DaaS strategies have increased dramatically in the last few years with the maturation of technologies such as data virtualization, data integration, MDM, SOA, BPM and Platform-as-a-service.
These questions are accelerating the Data-as-a-Service (DaaS) trend: How to deliver the right data to the right place at the right time? How to “virtualize” the data often trapped inside applications? How to support changing business requirements (analytics, reporting, and performance management) in spite of ever changing data volumes and complexity.
To roadmap Wall Street priorities for 2013, we have been having an interesting set of meetings recently with MDs and leading architects in various banks and investment services firms.
Got the scoop on analytics projects they are investing in — Anti-Money Laundering (AML) monitoring, trade surveillance and Know Your Customer (KYC) analytics. To enable AML and KYC initiatives…the big foundational investments in 2013 are around:
1) Strengthening the Golden Sources – Security Master, Account Master and Customer Master.
2) Various enterprise data management initiatives – Data Quality, Data Lineage, Data Lifecycle Management, Data Maturity and Enterprise Architecture procedures.
3) Reporting improvements via next generation Enterprise Datawarehouses (EDW) — Reporting on top of EDW addresses the core problems faced by Finance, Risk and Compliance when these functions extract their own feeds of data from the product systems through which the business is conducted and use differing platforms of associated reference data in support of their reporting processes. Lot of current investments are in the areas of Finance EDW which delivers common pool of contracts, positions and balances, organized on an enterprise wide basis and completed by anointed “gold” sources of reference data which ensure consistency and integration of information.
Crawl, walk, Run seems to be the execution game-plan as the data complexity is pretty horrendous. Take for instance, Citi alone….has approximately 200 million accounts and business in 160+ countries and jurisdictions.
The type of data challenges banks like Citi are wrestling with include: Read more
Big Data emphasizes the exponential growth of data volumes worldwide (collectively, >2.5 Exabytes/ day).
Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In parallel, the emerging field of data science introduces new terms including, predictive modeling, machine learning, parallelized and in-database algorithms, Map Reduce, and data monetization.
A variety of infographics have been published around Big Data, Data Scientists. Here is a compendium of some very interesting ones.
|Big Data Big Opportunity||A Data Scientist Study|
“Through 2015, more than 85 percent of
Fortune 500 organizations will fail to effectively exploit big data for competitive advantage” - Gartner BI Summit.
It doesn’t take genius to recognize that there is an increasing demand for information to improve shareholder value and gain competitive advantage by leveraging information, data and analytics as a strategic enterprise asset. The question is no longer about the importance of data but when, how, and where to leverage the asset. Read more
The Oracle BI stack illustrates the landscape changes taking place from hardware to mobile BI apps.
As I see it, there are two clusters of “parallel” innovation: (1) technology/infrastructure centric and (2) business/problem centric.
The interesting thing in the technology/infrastructure centric side is the multiple paths of innovation that are taking along different technology stacks shown below. The disruptive innovation is happening in parallel along 4 different fronts: Read more