Data Monetization is the End Goal
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.
Data Management, AML, and KYC Analytics
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 
Email Marketing is a Predictive Analytics Problem
Digital Marketing from 1999 to 2012
In his book Permission Marketing, Seth Godin referred to email marketing as “the most personal advertising medium in history”. That was 1999.
Where does email marketing stand in 2012 in the age of social media, omni-channel marketing and big data analytics? Here are some interesting data points.
What is a “Hadoop”? Explaining Big Data to the C-Suite
Keep hearing about Big Data and Hadoop? Having a hard time explaining what is behind the curtain?
The term “big data” comes from computational sciences to describe scenarios where the volume of the data outstrips the tools to store it or process it. Three reasons why we are generating data faster than ever: (1) Processes are increasingly automated; (2) Systems are increasingly interconnected; (3) People are increasingly “living” online.
As huge data sets invaded the corporate world there are new tools to help process big data. Corporations have to run analysis on massive data sets to separate the signal from the noisy data. Hadoop is an emerging framework for Web 2.0 and enterprise businesses who are dealing with data deluge challenges – store, process, index, and analyze large amounts of data as part of their business requirements.
So what’s the big deal? The first phase of e-commerce was primarily about cost and enabling transactions. So everyone got really good at this. Then we saw differentiation around convenience… fulfillment excellence (e.g., Amazon Prime) , or relevant recommendations (if you bought this and then you may like this – next best offer).
Then the game shifted as new data mashups became possible based on… seeing who is talking to who in your social network, seeing who you are transacting with via credit-card data, looking at what you are visiting via clickstreams, influenced by ad clickthru, ability to leverage where you are standing via mobile GPS location data and so on.
The differentiation is shifting to turning volumes of data into useful insights to sell more effectively. For instance, E-bay apparently has 9 petabytes of data in their Hadoop and Teradata cluster. With 97 million active buyers and sellers they have 2 Billion page view and 75 billion database calls each day. E-bay like others is racing to put in the analytics infrastructure to (1) collect real-time data; (2) process data as it flows; (3) explore and visualize. Read more
Oracle’s Analytics-as-a-Service Strategy: Exalytics, Exalogic and Exadata
Following the success of its Exadata (database as a service) and Exalogic (middleware-as-a-service) engineered systems, Oracle unveiled Exalytics Business Intelligence at Oracle OpenWorld 2011.
The goal of these appliances (engineered systems) is to help IT groups further shrink data center costs, increase system utilization and enable better application integration. All goals that CIOs everywhere continue to struggle with. CIOs now face an interesting decision matrix: Exalytics/Logic/Data systems versus traditional build from components versus hosted.
With ExaSystems, Oracle has a tremendous market advantage. Oracle owns most of the software that enterprises need today. Via acquisitions, Oracle owns the whole stack! Web tier, Middleware, Database software, Database tier, Storage tier. With Sun Microsystems it’s ideally positioned to maximize the platform capabilities. It’s easy for Oracle make its own software play nice on the Exalytics, Exalogic and Exadata platforms.
Wanted: CIO – BI/Analytics
In a tough economy, a new tech-fueled BI and analytics arms race is on to create the next competitive advantage.
Everyone is beginning to look beyond the status quo in BI, analytics, Big Data, Cloud Computing etc to fundamentally change how they discover fresh insights, how they can make smarter decisions, profit from customer intelligence and social media, and optimize performance management.
The headache for corporations is not the technology aspects but the leadership side. Who is going to lead this effort, corral the vendors and formalize and execute a more structured program.
Who is going to lead the effort to create the right toolset, dataset, skillset and mindset necessary for success?
As BI and Analytics moves from “experiment and test” lab projects to commercial deployments, companies are going to need more leadership and program management capabilities. They need leadership that can provide strategic, expert guidance for using powerful new technologies to find patterns and correlations in data transactions, event streams, and social media.
Some firms are making moves. In insurance, AIG – Chartis Inc. unit appointed Murli Buluswar to the new post of chief science officer. This aims to enhance Chartis’ focus on analytics… he “will be responsible for establishing a world-class R&D function to help improve Chartis’ global commercial and consumer business strategies and to deliver more value for customers.” This focus on analytics involves “asking the right questions and making science-driven decisions about strategies—whether it’s related to underwriting decisions, product innovation, pricing, distribution, marketing, claims or customer experience—with the end result of improving the scope of what Chartis delivers for customers”.
As a result of where we are in the maturity cycle and to support the business units better, we are seeing a new emerging role “CIO – BI” that is dotted lined to the global CIO or a shared services leader. Let’s look at a representative job posting from GE Capital, which always seems to be a step ahead of most companies. Read more
The Curious Case of Salesforce and Workday: Data Integration in the Cloud
The growing enterprise adoption of Salesforce SFA/CRM, Workday HR, Netsuite ERP, Oracle on Demand, Force.com for apps and Amazon Web Services for e-commerce will result in more fragmented enterprise data scattered across the cloud.
Automating the moving, monitoring, securing and synchronization of data is no longer a “nice-to-have” but “must-have” capability.
Data quality and integration issues — aggregating data from the myriad sources and services within an organization — are CIOs and IT Architects top concern about SaaS and the main reason they hesitate to adopt it (Data security is another concern). They have seen this hosted data silo and data jungle problem too many times in the past. They know how this movie is likely to unfold.
Developing strategic (data governance), tactical (consistent data integration requirements) or operational (vendor selection) strategies to deal with this emerging “internal-to-cloud” data quality problem is a growing priority in my humble opinion. Otherwise most enterprises are going to get less than optimal value from various SaaS solutions. Things are likely to get out of control pretty quickly. Read more
Analytics-as-a-Service: Understanding how Amazon.com is changing the rules
“By 2014, 30% of analytic applications will use proactive, predictive and forecasting capabilities” Gartner Forecast
“More firms will adopt Amazon EC2 model for data analytics. Put in a credit card, by an hour or months worth of compute and storage data. Charge for what you use. No huge sign up period or fee. Ability to fire up complex analytic systems. Can be a small or large player” Ravi Kalakota’s forecast for 2012
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Many organizations are starting to think about “analytics-as-a-service” as they struggle to cope with the problem of analyzing massive amounts of data to find patterns, extract signals from background noise and make predictions. In our discussions with CIOs and others, we are increasingly talking about leveraging the private or public cloud computing to build an analytics-as-a-service model.
Analytics-as-a-Service is an umbrella term I am using to encapsulate “Data-as-a-Service” and “Hadoop-as-a-Service” strategies. It is more sexy 
The strategic goal is to harness data to drive insights and better decisions faster than competition as a core competency. Executing this goal requires developing state-of-the-art capabilities around three facets: algorithms, platform building blocks, and infrastructure.
Analytics is moving out of the IT function and into business — marketing, research and development, into strategy. As result of this shift, the focus is greater on speed-to-insight than on common or low-cost platforms. In most IT organizations it takes anywhere from 6 weeks to 6 months to procure and configure servers. Then another several months to load, configure and test software. Not very fast for a business user who needs to churn data and test hypothesis. Hence cloud-as-a-analytics alternative is gaining traction with business users.
Proactive Risk Management – New KPIs for a Dodd-Frank World
The financial crisis of 2007–2011 is driving widespread changes in the U.S regulatory system. Dodd-Frank Act addresses “too big to fail” problem by tightening capital requirements and supervision of large financial firms and hedge funds. It also creates an “orderly liquidation authority” so the government can wind down a failing institution without market chaos.
Financial institutions will be spending billions to strengthen, streamline and automate their recordkeeping, risk management KPIs and dashboard systems. The implications on Data Retention and Archiving, Disaster Recovery and Continuity Planning have been well covered. But leveraging Business Analytics to proactively and reactively manage/monitor risk and compliance is an emerging frontier.
We believe that Business Analytics and real-time data management are poised to play a huge role in regulating the next generation of risk and compliance management in Financial Services industry (FSI). in this posting, we are going to examine the strategic and structural challenges, the dashboards and KPIs of interest that provide feedback, and what an effective execution roadmap needs to be for every organization.
