Big data is characterized by the dramatic growth in the volume of data (internally generated and from external sources) available to businesses. It is a characteristic of a companies IP generating capability and presents new opportunities for companies to grow revenues through better customer (AKA data) insights. Data growth have been around since the beginning of time, but has become a challenge given the recent improvement in integration, adoption of the cloud, and leveraging of social networks.

There are some very powerful and somewhat overwhelming statistics driving the big data discussion that should make business stop, listen, and think:

- $600 to buy a disk drive that can store all of the world’s music

- 5 billion mobile phones in use in 2010

- 30 billion pieces of content shared on Facebook every month

- 40%projected growth in global data generated

- 235 terabytes data collected by the US Library of Congress by April 2011

- 15 out of 17 sectors in the United States have more data stored per company than the US Library of Congress

- $300 billion potential annual value to US health care—more than double the total annual health care spending in Spain

- €250 billion potential annual value to Europe’s public sector administration—more than GDP of Greece- $600 billion potential annual consumer surplus from using personal location data globally

- 60% potential increase in retailers’ operating margins possible with big data

- By 2018, the United States alone could face a shortage of 140,000–190,000 more deep analytical talent positions and 1.5 million more data-savvy managers. This alone is hugh!

McKinsey Global Institute understands this issue quite well. In there “Big data: The next frontier for innovation, competition, and productivity” report, released May 2011, they identified five ways that Big Data could create value:

1. Big data can unlock significant value by making information transparent and usable at much higher frequency.

2. As organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time.

3. Big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services.

4. Sophisticated analytics can substantially improve decision-making.

5. Big data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance (preventive measures that take place before a failure occurs or is even noticed).

So, if you are into Big Data, then spend some time mining the MSI report. It is time well $pent.

1. Convergence of Mobile and Online Technologies - As mobility continues to grow into a maturing channel, banks and their vendor partners will produced richer mobile offerings that take advantage of its unique capabilities.

Implications: Banks need to develop a cohesive set of technologies that will make mobile app and online development easier for banks to manage.

2. Business Process Management – Banks need better methods of gathering and reporting data to both increase efficiency and ensure regulatory compliance.

Implications: Banks will need to invest time/money into big data, analytics,  and the technical challenges of analyzing and reporting very large amounts of information with a quick turnaround. Data integration, along with enterprise system integration, will also help banks obtain a more accurate view of their customers.

3. Message Centers will Replace Email – Dedicated web portals designed for secure communication between a bank and its customers will continue to replace legacy email systems. Security are among many of the benefits for such a move. Ernst & Young that found employees within organizations and businesses are increasingly the targets of hackers rather than individual consumers.

Implications: Banks employing third-party and joint marketing campaigns need to develop a coherent strategy for integrating these capabilities with the message center paradigm.

4. Table-based Banking Experience - The potential for a great tablet banking user experience, especially with the rich interface tablets offer (e.g., IOS, Android, etc.), is nearly unlimited. Customer like feeling good when they deal with their finances, the mobile rich user experience of the tablet world offers a means for personalizing the banking experience.

Implications: Banks need to develop a comprehensive online banking strategy based on cutting-edge mobile technologies, such as mobile remote deposit capture, in order to meet the mobile user experience needs of the customer.

5. security, Security, SECURITY – As more user rely on mobile and tablet based technologies to conduct their banking, these devices also will become the growing focus of hackers and fraudsters, who are always on the hunt for ripe targets. It almost goes without says, but Ernst & Young nevertheless report released in November 2011 on the current state of security threats noted that consumers who access sensitive data, such as banking information, on mobile devices at wi-fi hotspots are more susceptible to hacks.

Implications: Banks need to assess their security portfolio across all user channels and expect to invest time and money into ensuring safe online, mobile, and remote banking practices.

6. New Post-Channel World – “The days when a customer would walk into a branch to fulfill all of his or her banking needs are long gone,” according to Mr. Yurcan. In most cases, customer are expected to finish their business (e.g., loan application) in the same branch it was started. But changing customer demographics, work and travel habits are changing this expectation.

Implications: Banks will need to better manage the seamless integration of online, offline and mobile channels in 2012 and beyond.

7. Using Self-service to Generate Bottom Line Revenue – In 2011, the customer black lash against Bank of America’s $5/month ATM fee sent a load message to the banking industry – Don’t charge me for doing your job! Customer want self service and expect not to pay for it.

Implications: Banks need to focus on self-service to grow the bottom line by encouraging customers to embrace cost-saving practices (e-statements, online bill pay, etc.).

When I originally read the BMG book last year, I wrote about it value as well as the potential value of using the methodology on a personal level. Base on some exercises that the BMY recommends, I have since update my own model, Dr. Jerry A. Smith V2.0:

Click For Larger Version

This new BMY is for my work as a IT strategist, designed to help companies grow their business (revenue, margin, customers) through technology and social media driven capabilities. While not perfect (nothing ever is), it has helped me focus on those activities, resources, and partnerships needed to better help my clients.

D

The report recommends that leaders in companies need to support the move to social business

• Make themselves more visible, especially when public crises occur.
• Provide all employees with the training, tools and authority to engage with customers.
• Look beyond standard metrics to the value of brand loyalty, advocacy and a sense of community.

Currah explains that building the social enterprise will rely on a combination of cultural, functional and technical changes. He uses the graphic below as a visual framework, which identifies the key components of the social enterprise with respect to customer service.

The key point is that social CRM technologies are integral to every level of the customer service model in a social enterprise. However, the key is the “realtime flowof information between the customer service operation and the rest of the social enterprise.” In doing so, service organizations are equipped with the right information, at the right time, that is necessary to sustain the customer relationship.

The report also emphasizes the importance of CRM software as a means to harness the collective knowledge within the company.

• The customer service operation is equipped to monitor and engage with a targeted spectrum of media.
• Companies fully understand where, why and how their customers are using social media before making any changes
• A balance is struck across different types of media – telephone, email, web, social and mobile.
• The power of online communities is recognised, and customers are encouraged to help each other.
• Relationships are nurtured with advocates who wield particular influence on the Internet.
• Specialist tools are used to measure the impact of customers’ online activity

The report concludes with a belief that the social media will become the center piece of modern customer service activities. Currah say that the ”rise of the social customer has powered a broader transition to the social enterprise, which is seeking to emulate the speed and simplicity of the social web.”

>> Enterprise architecture is the architecture of business capabilities

>> Enterprise architecture provides a common basis for understanding and communicating how systems are structured to meet strategic objectives

>> Instead of allowing a single solution to drive the technology, EA provides a balanced approach to the selection, design, development and deployment of all the solutions to support the enterprise

>> Enterprise architecture allows stakeholders to prioritize and justify often conflicting technology trade-off decisions based on the big picture

The defining characteristic that differentiates Enterprise Architecture from other architectures is simple:

>> Enterprise scope – – covers multiple business units – crosses functional boundaries

For those who still have some doubt, here are a few excerpts from Holt’s review that might peek your interest:

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Background: Consider for a moment Linda, who is single, outspoken and very bright, and who, as a student, was deeply concerned with issues of discrimination and social justice.

Question: Which was more probable:

1. Linda is a bank teller
2. Linda is a bank teller and is active in the feminist movement

If you are like most people in Daniel Kahneman experiment, your response was (2); in other words, that given the background information furnished, “feminist bank teller” was more likely than “bank teller.”  This is, of course, a blatant violation of the laws of probability.

What has gone wrong here? An easy question (how coherent is the narrative?) is substituted for a more difficult one (how probable is it?). To explain this behavior, Kahneman uses two systems:

System 1 –  our fast, automatic, intuitive and largely unconscious mode. It is System 1 that detects hostility in a voice and effortlessly completes the phrase “bread and. . . . ” System 1 jumps to an intuitive conclusion based on a “heuristic” — an easy but imperfect way of answering hard questions. In general, System 1 uses association and metaphor to produce a quick and dirty draft of reality.

System 2 –  our slow, deliberate, analytical and consciously effortful mode of reasoning about the world. It is System 2 that swings into action when we have to fill out a tax form or park a car in a narrow space. System 2 lazily endorses this heuristic answer of System 1 without bothering to scrutinize whether it is logical.

In general. System 1 proposes, System 2 disposes.

Just putting on a frown, experiments show, works to reduce overconfidence; it causes us to be more analytical, more vigilant in our thinking; to question stories that we would otherwise unreflectively accept as true because they are facile and coherent.

Kahneman never grapples philosophically with the nature of rationality. He does, however, supply a fascinating account of what might be taken to be its goal: happiness. What does it mean to be happy? .

While these are powerful statistics that speak for themselves, Alexia Nielsen, Social Media, has written a great article that briefly highlights there meaning of this Pew Internet study. The bottom line is:

- 65% of online adults use social networking sites, and most describe their experiences in positive terms.

- Two-thirds of adult internet users (65%) now say they use a social networking site like MySpace, Facebook or LinkedIn, up from 61% one year ago. That’s more than double the percentage that reported social networking site usage in 2008 (29%).

- More half of all adults (50%) use social networking sites.

So, what does this mean to the average company? A lot. For the first time, the majority of people who are using social media have money. That’s right – they HAVE MONEY.

We are no longer talking about kids and young adults chatting away with their friend (AKA FaceBook). You can social market to them all day long, but capturing 100% of a market that has $0, still gives you Zero Revenue.

We now have a opportunity to help mature communities solve their needs and potentially make money at it. This is a good position to be in, if you are listening to your communities, have a great product/service, and are in a position to deliver it at an acceptable price.

Before we get into the discussion why enterprise data migration fail, lets first spend a bit of time of the basics. Data migration is often defined as process of transferring data between storage types, formats, and/or computer systems.   In essence, it is the one off selection, preparation and movement of relevant data, of the right quality, to the right place, at the right time. It is usually performed manually and/or programmatically when organizations change (extend, upgrade, adopt) information systems, where the new data storage format is not the same as the old. But not all migrations of data are data migrations.

Data migration is the irregular movement of data (an important concept that will come back to later). Regular movements of data, such as those found in data warehouse refreshes, are fully automated, highly repeatable, and take advantage of pre-mapped data schemas, are not part of this data migration space. With data migration, a single pass movement of data is often accompanied by low or inconsistent initial quality of data between disparate databases and/or applications. It is this large-scaled multi-factor characteristic (semantics, people, process, technical, workflows, data quality, and product types) of enterprise data migration that make is so complex, lending itself to numerous failure modes.

IDC has estimated that over 80% of data migration projects fail or have significant cost overruns, which is itself a type of financial failure. Within this cohort, 50% exceed their projected schedule by 75%,  their budgets are exceeded by 66%,  and 33% of these fail entirely. While these failure statistics are based on a wide range of projects, over have used some form of formal migration methodology. These are not good results if your looking for migrate a enterprise system while maintaining critical business function and financial predictability. Moreover, give the complexity of today’s enterprise systems, you can only reduce the risk of migration failure, but you can not eliminate it completely.

While there are no industry standards for migrating data within the enterprise that guarantee success, there are many migration methodologies designed around risk reduction. The most common (e.g., Practical Data Migration) are based on variants of proof of technologies/concepts, landscape analyses, data quality/cleansing, engaging the business early and often, testing, and automation through ETL tools. While these are good practices, or in some cases even necessary, for any project, they have limited demonstrated ability to ensure that the data is correct/error free after being transformed through complex data quality and business transformation rules.

So, with its high complexity, enterprise data migration should be dead, not because it is bad or evil, but because it the risk of failure is just too high. But, if not the enterprise migration, then what? Well, the answer lays in moving away from data migration as an unplanned irregular movement of data, something today’s enterprise architecture (EA) is all about. But what is EA in the context of migration?

Enterprise architecture is the continuous practice of organizing logic for business processes and IT infrastructure that reflect the integration and standardization requirements of the company’s business operating model (TOGAF 2009). EA is more than just structure, it is an dynamic means to realize architectural vision, business architecture, information systems architecture, technology architecture, governance, and even migration architecture. EA treats the business as an organic growing entity that evolves through a continuum of changes. Which should not be a surprise to most, given that people (organic by nature) constitute most of a business’s enterprise to begin with.

The key difference between traditional data migrations and migrations developed through enterprise architecture is that EA-driven migrations tend to be less complex and therefore more successful. EA-driven migration succeed because, by their nature, they are designed to succeed. Migrations are not an afterthought, a single one time event of irregular movements of data. Enterprise data migrations are designed in the context, not in leu of, an evolving business and its infrastructure and is the means to the businesses ends, not the ends itself.

Traditional data migration died a long time ago, we just never noticed it because we were too busy cleaning up all the failure debris. It is time for us now to start thinking differently so we don’t repeat this same migration mistakes. Enterprise Architecture-driven migration presents the best hope of success for those looking to continuously evolve their business model.

For more articles like this, please see LiquidHub.

These impressive, as well as amazing, statistics are now driving the way businesses are looking at social media.  Two years ago, you would have been hard pressed to find any large-scale corporate implementation of social media.  Today, companies like Best Buy and Jet Blue are more than leading the way; they have clearly defined how to perform at scale through transforming customer sentiment into profitable top-line revenue.

Social media is now turning into social business.  Through geographically-based push technology, businesses are looking over the next 12-18 months to Foursquare for their consumer-oriented products/services, proactively pushing real-time, instant-access sales.  With 70 percent of organizations banning access to outside social media networks, in 2012 we will see the mobile platform (smart phone, tablet) establishing its dominance as the connected mesh that keeps the social/business network together.

Social Media is more than just a trend.  What we are seeing is the emergence of stable and mature capabilities (business process and supporting technology) that are driving real, measurable business value.  It is an ideal time to start looking into social media strategies and policies, and to take advantage of the innovation and early adopter efforts that laid the ground work for Social Media 3.0.

Please check out other blog articles at LiquidHub.

The risk of human failure is possible in any endeavor, not with standing the work that I am doing to help a client plan for the migration of an enterprise claims management system. As part of developing an operational readiness plan, which spans two and a half years, we are developing a wide variety of governance characteristics which range from migration requirements to staging infrastructure to migration approach to business/risk assessments. It is a fairly comprehensive model for how, when, and where their business will be migrated.

As part of the process, we are now looking at some of the elements surrounding the human condition; specifically, the impact on productivity on the business during migration. Several studies have clearly demonstrated that there is a significant chance (30-50%) of decline in performance during periods of transition, during which new characteristics are being adopted. The question being address is how to deal with decline in a way that does not impact their clients. One can better prepare the employees which takes time or add additional temporary capacity (people) which takes money.

Our current thinking on how to address the impact on productivity is to create a Personal Operational Readiness program tailored to meet the individual capability maturity needs of each employee.  We are not only looking at different kinds of training (beyond the 3-5 day training programs), but training on operational data as well. When employees see their work in the new ontology, their productivity increases significantly, which is key for making the transition without adding large numbers of temporary employees.

More on this to follow…