Russ Pearlman's Blog

Massively Parallel Processing: A Foray into Big Data

As it’s name implies, the company Teradata was founded in the late 1970’s with the idea of supporting terabyte-sized databases. Coming out of research from Caltech[i], the core idea was to develop a specialized database solution that relied on parallel processing. Essentially, Teradata was looking to horizontally scale the database using the idea of a connected set of servers managed by a software layer that would automatically break down database problems. The idea behind this solution, know as massively parallel processing (MPP)[ii], solved the complexity of application and database architects having to solve the partitioning strategy. Eventually, Teradata introduced a complete end-to-end solution known as a data appliance that incorporated the hardware, software and storage encapsulated into a single solution to handle very large databases (now up to petabytes in size)[iii].

Today, there are a number of competitors to Teradata including IBM Netezza, Oracle Exadata, EMC Greenplum, HP Vertica, Microsoft SQL Server Parallel Warehouse and Paraccel. All of these solutions are based off of the MPP architecture and provide various configurations from software only to specialized data appliance hardware. IBM Netezza[iv], for example, provides a database appliance that uses specialized blade hardware and tailored integrated circuits to increase the performance of data queries.

How Merkle Uses Massively Parallel Systems to Go Big

In the evolution of building more robust marketing databases, Merkle has deployed numerous MPP databases ranging from just a few to over a dozen terabytes in size. These systems incorporate numerous data feeds (in some cases well over one hundred) including demographic information, promotional data, sales transactions, web feeds, model scores, event registration & attendance, social profiles, research, 3^rd party data (including credit bureaus), media performance and more. The value in using these MPP appliances is that they are able to ingest larger amounts of data, yet still run faster than traditional RDBM-based systems for analytical tasks such as creating attribute aggregates, performing analytical scoring and supporting business intelligence reporting.

Better Campaign Management and Execution

As an example, one of our major retail clients uses an MPP platform to significantly reduce the end-to-end campaign lifecycle (by 50%-70%) and improve marketing performance. They are able to accomplish this by leveraging the scale and speed of an MPP-based appliance to:

• Leverage a single instance of the data within the appliance (for analytics, reporting and campaigns) that includes calculated fields needed (as opposed to ad-hoc creation of aggregates across multiple data marts)
• Use a more comprehensive source of data to drive 20-35% response and revenue lifts across marketing campaigns
• Store increased loads of data including store / e-commence purchase history, contact history, web traffic and email
• Process campaigns faster by better managing campaign cadence to create better control groups for identification of incremental marketing opportunities yielding up to $10 million in operating income

Multitenant Solutions

Aside from processing large data sets for individual clients, Merkle is also using MPP technology to host multitenant solutions for digital and customer data integration. (The concept of multitenancy is core to cloud-based solutions as well to help drive cost efficiencies.) The sharing of space helps to optimize use of the environment and provides savings by not having to procure two sets of systems. Without the scale and performance of these tools, such an arrangement would not be possible. This has especially been true in the collection and management of digital data that often transfers as multiple gigabyte files that can be processed down into more manageable data sets within the data appliance itself.

In-database Analytics

Another big advantage that we’ve seen with MPP vendors is the introduction of in-database analytics. The traditional approach to performing analytics and scoring of a customer segment is to collect the data into a single database, overly with 3^rd party data, extract a sample customer file, create models / scores on the sample data set, finalize the models, extract the entire data set, run the scoring against the entire data set and re-insert the database back into the database. All of that back and forth of data movement is completely wasted, and the speed and performance of running those scoring algorithms against millions of customer records can be slow.

Compare that approach with to keeping the customer records within the MPP appliance and, after finalizing models against a sample data set, running all of the scores within the database itself. For one of Merkle’s medical equipment supply clients, we ran a speed comparison between their traditional approach and then using in-database analytics:

Beyond SQL – NoSQL

Aside from MPP solutions, there is also a whole set of technology colloquially known as NoSQL[v] (or “Not Only SQL”). Unlike relational or MPP systems, these solutions primarily use the concepts of key value pairs to store data instead of a structured schema. These key value pairs (for example, <“city”, “Columbia”>, <“city”, “New York”>) are then grouped and queried just using the key structure. This allows for extreme horizontal scaling because keys can be easily partitioned across servers. Additionally, by being designed around the concept of horizontal scale, NoSQL solutions can take advantage of commodity (i.e., cheap) hardware for each of the individual nodes. Where this also gets very interesting is in leveraging the use of cloud or virtual machines to scale the solution. That means that the starting costs of a NoSQL database system can be quite low yet scale to very large sizes by adding in additional server nodes over time to meet the performance needs. As an extreme example, Facebook has been able to scale their NoSQL database to over 15 petabytes of data across more than 2 thousand server nodes[vi].

While there are a number of impressive case studies and companies that are using NoSQL-based technologies, the commercial offerings in the space are still quite new and immature. The primary organizations providing these tools are open-source foundations such as Apache. The biggest names in NoSQL are Apache Hadoop (using MapReduce and HBase), Apache Cassandra, Project Voldemort and MangoDB. Though open-source, there are major Internet company players behind these solutions including Facebook, Google, Twitter, Amazon and Yahoo! that provided a lot of the initial research and development of these systems and continue to support their evolution. Additionally, foundations such as Apache are backed up by major commercial organizations including IBM and Oracle that provide dedicated developer resources.

The Changing Face of Data

The challenge is not only the amount that data is growing but the type of data is changing as well. Traditionally, computer information systems are really good and collecting, processing and analyzing structured data – information that can be described using a structure or schema. The earliest versions of these were known as flat models, originally used by punch-card systems and later mainframe programs, to structure data into fixed-length fields[i]. (These models are still used today in what people call “flat files” that are often used to transfer data between systems.) Today’s modern systems now primarily rely on relational models that structure data according to tables of data (made up of rows and columns) and the relations between them. These are stored in major database systems including Microsoft’s SQL Server, Oracle’s RDBMS and IBM’s DB2.

However, the emergence of semi-structured and unstructured data is fueling much of the Internet’s data growth. An example of semi-structured data is an email – it includes structured elements such as from, subject, date and content. However, the message itself can contain anything the user wishes in whatever format they want. Unstructured data examples include pictures, videos, phone conversations, text messages and Tweets. While structured data can be more easily analyzed, semi-structured and unstructured data is more open to interpretation and is more difficult for computer systems to manage. (Just think how hard it would be for you to answer questions about the document you are reading compared to a table of sales statistics.)

This is especially relevant to marketers. The rapid increases in consumer generated data includes on-line behaviors including participation in social networks, mobile searching (which now includes location-based data), targeted display ads, data integration across e-commerce / web-sites and digital messaging including email, SMS and texting. As a great example, researchers at Northwestern University used time to respond to emails to glean information about social closeness between users[ii]. The shorter it took to respond to an email, the closer their research showed the connection. Are you collecting that type of information? There’s a lot of data out there, and there’s a lot of work to fully analyze and understand it.

Today’s Approach to Storing and Processing Data Wasn’t Built for This Explosion

Our heavy reliance on relational data models was built for a different world. In 1970, E.F. Codd wrote his seminal paper[iii] that first described the concepts behind using relational models for data storage. He was primarily concerned about wasted disk space and faster searching of information within larger data sets (larger being relative as Codd only had to dea

l with kilobytes and megabytes of data). This was at a time when computer resources were expensive and efficiency was extremely valuable. For example, Intel’s first commercial chip (released in 1971) was capable of 92,000 operations per second compared with today’s Quad-core i7 chips that are capable of 177,730,000,000 operations per second[iv]. Storage costs are another area that has seen amazing efficiencies. In 1971, IBM disk drives cost $17,000,000 per gigabyte in today’s dollars[v]. Today, the cost is under $0.10 per gigabyte[vi] and declining quickly.

The building blocks to solving the problems outlined by Codd (matched with the reality of how expensive computers were back then) were to centralize the data store and eliminate as much redundancy of the data set as possible. This helped to speed up searches and ensured data integrity. Yet, forty years later, with vast increases in the amount of data and shrinking costs of computer systems, we still rely on these 1970’s innovations to manage our data.

An implication of this is seen in challenges related to how we scale our relational database management systems (RDBMs). The two major approaches to scaling computer systems are vertical scaling and horizontal scaling:

• Vertical scaling refers to the ability to add more scale to a single computer node by upgrading things such as the processing power, amount of memory or hard-drive capacity. Think of waiting in line at the grocery store, this approach parallels making the checkout process faster so that people in line behind you wait less.
• Horizontal scaling refers to the ability to add in additional nodes to manage the workload required of the system. Going back to our grocery store analogy, this approach is similar to adding in additional checkout lines.

Relational database systems have often relied on vertical scaling requiring expensive hardware and hitting pragmatic limits in what a single computer is capable of processing. Horizontal scaling, while sometimes being more complex, can scale larger and typically costs less. But, since RDBM systems were originally built on a single computer assumption, they aren’t as amenable to horizontal scale.

As a strategy around this, database architects and administrators have implemented a number of work-arounds to find ways to scale horizontally. One approach is to use a master-slave architecture that uses data replication; essentially, data is pushed to others servers that can be used for read-only operations like reporting. Another approach is to use partitioning strategies such as list partitioning that segregates data across databases (e.g., by country, by first letter of the last name, grouping by zip code, etc.) This allows a degree of horizontal scaling, but there are several significant drawbacks:

• Often, its up to the developer of the database system to make a choice on how to partition the data. While this strategy may work in theory, practice may prove otherwise. By putting the onus of the strategy on the application layer itself, the decision has to be made prior to building the solution, and if production performance demonstrates that the wrong strategy was selected, it will require a fairly significant re-design to mitigate.
• The partitions themselves are treated as separate data stores. That’s good news in terms of scale and performance, but the challenge is that if you want to combine information across databases, you have to do some pretty computationally expensive joins across system boundaries. That means that faster performance can suffer in the name of scale.
• The management and costs can be expensive. Relying on commercial vendors to provide partitioning in more seamless and manageable ways requires an enterprise suite of tools, technologies and expertise. Building and managing those environments can take a lot of resources both in terms of licensing and people costs.

While these various strategies and work-arounds have resulted in RDBM systems to scale to truly impressive levels, the onslaught of data and unnecessary complexity has brought us to an inflection point: managing for “Big Data”.

In today’s world, making effective decisions depends on having good information at your fingertips. But as our ability to collect and analyze vasts amount of this information has grown over the past decate, our capability to effectively use this information hasn’t sufficiently matured. It’s very likely that the big investments in the collection and storage of this data isn’t paying off in better decision making.

Yet, while we struggle with that gap today, the pace of data continues to accelerate. There are now more than 2 billion users of the Internet[i] accessing and generating vast amounts of data. According to Cisco’s most recent Visual Networking Index[ii], Internet traffic increased eightfold over the last five years and will increase another fourfold over the next five. They estimate that by 2015, annual Internet traffic will approach one zetabyte. That’s a staggering amount of data. The gap will continue to grow.

To put that into perspective, let’s start smaller with a petabyte. A petabyte is 10¹⁵ bytes or 1 million gigabytes – capable of storing about 350 million MP3 songs[iii]. Using Gracenotes[iv] as an estimate on the total number of songs available (around 97 million) and the release of about 50 albums (or 500 songs) per week, it would take almost a thousand more years to have a petabyte of professionally recorded music. And a zetabyte is one million petabytes!

Figure: How Big is Each Byte

The reason why this is so problematic to on-line marketers is that it continues to underscore the one-to-one marketing “data treadmill” – no matter how much data you collect about a single customer or potential customer, there’s always more to collect as their “digital exhaust”[vi] continues to expand in size and scope. While some of today’s largest marketing databases range into the terabytes of data, future database will need to expand significantly. But the emphasis will continue to be applying “big judgment” to “big data”; collecting more data and throwing it at existing processes is just throwing gasoline at the fire.

A colleague of mine passed on a link to an article: “ITIL vs. The Cloud: Pick one”. The main premise of the article is that ITIL (which is an IT focused control framework for managing IT systems) and Cloud (dare I define parenthetically as outsourced IT services typically providing software as a service capabilities).

While I think the article is provocative enough, I’d like to pile on – the people who are often pushing the most for cloud technology are often the least empathetic to what IT actually does. When you see IT as slow and expensive, you can easily be seduced by the idea of completely outsourcing business supporting systems to cut IT out, with the immediate reward of “it just works” type of thinking. (Everyone seems to believe that the other guy’s IT is better than my own. Having been in many organizations, be careful what you assume.). And, frankly, it’s often hard to argue that there aren’t some really great opportunities for SaaS to have major break-throughs. For example, I’m a big proponent of Concur, which is a travel and expense SaaS tool that I’ve been using for a long time. Equally, Salesforce.com has shown how CRM can be extremely effective in a cloud-based configuration.

But, as I often point out, it’s rarely the big applications themselves that are the hard part. What’s hard in IT is system integration, monitoring/operating things in connection with each other, managing data and dealing with unexpected situations. Cloud doesn’t solve the integration problem unless you completely migrate all systems onto a single platform. Cloud doesn’t solve monitoring/operating because it doesn’t get rid of all systems – and it perhaps makes it worse because you now have off-premise systems that you aren’t able to understand more fully what may be going wrong (i.e., is it a network failure, too much data, etc.) While there is some movement here, it’s still pretty much a black box. Managing data isn’t any better – it still exists in spreadsheet, databases, enterprise systems and cloud systems. As a matter of fact, cloud also often makes this worse as limited data format customizations within the cloud systems means that you have to implement additional ETL capabilities to make it fit the platform. And, finally, it doesn’t address unexpected errors – that can still happen across the organization and even in the cloud (or connecting to the cloud) as well. And, as the article points out, unexpected errors could now be coming from your SaaS provider who makes updates to their software without any typical production communications. The mantra “there are no migrations or versions” is sexy to the end user until all the data connections feeding the system breaks. (In computer science, we call this a “side effect” – which like the military equivalent “collateral damage” – sounds really innocuous until you realize that there is literally nothing you can do about it until you resolve the root cause. And, do you suspect it will be your cloud vendor stepping up to admit that it was their change that caused your problem?)

As a good example of this, I was at Dreamforce (the Salesforce.com conference) earlier this year where I listened to a team discuss a “success story” of building an application on Force.com. They called it a success because the team was able to build a ground up application in 9 weeks that pulled in data from numerous sources to  provide visibility to multi-level-marketing commission values. As the topic went on, though, it was noted that the data was actually fed from a corporate data warehouse that integrated all the systems into a single view which was then forwarded to the Force.com platform. Essentially, they had created a fairly straight forward non-transactional visibility system that relied on heavy duty IT support and integration to make function. If the underlying data warehouse went down or the integration between the systems broke, the application itself would go stale with data and be unusable. So, of course, this solution still requires IT support. (Perhaps that’s why salesforce.com changed its motto from “No IT” to “No software” – or perhas they were picking fights where they should have been building bridges.)

From my perspective, cloud should be seen not as everything but as another tool in the belt of solving business problems. There are a number of situations where it’s a great fit today. And, I expect that to conitnue to expand as more investments are made in the space. But, equally, it should not be seen as the solution to all problems, and, IT especially, needs to continue to deal with governance and processes like ITIL.

And, as a proponent of cloud concepts, I’d also like to warn IT that this point of view should not be used as a weapon to defend the status quo. Cloud is upon us, and we need to take advantage of it however we can – just like the introduction of any new technology that can lower costs, make us faster and deliver more value. Don’t be afraid to roll with the change…

In his article The Case for a Chief Marketing Technologist, Scott Brinker argues that marketing should hire a technology professional within the marketing organization who understands the end-to-end technology tools and trends. I completely understand why Scott believes this is a good idea, but I think he is making a fatal assumption – that IT as a centralized service to an organization is too slow, too costly, too difficult to work with and has no understanding of marketing. Unfortunately, in too many companies, that’s exactly the situation.

However, I believe that his argument is myopic. If marketing makes technology decisions in a vacuum, you miss a great opportunity to pull in information from across the organization or even share your information with others. For example, about five years ago, I was working with one of the world’s largest automotive retailers. The customer information systems were completely siloed between sales and warranty service – which caused numerous customer service challenges. By fully integrating these systems, we were able to provide seamless customer interaction across the organization all the way from marketing through sales through service – with much happier customers the result. This project required the cooperation of multiple business units including automotive, parts, warranty and sales.

Secondly, by putting a Chief Marketing Technologist in the organization itself, you create shadow IT that tends to prioritize its own needs independently. While this may accelerate things in the short term, it can really cause headaches in the long term. Often, as departmental solutions grow is size and complexity, IT needs to be brought in causing big transitional hardships such as costs, downtime and loss of functionality.

I believe the better answer is for marketing and IT to forge stronger alliances. When selecting tools, engage with IT to support the process so that they can worry about things like security, integration, performance and cost. When looking to implement a new project, work with IT to see if other sources of customer data can be pulled in for analysis. Of course, this is a two way street. Today, too many IT shops are ineffective and have little understanding of marketing. CIO’s should be pushing IT to become more business focused. If they aren’t, you can always initiate the conversation yourself – I’m not sure I’ve ever witnessed a close minded IT shop when business users are trying to treat them as partners.

At Merkle, we are often in the juxtaposition of marketing and IT. We make it our job to understand the business of marketing and provide value. We also make it our job to understand IT to make use of best in class solutions through the efficient use of technology systems. However, we find ourselves more and more engaging directly with senior technologists from IT at our client’s companies who see value in the services we provide and the data they could use across their organization. Finding ways for our clients to blur the lines between sales, marketing, IT and all other groups focused on the customer is what we strive for. It’s what all marketers should be striving for themselves…

I was just in Chicago to attend the Alterian Engaging Times summit. The attendance was great, and there were some impressive brands represented including Dave & Busters, Western Union and many others. The theme of the summit was the rise of social marketing and how to best engage customers.

The keynote address was given by Stan Rapp, an industry veteran – and quite a character to boot. I really liked a lot of what he had to say - but I think the most valuable part of the message was related to companies doing things “to” their customers, doing things “with” their customers and doing things “for” their customers.

Doing things “to” your customers are the typical horror stories that get posted all over the Internet. A company didn’t take care of me, made a mistake or just plain didn’t make me feel that they valued me as a customer. I know I’ve had that recently myself with such brands as Marriott, DirecTV and AT&T.

Doing things “with” your customers are ways that you engage the community. For example, Alcatel Lucent donates use of their campus for the American Cancer Society’s Relay for Life every year in Plano. This makes you feel good about the company, and it makes you feel connected.

Doing things “for” your customers are incentives and/or other special things you can do to provide value. For example, Nike provides information on running events and posts run times for users in training. American Express provides services for making hotel reservations and concert ticket purchases. Chik-Fil-A provides all sorts of free meal options when you engage with the brand (including dress like a cow day).

I think one of the simplest things that a company can do to move into this “for” group is just listening. Best Buy had a great idea with TwelpForce which is a program where Best Buy listens to Tweets and provides instant support / feedback. How often do you find yourself trying to call customer service, send an email or post to a company’s web site and not feel like they understand your questions / comments  / complaints? The simple first step of acknowledging a customer and responding to them really moves a company dramatically up that chain.

David Williams, Merkle’s CEO, often challenges with the question on how you can spend a large advertising budget in social media. For traditional media, it’s as simple as buying print ads, direct mail fliers, radio and TV commercials. Part of the answer, I believe, is moving from a unidirectional medium to bi-directonal. To do that, you need to integrate your online systems (i.e., web-sites and customer information systems), your support systems (i.e., call centers) and your marketing tools (i.e., campaign management, micro-sites, social presence, etc.) In this new world, it isn’t about buying lots of eyeballs – it’s about crafting the message and engaging in a conversation. It’s not a one time investment, it’s a long term investment that really changes the game.

I know that it’s been a while since I updated my blog. The biggest reason is that I’ve taken on a new position at Merkle as the Chief Technology Officer.

Since Merkle isn’t a well known company outside of the Database Marketing environment, it’s worth spending a bit of time describing what Merkle does. Since it’s foundation in the 70’s, Merkle has traditionally focused on helping companies reach their customers. In the most recent incarnation, that means helping marketing organizations to be most effective in understanding their customers, how to effectively execute campaigns and how to measure the results of those efforts. Now with more than 1,200 employees and over $250 million in revenue, Merkle is embarking on its next generation focused on end-to-end, integrated marketing optimization.

And, that’s where I fit in. My background has been on custom application development and integration with a focus on new media and digital channels. The next generation of technology for Merkle extends beyond its existing strengths in developing customer databases, analytics and campaigns (including direct mail, email and mobile). It’s a stronger reach into operational data, digital asset management, marketing organization effectiveness and new aspects of customer interactions including social. While I was at Capgemini, one of the hardest questions we had with our own clients was how to monetize these digital channels. At Merkle, I’m working to help answer that question – along with some of the biggest and best brands in the world.

I’m really excited about the opportunity…

A repost from my guest blog appearance from the Ericsson Televisionary blog site.

Capgemini Conulting just released a report together with In-Stat where we estimate that, by 2013, about 46 million households will use a connected Blu-ray player, video game console or a media enabled PC to stream video from the Internet to a TV set. Overall, the data from the report points to a strong and sustained growth pattern for the electronic entertainment industry. Central to the report’s findings is an identified US$10 billion in revenues that do not exist today—but will be on the market for electronic entertainment by 2013. Other key findings from the report include:

• By 2013, 93 million US households will have broadband (up from 72.9 million in 2008) • Saturation of media enabled PCs connected to TV sets is expected to reach 45.2 million US households by 2013 (up from 18 million in 2008)
• Pay-TV video on demand services are expected to provide over US$ 2.6 billion in ad revenues to the Pay-TV industry in 2013
• In 2008, the market for advertising delivered through online video services was nascent. By 2013, Capgemini and InStat expect this market to grow by over 1400 percent—up to about US$ 1.8 billion.

To download the entire report for free please click. To learn more about innovative Digital Content Services that are helping define, shape and implement new business models for companies in order to capitalize on this new market growth, please click for a free whitepaper.

So, Chitika Labs has a relatively scientific way to track iPads sales by looking across their ad network looking for iPads that are browsing the network. Pretty good idea, and it seems fairly reasonable to me. As of this time, they are tracking North of 850,000 iPads out there in the wild with an uptick of about 1 every 3 seconds (if that rate keeps up, that represents about 10M iPads per year). Interestingly, they also have some information related to usage by state (thus my quick calculation of 25,000 iPads in Wyoming).

Now, I’m not trying to continue throwing cold water on Apple, and this is now my third posting on the iPad, but I think that these preliminary numbers continue to reinforce my estimate of about 4.5M iPads that I think will ultimately be sold. I think that the strong out of the gate “fanboi” buyers boosted the original population, and I suspect that update will slow down quite a bit. When the 3G versions come out, there will certainly be another major uptick, but I don’t expect that to be huge.

Anecdotally, I’m hearing a lot of great things about the iPad, and I’m even hearing about “notebook” replacements, but I don’t think that is realistic. It’s a good device for simple email, browsing and viewing Microsoft Office documents. But the more advanced capabilities of a laptop is still essential.

I just finished reading Walk the Walk by Alan Deutschman. It’s a good short read, but it does highlight a few important things that I think are absolutely true of leadership. First, keep it simple – let everyone know where you stand and where your first 2 or 3 priorities lay. Second, there is not such thing as a typical leader.

As for the first point, its not only important to focus on the key few things that drive you, but you have to be consistent and actually follow what you said. People rarely respond to words – they respond to actions. When you say one thing and do another, they lose faith in you as a leader. When they see you say something and actually do it – they are inspired. Recently, I was involved in a company internal meeting where we reviewed the fact that many of our delivery projects were under performing from a delivery perspective (financially mostly). What were the root causes? Well, for one, while we tell everyone that delivery is important to us, in reality, we are focused on sales and revenue as our KPIs. Given this emphasis (and clear demonstration on how important it is), our ‘walking the walk’ resulted in some very good sales – even in a poor economic climate. The down side, of course, is that we’ve let slip a few fundamentals of our delivery capability. To fix it, we can’t simply start communicating on how important delivery is (which is what I heard our leadership say was step #1). But, we have to start ‘walking the walk’ and show our teams that there is teeth behind this – by rewarding delivery, by emphasizing it more often and by judging our people based on it.

I think the second point is just as critical. Often, people see leaders that they respect and try to emulate them – often at their own peril. We all have natural abilities and ways of thinking. Trying to channel those based on successful role models is a good thing, but trying to dramatically change your persona based on what you “think” a good leader is will end up causing you to ‘talk the walk’ but ultimately end up in trouble. Is Bill Gates a successful leader? Yes. And Steve Jobs? Yes. Are they almost totally different people? Yes. A good lesson here is to be yourself, find your true principles and follow where they lead you.

And, one final note – the book decides to end with a case study on Barack Obama. Unfortunately, the book was published well in advance of his greatly declining public opinion polls. While Alan Deutschman appears to be a big fan of Obama (not sure if it’s his policies or leadership skills), he ultimately further underscores his own point. Obama has continued to be a model of “talking” not “walking.” When he promised transparency but didn’t deliver – he lost leadership credibility. When he promised the end to partisanship but didn’t deliver – he lost credibility. Barack Obama has a tremendous number of natural abilities that makes him a potentially transformative leader – but his inability to “walk the walk” is turning him into just another politician.