12 Attributes of a Great Leader

"A managers output is the output of the organization under her supervision or influence." - Andy Grove


I believe that most managers want to be great managers. In fact, many aspire to transcend management and to be deemed leaders. While there are countless books on the topic, sometimes they are too much theory and not enough practice, to be relevant and applicable. One of the main roles of a leader is to teach; through actions of commission, actions of omission, and through a thoughtful dialogue. The goal of this series is to share what I believe are the hallmarks of great management.

In High Output Management, Andy Grove explores why, at times, an individual is not able to achieve their potential in a job. He simplifies it to one of 2 reasons: 1) they are incapable, or 2) they are not motivated. In either case, it's the responsibility of the manager to assess and remediate the situation. This is not comfortable, nor easy. Hence, this is why great leadership is difficult.

I will focus on what I think are the 12 defining attributes of a great leader:

1) Team builder- assembling and motivating teams.
2) Running teams- a disciplined management system, based on thoughtful planning.
3) Expectations, Accountability, and Empowerment - the #1 issue I see is here.
4) Being on offense, not defense- leading instead of reacting.
5) Engagement and influence- creating informal influence broadly.
6) Operational rigor- managing the details, without micro-managing.
7) Clear and candid communication - never leaving a gray area.
8) Training- a critical role of a manager.
9) Mental toughness- never talked about enough, yet many managers fail due to this aspect alone.
10) Strategic thinking- having a point of view, differentiated and right.
11) Obsessing over clients- knowing who pays the bills and applying it to every decision.
12) Positive attitude- Motivating by example.

I'll cover each topic via blog and/or podcast.

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2) Running teams-

The hardest thing for a new manager/leader to adjust to is being the pace setter. Once you assume the role of a leader, your job is to be on offense, not defense. I see even the greatest individual contributors struggle with this at times, because their success has been defined by doing everything that is asked of them. However, once you assume the manager role, you must become the one setting the direction and sparking activity. And, it can't just be activity for activity sake; it has to be thoughtful, pointed, and focused. This is the notion of a thoughtful management system.

In this podcast, I am joined by a great leader, Derek Schoettle, who was the CEO of Cloudant, before joining IBM via acquisition. We discuss how effective managers run teams, set pace, and foster open communication. 3 major topics are covered:

1) Committing to a course: No sudden, jerky movements and how to establish consistency in communication patterns.

2) The Rockefeller Habits: Set priorities (1-5), manage key metrics/data, and establish a rhythm.

3) Conducting 1-on-1's: Using formal and informal approaches to communicate for impact.

I hope you enjoy the podcast.

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5) Engagement and influence-

"Great leaders are relaxed when the team is stressed, and stressed when the team is relaxed."


I had a chance to talk with Jerome Selva about Engagement and Influence recently.

Podcast here.

We discuss:

- Informal Influence
- Getting comfortable in your own skin
- Tools for informal influence (blogs, videos, etc.)
- Looking outside your defined scope
- Emotional intelligence


In addition, Jerome shared the following for further reading:

Travis Bradberry and Jean Greaves — "Emotional Intelligence 2.0"

Emily Sterrett — “The Manager’s Pocket Guide to Emotional Intelligence”

Daniel Goleman "What makes a leader"

HBR article: https://hbr.org/2014/03/spotlight-on-thriving-at-the-top

EI test: http://www.ihhp.com/free-eq-quiz/

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7) Clear and candid communication -
I tend to say whatever is on my mind, as succinctly as possible. I believe it provides clarity (even if it’s not agreed with) and clarity leads to speed. Hence, I’ve always leaned towards saying exactly what I am thinking. I’ve had more than one person tell me, “you say the things that other people are thinking.”.

Now, that’s my style. It doesn’t mean it’s the right style or the only style. Everyone is different and should communicate in a manner that fits their style. That being said, I think one hallmark of leadership and management is being able to have the candid conversations and if necessary, delivering the uncomfortable truth.

In this podcast, I am joined by a colleague, Ritika Gunnar, to discuss the topic of Candid Communication as a manager and leader. Our conversation focuses in 3 areas:

1) Sharpening contradictions: the best managers identify disagreement in their team and tease it out. They know that letting it persist can create an unhealthy culture. It’s much better to get it on the table, even if it leads to a difficult discussion, than to let it lie in the background.

2) Don’t let problems linger: if you have a challenge with someone or something, speak up…put it on the table. If you let it linger silently, frustration and anxiety build and the trust amongst a team deteriorates over time.

3) Giving feedback: for many managers, it is very hard to give candid feedback, especially when it is negative or potentially confrontational. I believe that at their core, everyone wants to know the truth and where they stand. So, we discuss some techniques for how to deliver the harder messages. Your teams will thank you for it (sometimes many years down the road).

I hope you enjoy the podcast.



"In classical times, when Cicero had finished speaking, the people said, 'How well he spoke', but when Demosthenes had finished speaking, they said, 'Let us march'"- Adlai Stevenson

Pattern Recognition




Elements of Success Rhyme

The science of pattern recognition has been explored for hundreds of years, with the primary goal of optimally extracting patterns from data or situations, and effectively separating one pattern from another. Applications of pattern recognition are found everywhere, whether it’s categorizing disease, predicting outbreaks of disease, identifying individuals (through face or speech recognition), or classifying data. In fact, pattern recognition is so ingrained in many things we do, we often forget that it’s a unique discipline which must be treated as such if we want to really benefit from it.

According to Tren Griffin, a prominent blogger and IT executive, Bruce Dunlevie, a general partner at the venture capital rm Benchmark Capital, once said to him, “Pattern recognition is an essential skill in venture capital.” Griffin elaborates the point Dunlevie was making that “while the elements of success in the venture business do not repeat themselves precisely, they often rhyme. In evaluating companies, the successful VC will often see something that reminds them of patterns they have seen before.” Practical application of pattern recognition for business value is difficult. The great investors have a keen understanding of how to identify and apply patterns.


Pattern Recognition: A Gift or a Trap?

Written in 2003 by William Gibson, Pattern Recognition (G.P. Putnam’s Sons) is a novel that explores the human desire to synthesize patterns in what is otherwise meaningless data and information. The book chronicles a global traveler, a marketing consultant, who has to unravel an Internet-based mystery. In the course of the book, Gibson implies that humans find patterns in many places, but that does not mean that they are always relevant. In one part of the book, a friend of the marketing consultant states, “Homo sapiens are about pattern recognition. Both a gift and a trap.” The implication is that humans find some level of comfort in discovering patterns in data or in most any medium, as it helps to explain what would otherwise seem to be a random occurrence. The trap comes into play when there is really not a pattern to be discovered because, in that case, humans will be inclined to discover one anyway, just for the psychological comfort that it affords.

Patterns are useful and meaningful only when they are valid. The bias that humans have to find patterns, even if patterns don’t exist, is an important phenomenon to recognize, as that knowledge can help to tame these natural biases.


Tsukiji Market

The seafood will start arriving at Tsukiji before four in the morning, so an interested observer must start her day quite early. The market will see 400 different species passing through on any given day, eventually making their way to street carts or the most prominent restaurants in Tokyo. The auction determines the destination of each delicacy. In any given year, the fish markets in Tokyo will handle over 700 metric tons of seafood, representing a value of nearly $6 billion.

The volume of species passing through Tsukiji represents an interesting challenge in organizing and classifying the catch of the day. In the 2001 book Pattern Classification (Wiley), Richard Duda provided an interesting view of this process, using fish as an example.

With a fairly rudimentary example — fish sorting — Duda is able to explain a number of key aspects of pattern recognition.

A worker in a fish market, Tsukiji or otherwise, faces the problem of sorting fish on a conveyor belt according to their species. This must happen over and over again, and must be done accurately to ensure quality. In Duda’s simple example in the book, it’s assumed that there are only two types of fish: sea bass and salmon.

As the fish come in on the conveyor belt, the worker must quickly determine and classify the fishes’ species.

There are many factors that can distinguish one type of fish from another. It could be the length, width, weight, number and shape of fins, size of head or eyes, and perhaps the overall body shape.
There are also a number of factors that could interrupt or negatively affect the process of distinguishing (sensing) one type from the other. These factors may include the lighting, the position of the fish on the conveyor belt, the steadiness of the photographer taking the picture, and so on.

The process, to ensure the most accurate determination, consists of capturing the image, isolating the fish, taking measurements, and making a decision. However, the process can be enhanced or complicated, based on the number of variables. If an expert fisherman indicates that a sea bass is longer than salmon, that’s an important data point, and length becomes a key feature to consider. However, a few data points will quickly demonstrate that while sea bass are longer than salmon on average, there are many examples where that does not hold true. Therefore, we cannot make an accurate determination of fish type based on that factor alone.

With the knowledge that length cannot be the sole feature considered, selecting additional features becomes critical. Multiple features — for example, width and lightness — start to give a higher- confidence view of the fish type.

Duda defines pattern recognition as the act of collecting raw data and taking an action based on the category of the pattern. Recognition is not an exact match. Instead, it’s an understanding of what is common, which can be expanded to conclude the factors that are repeatable.



A Method for Recognizing Patterns

Answering the three key questions (what is it?, where is it?, and how it is constructed?) seems straightforward — until there is a large, complex set of data to be put through that test. At that point, answering those questions is much more daunting. Like any difficult problem, this calls for a process or method to break it into smaller steps. In this case, the method can be as straightforward as five steps, leading to conclusions from raw inputs:

1. Data acquisition and sensing: The measurement and collection of physical variables.

2. Pre-processing: Extracting noise in data and starting to isolate patterns of interest. In the fish example given earlier in the chapter, you would isolate the fish from each other and from the background. Patterns are well separated and not overlapping.

3. Feature extraction: Finding a new representation in terms of features. For the fish, you would measure certain features.

4. Classification: Utilizing features and learned models to assign a pattern to a category. For the fish, you would clearly identify the key distinguishing features (length, weight, etc.).

5. Post-processing: Assessing the confidence of decisions, by leveraging other sources of information or context. Ultimately, this step allows the application of content-dependent information, which improves outcomes.




Pattern recognition techniques find application in many areas, from machine learning to statistics, from mathematics to computer science. The real challenge is practical application. And to apply these techniques, a framework is needed.


Elements of Success Rhyme (continued)

Pattern recognition can be a gift or a trap.

It’s a trap if a person is lulled into believing that history repeats itself and therefore there is simply a recipe to be followed. This is lazy thinking, which rarely leads to exceptional outcomes or insights.

On the other hand, it’s a gift to realize that, as mentioned in this chapter’s introduction, the elements of success rhyme. Said another way, there are commonalities between successful strategies in businesses or other settings. And the proper application of a framework or methodology to identify patterns and to understand what is a pattern and what is not can be very powerful.

The inherent bias within humans will seek patterns, even where patterns do not exist. Understanding a pattern versus the presence of a bias is a differentiator in the Data era. Indeed, big data provides a means of identifying statistically significant patterns in order to avoid these biases.


This post is adapted from the book, Big Data Revolution: What farmers, doctors, and insurance agents teach us about discovering big data patterns, Wiley, 2015. Find more on the web at http://www.bigdatarevolutionbook.com

Ubuntu: A New Way to Work

“Teamwork and intelligence wins championships.” — Michael Jordan


There was an anthropologist dispatched to Africa many years ago to study the lives and customs of local tribes. While each one is unique, they share many customs across the geographies and locations. The anthropologist tells a story of how one time he brought along a large basket of candy, which quickly got the attention of all the children in the tribe. Instead of just handing it out, he decided to play a game. He sat the basket of candy under a tree and gathered all of the children about 50 yards away from the tree. He informed them that they would have a race, and that the first child to get there could keep all of the candy to themselves. The children lined up, ready for the race. When the anthropologist said “Go”, he was surprised to see what happened: all of the children joined hands and moved towards the tree in unison. When they got there, they neatly divided up the candy and sat down to enjoy it together. When he questioned why they did this, the children responded, “Ubuntu. How could any of us be happy if all the others were sad.”

Nelson Mandela describes it well; “In Africa, there is a concept known as Ubuntu- the profound sense that we are human only through the humanity of others; that if we are to accomplish anything in this world it will in equal measure be due to the work and achievements of others.”

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Read the rest on Medium.

Decentralized Analytics for a Complex World


In 2015, General Stan McChrystal published Team of Teams, New Rules of Engagement For a Complex World. It was the culmination of his experience in adapting to a world that had changed faster than the organization that he was responsible to lead. When he assumed command for the Joint Special Operations Task Force in 2003, he recognized that their typical approaches to communication were failing. The enemy was a decentralized network that could move very quickly and accordingly, none of his organizations traditional advantages (equipment, training etc) mattered.

He saw the need to re-organize his force as a network, combining transparent communication with decentralized decision-making authority. Said another way, decisions should be made at the lowest level possible, as quickly as possible, and then, and only then, should data flow back to a centralized point. Information silos were torn down and data flowed faster, as the organization became flatter and more flexible.

Observing that the world is changing faster than ever, McChrystal recognized that the endpoints were the most valuable and the place that most decision making should take place. This prompted the question:

What if you could combine the adaptability, agility, and cohesion of a small team with the power and resources of a giant organization?

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As I work with organizations around the world, I see a similar problem to the one observed by General McChrystal: data and information are locked into an antiquated and centralized model. The impact is that the professionals in most organizations do not have the data they need, in the moment it is required, to make the optimal decision. Even worse, most investments around Big Data today are not addressing this problem, as they are primarily focused on reducing the cost of storage or simply augmenting traditional approaches data management. Enterprises are not moving along the Big Data maturity curve fast enough:




While its not life or death in most cases, the information crisis in organizations is reaching a peak. Companies have not had a decentralized approach to analytics, to complement their centralized architecture. Until now.

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Today, we are announcing Quarks. An open source, lightweight, embedded streaming analytics runtime, designed for edge analytics. It can be embedded on a device, gateway, or really anywhere, to analyze events locally, on the edge. For the first time ever, analytics will be truly decentralized. This will shorten the window to insights, while reducing communication costs by only sending the relevant events back to a centralized location. What General McChrystal did to modernize complex field engagements, we are doing for analytics in the enterprise.

While many believe that the Internet of Things (IoT) may be over-hyped, I would assert the opposite; we are just starting to realize the enormous potential of a fully connected world. A few data points:

1) $1.7 trillion of value will be added to the global economy by IoT in 2019. (source: Business Insider)
2) The world will grow from 13 billion to 29 billion connected devices by 2020. (source: IDC)
3) 82% of enterprise decision makers say that IoT is strategic to their enterprise. (source: IDC)
4) While exabytes of IoT data are generated every day, 88% of it goes unused. (Source: IBM Research)

Despite this obvious opportunity, most enterprises are limited by the costs and time lag associated with transmitting data for centralized analysis. To compound the situation, data streams from IoT devices are complex, and there is little ability to reuse analytical programs. Lastly, 52% of developers working on IoT are concerned that existing tools do not meet their needs (source: Evans Data Corporation). Enter, the value of open source.

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Quarks is a programming model and runtime for analytics at the edge. It includes a programming SDK, a lightweight and embeddable runtime, and is open source (incubation proposal), available on GitHub.

This gives data engineers what they want:
− Easy access to IoT data streams
− Integrated data at rest with IoT data streams
− Curated IoT data streams
− The ability to make IoT data streams available for key stakeholders

This gives data developers what they want:
− Access to IoT data streams through APIs
− The ability to deploy machine learning, spatial temporal and other deep analytics on IoT data streams
− Familiar programming tools like Java or Python to work with IoT data streams
− The ability to analyze IoT data streams to build cognitive applications

Analytics at the edge is finally available to everyone, starting today, with Quarks. And, the use cases are extensive. For example, in 2015, Dimension Data became the official technology partner for the Tour de France, the worlds largest and most prestigious cycling race.


In support of their goal to revolutionize the viewing experience of billions of cycling fans across the globe, Dimension Data leveraged IBM Streams to analyze thousand of data points per second, from over 200 riders, across 21 days of cycling.


The potential of embedding Quarks in connected devices, on the network edge (essentially on each bike) would enable a new style of decentralized analytics: detecting in real-time, critical race events as they happen (a major rider crash for example), rather than having to infer these events from location and speed data alone. With the ability to analyze data at the end point, that data stream can then be integrated with Kafka, etc. and moved directly into Hadoop for storage or Spark for analytics. This will drive analytics at a never before seen velocity in enterprises.


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We live in a world of increasing complexity and speed. As General McChrystal described, organizations that rely solely on centralized architectures for decision making and information flow will fail. At IBM, we are proud to lead the decentralization of analytics, complementing centralized architectures, as a basis for Cognitive Computing.