Chapter 5. Building a Winning Team

As we have discussed previously, Big Data brings many new disciplines and capabilities to your business. Many organizations attempt to organize Big Data initiatives in traditional structures and use existing people's capabilities with some training augmentation. In Chapter 3, we went into great depth to understand how Big Data projects are different from normal technology projects, and we worked on developing unique project management frameworks. In line with that, your people practices also need to change for Big Data initiatives. Our experience shows that most Big Data projects fail due to incorrect team composition and interplay between team members.

To succeed in your Big Data initiatives, you must bring the right people together in a structure. This chapter provides guidance on how to build teams to pursue Big Data initiatives and make them work.

Understanding the distinctive skills you need

Successful Big Data projects require many skills to come together in delivering them—statistical, computational, data management, infrastructure management, business process, behavioral, change management, and communication skills. As we progress through this chapter, we will be reviewing these skills in more detail. In the earlier chapters, we discussed that delivering a Big Data project requires you to bring together an eclectic group of people. For the team to work effectively and deliver results, you want almost all of them to demonstrate some common traits such as the ones below:

• Business knowledge: People in your team need to understand how your business works, in particular, how it makes money, which business processes and operational processes contribute to revenue and profits, how people's actions impact these processes and their outcome, what makes your business successful, what challenges your business is facing today and is expected...

Defining the team and structure

Now you have a good picture of all the different skills you need in your Big Data team. Make sure you have a clear idea of what role you personally want to play, which could be either one of these or a completely different one. This will help avoid any future confusion or role proliferation.

The next most important question is when to assemble the team. As soon as you decide or have a strong desire to pursue Big Data initiatives, and have started identifying Gold Coins, get started on building the team. You must have your team before you invest significantly in the technology and infrastructure. You need to start by identifying the people who fulfil the requirements of the various profiles. Scan the people currently employed by your organization to see whether anybody matches up to the expectation. We have seen many instances where people with the required skills are tucked away in some other profiles in some other parts of your business. Once you get executive...

Building an extended ecosystem

You have big plans for how you want to use Big Data to improve your business. If you are planning to accomplish all your goals with your committed but small team, you are in for a big challenge; you may end up short staffed and under-skilled and will slowly get frustrated by the lack of progress as per your expectations. For additional help in getting specialized skills, expert guidance, and expediting your goals, you can build an extended ecosystem and draw upon from the skills and knowledge of that ecosystem. Most companies with successful Big Data projects have leveraged such external capabilities.

Building such an external network requires very different approaches than building an in-house team. Taking advantage of such a network effectively requires considerable effort from your end. In this section, we shall discuss some ideas around building and using an extended ecosystem to further your Big Data agenda.

Educational institutes

In most countries, leading...

Improving team alignment and performance

In the initial days, your project team for Big Data Analysis is most likely to be a small one. It is critical for your team to be well aligned with each other and towards the common objectives you have set. Even for larger teams, this problem still remains. You can take a number of steps towards this goal. We will now discuss some of the high impact ones in this section.

Training and orientation

One of the first steps to bring everybody together is to conduct a common set of training and orientation programs. This will help them speak a common language and understand each other better. Some of the key topics you should include in this program are as follows:

• Statistics: Many of us have taken Statistics courses in our schools will remember most of these concepts and use a few of them from time to time. It is unlikely that we will have a deep familiarity with the theories and formulae, and how to apply them on large problems, unless our jobs required...

Motivating your team towards progress

By now, it is clear that your Big Data team has a critical role to play in your business' future. You want them motivated and at their peak performance to help you drive your business towards growth. These people are unique and need different treatment to motivate them. The right people in your Big Data team will demonstrate some common behavioral traits:

• They will not be bothered by organizational hierarchies, even though they will show respect for authority and structure

• They will not get too excited by normal motivational incentives such as financial or positional rewards; they will be more excited by the opportunity to solve complex analytical and business problems

• They might be reticent at times, but they like to share and collaborate and be generally helpful to others in the right environment

The traditional motivation theories do not work well with Big Data teams. They live by a slightly different creed. These people are precious and they know it...

Summary

In this chapter, you focused on organizing your team to lead you down a path of success. You identified the distinctive skills you require for your project—data scientists, experimental analysts, application developers, infrastructure specialists, change leaders, and project managers.

You identified the unique experiences and expertise you need in these profiles in the context of Big Data projects and where to source them from. You then worked on how to put your team together and what kind of structure to put them in.

Next, you learned about how to build an extended ecosystem comprising academic institutes and consulting organizations to accelerate your Big Data initiatives. In this discussion, you worked on defining the right engagement framework and some best practices to make the engagement successful. You then explored techniques to improve your team alignment and make them more productive through training, quick-wins, and innovative leadership models. Finally, you worked on topics...