Is your organization looking to build a world-class data analytics team but not sure where to start?  Perhaps you have already established a data analytics team (or data science team, if you prefer) – but they are not as successful as they could be?  You’re not alone!  Data analytics is a broad field.  Even for the most eager-to-be-data-driven companies, it can be overwhelming to decide how to create enterprise-wide advanced analytics and business insights capabilities.  Questions regarding identifying, hiring and retaining talent, resource allocation, team structure, alignment of data efforts and responsibilities abound.  It doesn’t help that little industry consensus exists regarding best practices on these issues.

At StrategyWise, we see companies striving to figure out what to do with all of their data.  They ask us “how can we build and operate a competent team of data analytics experts in a way that has a successful impact on our business?”

In this article, we offer you 5 tips we’ve seen proven to be helpful in building out your enterprise analytics capabilities.

  1. Create Your Data Vision and Strategy First

Do. This. First.  You can’t just hire a Ph.D. in Data Science and expect them to make data analytics happen!  Before you put out your first recruitment ad, before any budget decisions, you need to formulate a vision of what the data analytics team will achieve, why and how.   If you skip this step, you might find later that your data analytics efforts revert to a simple reporting function.

The way to create and put your data vision in place is to create a Data Roadmap.   This is where we spend much time working alongside clients helping them to answer questions such as:

  • What needs to be done with our data? How can we leverage data as an asset?
  • How do we get from basic business insights to advanced analytics?
  • Should we create a data analytics center of excellence or a decentralized model?
  • What is our current culture and can it become data-driven?
  • How will we scale data analytics capabilities across the organization?
  • What tools and technologies should we invest in?

There are many more questions to be answered during the road mapping process. You may not have answers to them all but having a clear vision will carry you a long way to finding them.  In fact, you will need to be able to communicate this vision clearly to attract top data analytics talent. Candidates will want to know where they are going in their roles, who and what they will be working with and why. They want to be challenged, and they want to see a clear path of progression in building out more and more advanced capabilities personally, and in their team.

Only when you have completed a review of existing data and capabilities, agreed on critical areas that need data analytics investment, and identified data-driven opportunities should you begin to think about building a data analytics team.

  1. Allocate Sufficient Resources

Developing a top notch, sustainable analytics team requires careful thought and agreement to commit significant resources to see the team through over time. It can’t be a one-quarter experiment. This commitment needs to be agreed on by all stakeholders and most importantly, executed.

During the road mapping process, if you can prioritize a data analytics project that will deliver value quickly, this will help to gain buy-in from executives to allocate requisite resources for the team build-out.

So what kind of resources should you think about?  First, technical data analytics talent does not come at a low price.  Carefully consider how much budget will be needed to recruit talent for the roles that you envision based on your roadmap. Can you pay top dollar for the best Data Scientists? Or should you contract out some roles initially? Or do you need to start from scratch and home grow your data analytics team over time by investing in ongoing training? Most firms we work with choose a hybrid approach – hiring or appointing a key internal team member to work with an outside firm in the early stages. As internal wins, and confidence grow, adding more resources becomes an easier sell to the budget committee.

Building a data science and analytics team in the enterpriseYour data analytics team will also need access to high-performance computing platforms, automation tools and specialized software tools. Your top talent will ask for access to expensive databases and online libraries. They will expect ongoing training and career development. And on a non-financial level, analysts will need time with senior managers or even external academics.

An oft-overlooked factor in resourcing is the need for continuity of data analytics team members.   Data scientists need continuity of colleagues and skills to become more effective, better performing and trusting over time.  Have you heard about the shortage of Data Scientists? Retention demands resources. Don’t kid yourself here. It is possible to hire new analysts with top skills, yes. But it is very costly to replace a seasoned analytics professional in whom the team has confidence. Plan for continued investment in career development and training, for example, to increase your chances of retaining your team.

  1. Enlist a Diversity of Complementary Skills

The saying goes “Hire teams, not unicorns.”   In media hype today, the Data Scientist is the unicorn. The fact is you cannot hire one Data Scientist who can do it all.  Data Scientist, Data Architect, Data Analyst, Data Engineer. These are distinct roles with different skills that you probably want on your analytics team.

The daunting task for HR is to sort through all those applications and diversity of qualifications.  Our advice is to aim to lay a solid foundation and avoid overly exotic skills profiles in your initial hiring.   Since we advise clients to go for quick wins early on to demonstrate ROI, we suggest hiring people who with no fluff can achieve that.   For example, an initial team could consist of a Technical Project Manager with 3-5 years of experience, a Data Scientist, and a data engineer who can code and aggregate data. Each person should understand the other team members’ roles at a comfortable level.

That said, for a data analytics team to be effective, you need to pair talented quants with people who have strong functional domain experience.  Why?  Someone on the analytics team should have the business and relationship skills to help frame analytical questions, identify problems and communicate the results effectively to management.  An example would be a Product Manager who has a keen interest in data analytics. Functional experts from Finance and Audit or Marketing who also work with numbers and data can be very useful.

Of course, data analytics requires a significant cross-section of technical skills that are hard to find in just one or two people.  You will need people with math, statistics, machine learning backgrounds who also understand algorithms and modeling. You can achieve this by hiring people who are strong in one area and have some understanding of others.  There’s no one right mix of skill sets, just make sure they complement each other.

  1. Create an Engaging Team CultureBuilding a data analytics team

The culture of your data analytics team is probably the most defining factor in its success.  Culture affects hiring, retention, and performance.  How do you incubate and sustain an engaging culture?

  • Provide a challenging and exciting environment

Data Scientists and analysts by nature seek out challenging and high performing environments, colleagues whom they can challenge with new ideas, and vice versa. They need to be able to engage with others in problem solving for serious problems. Your goal should thus be to assemble a high performing and dynamic group of people who are not afraid to disagree or entertain the most far-reaching of solutions.

  • Invest in retention

As we mentioned, the continuity of the data analytics team is very important. It takes time to build up intellectual trust in this field and to understand the many unique ways colleagues might approach problems. If a team member leaves, sure you can hire a new analyst with skills, but it will be hard to quickly re-create the team knowledge-base and trust that has been built up.  This is especially true if an experienced analytics professional leaves.  Serious investment in team retention tactics is far less costly than turnover.

  • Provide freedom

Data analysts and data scientists like to get their hands dirty and have a good degree of independence in their roles. These guys work really hard too.  Make sure they have some built-in downtime and flexibility on the job to pursue new ideas and research.  Analysts crave time to invest in their own processes and productivity.  Allowing this could pay off later in the form of on the job satisfaction, retention and innovative work that leads to breakthroughs.

  • Have analysts hang out together

It’s a simple thing, but in today’s networked organizations, not always obvious.  Team members should sit close to each other, not be spread across a building or organization. There are many nuances to working with data.  Frequent or rapid fire interaction between team members easily resolves the many many painful, trivial issues that come up in Data Science.  It also provides that dynamic environment they seek.

  1. Provide a Data Savvy Evangelical Manager

Executive management may not always value or understand the needs of the data analytics team. They are an expense.  The analytics team needs a manager, experienced in data analytics, who can advocate for the team within the organization.

At the same time, an analytics team will be most successful when the manager has direct experience with the nitty-gritty of data science such as messy data sets and poorly communicative business units. Someone who is not afraid to get their hands dirty.   To be respected and trusted as a leader by a top performing team managers should engage in research and other data-related activities to keep their finger on the pulse of data science.

A manager who is data savvy will also be able to develop the confidence in their team members to allow them to take the lead on things that matter to them.  They will be able to challenge team members on problems in a way that garners respect. This improves their job experience.

If you’re thinking that all of this will take a lot more time than hiring a couple of Data Scientists, you’re right!  Accomplishing all of this does not happen overnight.   You need to take a 2-5 year perspective to strategize, find resources, recruit and train, engage and solidify your data analytics team and your organizational processes.   It isn’t easy, but it can be achieved with the right amount of planning and commitment.

And once embedded in your organization, a high-quality data analytics team and capability can help your organization achieve a higher level of performance and become one that uses analytical insights as part of the normal course of business.