Now you’re the Data Science leader, what next?

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One of the joys of our line of work is ‘match making’ and talking with happy successful candidates after they’ve started their new job. For those who are taking a step up with this job move, you hear a special kind of excitement, mixed with trepidation.

Let me start out by saying that the following is intended to be a helpful guide in respect of finding the right people for your business. There are many circumstances where you may need to deviate from the guide I provide and sometimes, it may be down to the level of maturity of your own business, your business plans, or other circumstances that flex the necessary package of desired skills. Use what I set out here as a starting point to think about what’s needed but remember to personalise it to your own specific need.

Many analysts or data scientists aspire to a leadership position, but it can be daunting when they get that role. Especially on your first day.

What do you do now? You’ve talked a good game to get here, so how do you ensure you make a positive impression in those critical first few months? Well, if you’ve been through this change, you’ll probably know better than us.

But, for any analytics leaders out there who are facing this challenge, I thought we’d share what we’ve heard works; from those we’ve seen go on to greater success.

Start with listening

It’s understandable that when we start in a senior role we feel the need to prove ourselves. But it can be a mistake to appear to arrive, with your own views already settled, and a passion to drive through your own agenda.

Chatting with the teams of new leaders, it’s very apparent that data scientists and analysts want to feel heard and valued for their expertise. Perhaps it’s worth reflecting on what you valued, from your leader, when you were working as an analyst?

A number of leadership books agree; effective leaders demonstrate first that they are keen to learn and hear from all stakeholders. Avoid the pitfall of being drawn in too early to committing to others agendas. Then, you can win a lot of future influence by showing you want to first understand, before you seek to be understood.

Know your goals

Plenty of leadership writers share regularly on the importance of goal setting, so we can only agree. Those we have seen achieve the most as analytics leaders appear clear on both their own goals and what the organization expects of them.

Having both appears to be a key. It’s such a help to work with job candidates who are clear on their personal goals, as well as the challenges they can tackle. Those effectively leading their teams have also told us how it helps to share both.

Data Scientists or Analysts like to hear how others have progressed in their careers and to feel they too have other goals (a whole life to balance with just work priorities). Leaders who share their goals often inspire their teams; to be clearer on what they want and need to achieve. Setting your goals in your new role is often a natural next step, after having met and heard from all your key stakeholders.

Identify quick wins

Whether the magic period is 30, 60 or 90 days, experts appear to agree that new leaders need to make a visible difference early on. So, after hearing others and setting your team goals, what next?

Leaders we have known to go on to build analytics teams of over 40 analysts, say the secret of their success was incremental ‘quick wins’. From keeping close to how the business is performing, what matters to other leaders and customer insights they know already – the skill appears to be in spotting what to tackle first.

Ideally, you want a lower risk challenge, that you are confident your team can achieve. But it need to be one that will be valued more than the effort required. Curiously, data and analytics are often still ‘dark arts’ to many businesses, so you may be surprised how much impact a simpler piece of analysis can achieve.

Experienced analysts often share with us, that the work that ‘made their name’ or ‘made a real difference’, was not their most technical. Often it sounds like it was not hugely complex. What mattered more was it was very relevant and others could see how to act upon it. Spotting one of these ‘sweet spots’ at a time and focusing your team there, that sounds like a recipe to build a great reputation.

Keep in touch with your profession

A leader’s life is full of meetings and when you’re not in those you have emails to catch-up on. That’s a common complain these days. Perhaps it’s even more frustrating for those technical leaders, who have built their careers upon mastering a profession (like data science).

Candidates who were initially thrilled to secure a senior role, within a well paying corporation, can become discouraged over the years. We sometimes hear those, who were once technical whizzes as analysts, complain that their lives are now full of navigating bureaucracy and governance.

So, how do data science leaders, who want to keep in touch with their teams and encourage technical excellence, stay up to date? The good news is that these days there are both tons of resources online (including analytics blogs) and access through social media to relevant communities.

MBN is delighted organize Scotland Data Science & Technology Meetup in Edinburgh and host events throughout the UK. Our events are well attended by a mix of students, entrepreneurs and leaders within business. They provide an ideal opportunity to network and have your thinking informed or even challenged.

It’s also good to see that we are not alone in encouraging such collaboration. Data Talent Scotland 2016 was a well received collaboration with The Data Lab and We Are The Future. Plus, in April we will be hosting our first event for the Data Science community in Berlin.

Whatever resource you use, or event you attend, it can be worthwhile establishing a habit of protecting time for your continued professional development. As Stephen Covey advised in the classic “7 habits of highly effective people” you also need to invest in yourself, or pause to “sharpen the saw” as he puts it.

What works for you?

Wherever you are at in your data science career, I hope those ideas were helpful. Do share what has worked for you. It would be great to get a conversation started on best advice for new analytics leaders.