- Strategies for Effective Data Leadership
- Data Leadership Challenges I Failed At So You Don't Have To! PART II
Data Leadership Challenges I Failed At So You Don't Have To! PART II
How to avoid the mistakes I made!
I hit you up with part 1 of this series, Data Challenges I Failed At So You Don’t Have To!
I discussed challenges I faced in my various data roles that I failed to find an effective solution for at the time. With the benefit of 20/20 hindsight vision, I now know how I would have addressed them.
For all you newbies, you can find part I here.
This week, I bring you part II (of 3)
Let’s go! ⬇️
1. Under resourced data teams
🏔 Challenge: It was always really hard to get budget in order to scale my team. Many times I’d be allocated budget towards the start of the planning process only to have that stripped away and reallocated elsewhere at the end of the cycle - usually going towards engineering teams.
📉 Impact: Having a data team that rarely grew meant that as the business scaled, it was extremely difficult to keep up with the demands on the team. We were unable to be proactive in contributing towards the business because we were always chasing our tail.
It made it hard to demonstrate the value of the team, when we were always struggling to achieve anything tangible or to value add.
👨🏼🎓What I learned: In people’s minds there is a clear correlation between the work an engineer does with commercial outcomes, but not so much with the work of a data analyst. Unless we can demonstrate tangible value from data and data initiatives, we’re unlikely to receive the necessary allocations. Thus, our teams won’t grow.
🚀 Today’s Approach: Back then I was afraid to drop the ball. Today, faced with an under-resourced team, that is the first thing I would do.
It is essential that a data team demonstrate value. This is often hard to do in a BAU situation were data teams are fielding requests from various stakeholders across the business. And this is the challenge that prohibits us from being proactive about discovering opportunities to discover and add value.
To succeed we need to be proactive. In order to be proactive, we need to prioritise the right things. Inevitably, this will mean saying no to things that don’t add enough value or help to demonstrate where the value lies.
If there is too much for us to juggle to do this, then we must be prepared to drop the ball. ⚽️
2. Data excluded from strategic decision making
🏔 Challenge: Compared to the other domains within business such as finance, product, engineering and marketing etc., data is relatively new.
Data teams have typically popped up inside organisations only in the last 15-20 years. Because of this, data teams and data leaders haven’t quite achieved the same level of reverence within the org as have those other domains. As a result, we are still excluded from strategic conversations and business planning.
📉 Impact: As long data continues to be seen as a technical discipline it will be hard for many business leaders to clearly see how data adds commercial value. The results of this is that strategies and ultimately targets are set without being sufficiently supported by data and insight.
👨🏼🎓What I learned: If data leaders and teams are excluded from strategy setting exercises, then it is highly likely that those strategies will be misinformed, and based on opinion and intuition rather than hard data.
This is bad news for any business hoping to compete in the current and future market.
🚀 Today’s Approach: Data leaders must fight tooth and nail to be included in strategy setting initiatives. To do this requires strong and influential data leadership.
There are 2 things the data leader should strive for in order to make this a reality. The first one is to deliver or champion data literacy initiatives across the business. These initiatives need to involve all key stakeholders and provide a heavy focus on how data informs strategy.
The other is to continuously demonstrate the commercial value that data provides at every opportunity. Every piece of analysis must be supported by recommendations for action and estimates for business impact from taking that action.
Check back in next week for part 3 where I look at how I failed in regards to:
Failure to educate stakeholders on simple data concepts, &
How I failed to handle data tool adoption.