5 Problems That are Killing Data Leader's Effectiveness

Being a data leader today is hard.

Not only are there major misconceptions around the role and purpose of data in many organisations, but data leaders themselves lack the ability to effectively influence their orgs in the right direction.

I’ve identified 5 specific problems that undermining a data leaders' ability to be effective in their roles as well as what you can do about them!

Let’s explore:

🙅🏼‍♂️ 1. Companies saying they are data driven when they’re not

PROBLEM: Data Driven is an overused and misunderstood buzz word. In most cases, companies that call themselves ‘data' driven’ simply mean that they have data available to look at via dashboards.

Whether they are truly data driven will come down to how effectively data plays a role in the evaluation and decision making process - something which is much rarer.

IMPACT: Companies thinking they are data driven when they aren’t, will stifle investment in data and hold data leaders back in both their infuence and they career progression.

WHAT TO DO ABOUT IT: As data leaders it is on us to hold decision makers accountable for their decisions and the data they used to make it. We should demand transparency around the decision making process and ensure we have a seat at the table when data is being discussed.

💸 2. Data teams are woefully under resourced

PROBLEM: Data teams are seen as a cost centre rather than a revenue driver so struggle to receive investment and budget that can be used to grow the team and it’s capabilities.

IMPACT: The expectations of the data team are never met and so the cycle continues.

WHAT TO DO ABOUT IT: To reverse this trend we need to demonstrate the value in everything we do. We need to become better at selling our wins and highlighting the contribution to positive business outcomes that data has enabled. Every single conversation with the business about data should focus on impact.

🕵🏻‍♀️ 3. Shadow data teams popping up

PROBLEM: When the central data team is ineffective, other teams will take matters into their own hands. They will either do their own ‘analysis’ or even hire there own data analyst(s). This is usually due to the actual data team not being able to support their data needs.

IMPACT: This can get very messy. Different tools and methodologies can pop up causing a range of issue across the business when it comes to measurement. It will usually do more harm than good for the central data team.

WHAT TO DO ABOUT IT: Short of prohibiting it to your best ability, see if you can find new ways to support the teams that need it more. Or, if it is unavoidable, bring the new data team in under your wing. At least that way you can create some consistency in how data is measure and reported.

☎️ 4. Data team is seen as a service desk/support function

PROBLEM: Data teams are treated as a support function rather than as a business partner. This usually takes the form of other business users sending ad hoc requests for data to the data team as and and when they need data.

IMPACT: This approach is very common in businesses but the outcome is a disengaged data team that doesn’t appreciate the business context of the data that is requested of them. They are therefore ineffective at finding ways to add value back to the business.

WHAT TO DO ABOUT IT: There are a lot of things that need to be done to reverse this problem as it is one of both mindset and organisational structure. Perhaps the most effective response this however is to empower your data team to push back and to encourage them to ask more questions.

Asking ‘why’ when receiving a request for data can help get to the bottom of the actual problem than needs solving.

🛠 5. Belief that all data problems are solved with more tech

PROBLEM: Data is very widely seen to be a technical discipline rather than a business one. The result of this is that non-data leaders believe that data problems can only be solved with more investment in data technology.

IMPACT: This further compounds the assessment that data is a cost centre and unless clear ROI can be established between the new tech and the outcomes is produces, the organisation will enter a never ending spiral of tech investment > failure > further tech investment.

WHAT TO DO ABOUT IT: Data needs to be seen as a business discipline and NOT a technical one. Any data initiatives that the business wants to undertake should be underscored by a strong business case that has strategic alignment with the businesses priorities.

Technology is actually the last piece of the puzzle. Once the other questions have been answered you should then determine whether or not technology will be needed to solve this problem.

⚡️Whenever you are ready here are a few ways I can help you: