Successful use of advanced analytics is not only up to the data analysts, data engineers, and data scientists. Leadership have a crucial role to play.
Leadership is crucial towards achieving success with data analytics
Data science success isn't only up to the data analysts, data engineers, and data scientists. Leadership have a role to play too. They must define and own a data analytics strategy for their organization which can help their organization create and maintain a competitive advantage over their peers. And to do that, they must understand their business, and more importantly, how data analytics can best add value to their business.
And strong leadership should start with those directly supervising data professionals. These managers and team leads need to provide guidance and support to ensure quality output from their team. That includes input on suitable methods, ensuring best-practices are being followed, and keeping strong lines of communication between their analytical team members and business areas, to ensure that the work their team produces remains impactful and relevant.
Where should you start as a leader?
But where do you start as a leader? Well, no matter your level in the organization, we believe it is beneficial to understand all the points below to some degree. Your focus will depend on your role, obviously. But what we want to stress here, is that those organization who have successfully embraced a data-driven culture will have a broad acknowledgement and understanding of all concepts below.
So to start of, think through what data analytics is, and how it's used in your organization and industry. Do you have an understanding of:
- the role data analytics plays in various contexts, particularly those related to your business
- where your organization is already utilizing data analytics, and where it is under-utilizing analytics
- how your peers and competitors are achieving success and gaining an advantage from using data analytics
- what structural or transformational changes your industry is facing, and how they may present new analytical opportunities
Next, take a look at your analytical teams and resources. Do you have an understanding of:
- the different roles and responsibilities across data analytics teams
- how data analytics teams should interface and relate to other areas, teams, and functions across the organization
- what data analytics education and training you should offer and support for your organization
- how to encourage and empower data analytics teams, not just for the present, but for the future
Then, in terms of how your analytical teams do their work. Do you know:
- what data analytics tools, platforms, and technologies are being used by your organization
- who the key technology and solution providers are for your organization and industry
- what platform and technology investments your organization should make for the future
And finally, in terms of executing on analytical opportunities. Do you know how to:
- identify a successful and an unsuccessful data analytics project, before, during and after it's execution
- determine if the data available and used is appropriate for the given problem
- identify the main assumptions made in an analytical workflow and how to best challenge their use
- best extract insights and form coherent stories based on analysis
There's a lot to chew on here. And you shouldn't be discouraged if you feel overwhelmed. Instead, use this as the opportunity to sit down with the right members of your organization and brainstorm the above. There isn't going to be a single correct answer for each point. But what is important is understanding the answers based on your organization and the types of analytical problems your organization faces.