As we begin building the next generation of world-leading Data & Analytics professionals through our market-leading program, we thought we would share some insights from our vastly experienced team on how you can make a successful Data & Analytics team and culture.
Our team consists of some of the most experienced data and consulting professionals in the world and through their careers, they have experienced a variety of types of businesses and environments.
While this is not an exhaustive list, we have narrowed it down to 10 tips and tricks to build a successful Data & Analytics team and culture. These are just some of the things we explore extensively through our marketing-leading curriculum with our consultants at Rockborne.
- Focus on the data – Understand the whole process of the data that you will be creating, harvesting, acquiring, and curating – and how it can be re-purposed and utilised to the fullest extent.
- Contract carefully with consumers – Ensure there is favourable data access to enable commercialisation and innovation
- Look for sources of supplementary data – Explore sources outside of the organisation to identify commercial and non-commercial sources of supplementary data
- Find the right structure for your needs – The right structure for your operating model is dependent upon the type of business in question (federated, centralised, CoE, etc) and so you need to look at the industry standards
- Where you operate has an impact – The differences in data legislation and culture need to be understood for each operating region/country/state
- Everyone needs to be properly trained – The whole team needs to be data literate and data comfortable – successful data applications require a team not 1 or 2 heroes.
- Data should inform common sense decisions – Data must be seen as a critical component in business decision making where applicable but common sense still plays a role as there are other factors to consider
- Data usage throughout the pipeline should be considered – The development process needs to think about application data that will be generated and analysed as well as the data required
- Systems need to work together- Integration across analytics environments throughout the enterprise is critical to enable the application manufacturing process to work effectively
- Ensure there is quality, security, and compliance -These remain of the utmost importance which includes auditing of data access, usage and action.