10 tips and tricks to help you successfully build your Data & Analytics team and culture

by Waseem Ali

06 Jul '21

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.

 

 

  1. 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.
  2. Contract carefully with consumers – Ensure there is favourable data access to enable commercialisation and innovation
  3. Look for sources of supplementary data – Explore sources outside of the organisation to identify commercial and non-commercial sources of supplementary data
  4. 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
  5. Where you operate has an impact – The differences in data legislation and culture need to be understood for each operating region/country/state
  6. 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.
  7. 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
  8. 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
  9. Systems need to work together- Integration across analytics environments throughout the enterprise is critical to enable the application manufacturing process to work effectively
  10. Ensure there is quality, security, and compliance -These remain of the utmost importance which includes auditing of data access, usage and action.

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