In recent years building a future-proof data and analytics solution and service has been rapidly rising-up the agenda of most CxOs with good reason. Based on our experience at Adatis, here are 10 questions that I think are worth considering before committing to how you operate and evolve your own Data Analytics Platform.
1. Can we attract the talent to do this in house?
Data and analytics skills are in very high demand in the market place and certainly those individuals with the latest cloud experience command a premium. Can you offer a role that offers the interest and reward to attract the best talent and do you have the time to invest in finding them?
2. How do we ensure the on-going efficiency of the platform?
With cloud technology there may be direct savings to be made by ensuring that the Data Analytics platform is optimised and remains so. Will the team have the time and knowledge to monitor and ensure your platform remains efficient and minimise your consumption costs?
3. Is it straightforward for the team to cover the critical hours of processing and operation?
The business dependency on a Data Analytics platform is increasing and the likelihood is that data is no longer just arriving in a batch, in the early hours of the morning. Can your team provide the necessary hours of cover to ensure that as a minimum, by the start of the business day everything is processed, and the platform will be trusted?
4. Can we build the scale of team required to cover all the essential skills?
The range of technologies that form a modern Data Analytics Platform can be bewildering. Will you have the scope to build a team with the breadth of skills required to operate a platform, from cloud infrastructure to data science model retraining and be able to collaborate effectively with your end-users?
5. Do we have the budget to invest in the training to ensure the team are effective?
To operate the data analytics platform and provide down to 3rd line support, the team will need a depth to their knowledge. Can you provide the team the exposure to learning and development opportunities such that they can become experts in the application of the technology and operation of the service?
6. Can we retain the knowledge of the platform efficiently or is this a potential risk?
Data Analytic Platforms are generally complicated and evolve over time. Will there be wasted effort and potentially an impact on the service in ensuring that the knowledge is shared and maintained, perhaps as individuals leave and join the team?
7. Would we benefit from having access to experts?
The technology and specifically cloud platforms are continually evolving. Are you able to keep track of the change or will you have access to experts who can provide you regular updates and provide recommendations of how they might be of value?
8. How will we continually improve the solution and service?
The team will likely be faced with a continual list of improvements that are required to ensure the solution evolves with the changing business. Will you have the processes and safe guarded time to respond and implement the required on-going changes, and will you be able to provide an impartial perspective to evaluate the service and identify ways in which it can be improved?
9. Are we confident on what the operational costs will be, and can we control these?
There may be several factors that add an unknown element to your operational costs e.g. call-out allowances, cloud consumption, recruitment fees, training costs, ad-hoc advice and guidance. Would it be beneficial to be able to fix your operational costs, potentially for several years?
10. And so, bearing in mind all of the above. Is in-house or even your preferred out-source partner likely to provide the right service for your organisation?
Every organisation and every platform is different, and one size does not fit all. So if you’d like some advice on answering these questions for your organisation please do message me and the Adatis team will be very happy to discuss.