Run your Data Warehouse project like an 18th Century trade ship
When you complete a large data project there is a tremendous sense of achievement. Everything was taken into account, we have the best people, the most advanced technology and the best processes to support them. We conquered.
And yet after a year or so adoption stagnates or starts to drop, the Data Stewards start to slip, employees start to perceive the whole ordeal as another chore. It really feels like the strategy “withers” before our very eyes and barbarians are just waiting for their shot.
This is because despite our best efforts and investments, Data Strategy project is never done. It needs to be nurtured much like plants need constant source of water and sun and cannot be just a one-shot investment of water and UV radiation. This is usually counter-intuitive to project managers, used to projects summarized and closed at some point of time either achieving success or fail immediately before moving on to the next best thing.
In our data practice we have seen and implemented Data Strategies on organizations big and small from all around the world and industries as different as they come. But we had always one advice for all of them – start small and evolve your Data Empire. Everyone can afford small steps towards their goal, not everyone can withstand a failure of enormous projects.
Revolution is good for marketing – not so much for a Data Strategy. The key to success is not to try reinvent Data Strategy completely when it starts to crack but to slowly evolve it continuously from day one. This process should be discussed and implemented as a part of the original Data Strategy project as small improvement take time and need to be taken before .
In Phronesis Path we use some simple techniques to achieve this:
Survey is underrated way of gathering feedback but it’s also the one which shows the early signs of problems and allows to efficiently monitor progress. At the same time, surveys are often dreaded by the employees as another boring chore then need to do.
When planning for surveys keep the following in mind:
No one likes to be forced into doing things and that could affect the results. Instead offer words of encouragement, explain why this is vital for you and the organization. Also do not forget to thank your employees for participating, share the results and communicate actions. If your survey does not lead to any changes, employees will soon disregard them.
Ideally survey should be complete by 5-10 minutes not to be another chore for the employee. Standardized questions with common scale will help you track your evolution and gauge if the direction is affected by the actions you take. Leave room for giving open feedback at the end – you may be surprised by some of the keen observations your team has!
Surveys need to be carefully crafted. Templates for those surveys should be created as a part of the original Data Strategy implementation project to make sure there are treated with the attention they deserve.
Kaizen is a philosophy of bringing constant, small improvements to the process which over longer time can yield significant benefits. It is the very opposite of revolutionary changes and by some can be misinterpreted as “slow”. But because the improvements are small, also the risk is very low. Kaizen cycle of improvement follows a set of steps:
For many managers this approach is counter-intuitive as they are looking for big, flashy solutions that will save thousands of dollars and grant them the favor of the leadership. But those are expensive and spark resistance (as any big changes would) so the risk to fail soars. Kaizen is small enough to fly under the radar and inexpensive enough so even if some initiatives fail, the impact will not be significant. If you want to adopt Kaizen approach then you need to make sure your company culture encourages it.
Current Business Intelligence and Data Analysis landscape provide new opportunities that were previously reachable only by IT personnel. Now we have access to variety of low-code, no-code and AI-driven tools which can put your business users in the chariot driver seat.
Approach to invest into easily operated tools can be done for organizations of any size but can be even more effective if your company is small or medium-sized and you would want to empower your business users without investing extensive resources into building a sophisticated IT department.
When planning your technical environment, frequently ask yourself “who should be using this?”. If your answer is your business users then chances are they have at most some basic data experience. This should be your factor to look for low/no-code tools that they can utilize.
The final aspect is to build a community around data and it’s usage. Provide a platform for your users to exchange ideas, discuss features they were able to use in their analytics and ask others for hints when they get stuck.
One cannot stress enough how important communities are in driving adoption and evolving your Data Strategy. They bring together data, people and technology to ensure step by step the organization is going in the right direction.
Start small (as always) setup a forum, send users to training sessions, ask the savviest ones to engage on the forum and help answer questions. Make sure to recognize their involvement in the forum and the community.
In the end, success of a Data Strategy is measured by its ability to adapt and change over time and a good Data Strategy should never be considered “conquered”. Continuous improvement heavily depends on company culture which needs to see the potential of Evolution versus the empty promises of Revolution. It does not matter if your company is small, medium or worldwide empire, you can evolve your Data Strategy with small steps towards the goal.
See how we can help you build your Empire at https://www.phronesispath.com/service/data-strategy/
Phronesis Path wishes you many conquests and even more small improvements.
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