With the desire to turn companies into data-driven companies, also came a huge investment in data and machine learning.
Initially, that investment was done exclusively in big tech companies but rapidly the remaining industries saw the potential of this shift and the benefits it could bring for the company’s growth and evolution.
With that investment, a new era of data scientists, data engineers, data leaders, data executives, and other data professionals has arisen. A new set of data tools and frameworks also emerged.
But today we can say that most of that investment was more tactical than strategic. The investment didn’t generate the expected impact.
There were dashboards built, but the business continued to use its individual excel files.
There were streaming data pipelines being deployed, but nobody understood why they were relevant.
There were machine learning models implemented, but the decision-making process was the same.
Data strategy in today’s organizations is broken. It’s designed to operate in silos.
It’s focused on results such as how accurate a model is, how pretty are these dashboards, or how modern is this data stack.
No actual focus on outcomes such as the level of adoption in the organization, how easy is for the employees to access it, how satisfied they are with data, and what impact it had on the organization.
From my experience, some of the reasons for such crises are:
It's unclear how data will contribute to the objectives and results of the company.
Lack of data literacy initiatives in the organization.
No space for experimentation. Which means no space for failure. No space for innovation. No space for continuous improvement and long-term growth.
If you take a deep look at the 3 main reasons shared, you will notice, those can apply to all business initiatives in the organization. Data is no different. The expectations and the outcomes you need to manage are different and the professionals are different as well, but not the rest.
So why organizations are treating data teams as second-class citizens while stating they want to become data-driven-oriented?
There is no equal journey for anyone. Even for companies in the same industry. We were not able so far to standardize processes the way software engineers did with traditional applications. Senior executives still don’t understand how data works.
Data professionals need to realize it and embrace these facts as part of the journey. Tackle these issues as part of the Data roadmap.
In addition, data professionals can follow these 7 simple steps to increase the probability of data success in the organization.
Understand the vision and strategy of the company.
Identify how data and AI can contribute to that vision.
Define the role data & AI will perform and which outcomes to expect.
Make experimentation costs part of your MTP (mid-term plan)
Ensure data teams understand the impact of their daily job. Remind them when needed.
Educate senior and middle management. Point out what's expected from them and what they should expect in return. Make them owners of some parts of the plan to ensure engagement.
Reshape and adapt the strategy as you go. Continue to communicate changes throughout the organization.
Implementing a data strategy is not just about the technology. It’s not about how well it performs or automates the business tasks. It all starts with education, expectations management, a clear roadmap, transparent and clear communication, and most importantly, real interest in the internal user habits and problems.
It's easier to write than to put in place, I know. But nothing good comes out without smart and hard work.
So don't forget to arm yourself with real facts, energy, empathy, and be bold.
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Thank you so much for reading and let’s talk again soon.