Data cloud migration: Why is this change a management challenge, not a technical one?

Organizations are experiencing a fundamental change in their storage and data access architectures, realizing that maintaining large on-premises data warehouses is no longer necessary. Today, the cost of keeping data in the cloud is much lower, with improved security and faster access for various purposes. Business Intelligence, Data Science, Machine Learning, Automation, and Data streaming, are just some of the applications fueling this cloud adoption trend, setting innovators apart from followers.

The technical challenge is enormous, and there is abundant material discussing it, from Total Cost of Ownership (TCO) considerations, to the best migration roadmap tailored to each company's characteristics and needs.

However, an aspect that is not discussed as much is the impact on the individuals who will ultimately use the data that IT teams have worked hard to make available in the cloud. These end-users, the everyday people who make the business function, are the ones who will determine whether the investment is worthwhile. They are the decision-makers when the moment of truth arrives.

What does this process of moving data to the cloud mean for them? Why is it relevant, and what is expected of them in this new environment?

The change is immense. For someone who receives reports or dashboards updated possibly once a day and acts based on what they see in those reports, that environment is about to disappear. From now on, without even considering AI processes, this same person will be able to access their data in real-time, which implies a capacity for reasoning and action reduced to minutes instead of hours. Moreover, they will be expected not only to review data but also to use analytics platforms to gain much deeper and more relevant insights from that data. They will have platforms to which they can ask questions in natural language to obtain knowledge, but of course, first, they have to know what to ask and how, and that, in itself, is a challenge.

While individuals know their businesses and functions, they often don't know how to improve their performance based on data or how to fully utilize the new capabilities provided by the IT department for their benefit. There is a significant gap between their desires and what they can achieve, and this gap is often overlooked in cloud migration projects, even though it is the most challenging to close.

Bridging this gap does not depend on technology; it relies on the ability to adapt and change people’s behavior, which is generally a process that doesn’t happen overnight. Human adaptation takes time; learning new ways of operating and forming new habits cannot be achieved solely through a workshop. It requires an entire process of new thinking, a series of personal and collective experimentation that generates new paths in the way of being and experiencing everyday life.

Today, I don't have all the answers on how this should be done; it's something we are continuously experiencing and learning together. Never before have we been able to access so much data so freely, closely, and with so many tools at our disposal.

" For executives, this process should be considered a cultural change, a change in behavior within their organizations, leading to the creation of a renovated data-driven work culture that delivers knowledge minute by minute in various formats. Rather than spoon-feeding information, individuals should be empowered to specialize, experiment, and learn. The faster the team adapts, the faster the organization can move."

As a leader in a company dedicated to enabling organizations to utilize data, I aim to sow the seed in this blog that it's not just about moving data to the cloud. It's about moving people onto this new wave, accompanying them with supportive programs, embracing daily changes, and promoting the free- dom to make mistakes in order to grow. The only way to achieve exceptional results is by doing things differently, which may involve an arduous journey.

At Derevo, we have had the opportunity to support various clients through similar transformations, assisting them at different stages of their processes and designing holistic and long-lasting analytics platform adoption schemes. We don't claim to have a magic formula, but we have experience living the change with our clients, and I want to offer some ideas for reflection. These ideas are available for anyone who finds them relevant because we are all continuously learning and evolving in this age of unprecedented data accessibility.

1. Implement a culture of Psychological Safety applied to decision-making, allowing for failure while being cautious about failing small, failing fast, and learning from it.

2. Maintain an internal advisory process to educate employees about the capabilities offered by the platforms accessible to the organization.

3. Encourage using optional or additional functionality trials to conduct initial testing and evaluate possibilities. Motivate individuals to experiment beyond budget constraints as technology evolves rapidly.

4. Establish a robust yet flexible governance structure to facilitate agile integration of new data and technologies into the ecosystem, promoting expansion and adaptability.

5. Involve business stakeholders consistently in IT projects. A governed data lake in the cloud is pointless if business users don't know how to use it or understand its potential.

6. Prioritize communication and explanation. Surface-level understanding won't lead to change; we must delve into the depths of problems, technologies, and new possibilities, extracting meaningful insights.

7. Stay grounded. While we can implement groundbreaking projects that seemed impossible a few years ago, this isn't magic. AI, data science, and machine learning won't do the substantial work for us - we need to identify what genuinely adds value to the business, avoiding overly complex projects with limited benefits or feasibility in the short term.

8. And finally, eight, because I prefer even numbers, embrace constant and consistent experimentation. Changing learned behavior requires learning something new. The scientific method should guide us - generating hypotheses, experimenting, learning from failures, and making successful strides down the right path. This approach will likely lead to the fastest progress if properly utilized with current technologies.