Data by itself is meaningless. It only becomes valuable when you correctly apply it to reach your business objectives. Thus, your organization’s data strategy begins with what you want to achieve – not with the logistics of using data. This requires fundamental changes, not just for your processes, but for your people – and for your organization as a whole.
Since the early days of ‘big data’, we’ve continued to hear the same refrain from numerous CEOs: “We invested so much money in data, but as a company we’re still working in the exact same way as we did five years ago.”
This is a smoking gun that points at why data projects fail. It’s usually not because of the tools or analytics that companies spend resources on – it’s a given that you’ll need the right tools to put data to work for your business. That’s not a big challenge, beyond having the financial resources needed.
Data projects fail because business leaders fail to approach them as transformational: the organization itself must evolve in order to work with data.
With this in mind, it becomes clear that the scope of a data strategy roadmap is not just about the actual data; it’s about people, business processes, organizational structures and skillsets.
The extreme depth and breadth of a data strategy means that it will only be successful with the full support and involvement of corporate leaders. To work with data, processes spanning departments, roles, functions and reporting lines will change, and doing so requires high-level authority.
A data strategy roadmap also involves the acquisition of data skills, also in non-data-related departments. For example, when a business wants to apply artificial intelligence in targeted marketing campaigns, a certain level of knowledge is needed for the marketing team to correctly configure and use the tools your data experts create.
And finally, creating and executing a data strategy means cross-functional collaboration. If you hire a group of PhDs and put them to work creating tools without involving the people that are closest to the challenges those tools are meant to solve, you’ll end up with an expensive – but useless – tool.
That being said, even if your data experts are creating extremely smart data-driven solutions and apps that solve real business challenges, if your people don’t understand or believe in the effectiveness of these tools, they won’t use them. That puts a full stop in the ability of your data strategy to deliver returns on investment.
As companies become more data driven, there is less “human” involvement in decision making. In the early days of analytics, the actual analysis would happen offline, reports would be created based on the results, and then these reports would be handed over to leaders to use – or not – as they wanted.
Current AI models are almost living entities; they learn and operate – and even act – in real time. This constitutes a huge change in processes, data flows and organizational structures.
This is why data skills are needed, even in traditional departments. Everybody must be tech savvy enough to understand how AI and data analytics work, which means that your employees’ roles and functions will change. Rather than being experience based, certain functions will be more about correctly using an incredibly smart tool that functions as a copilot that can make decisions on its own.
To sum up, data is not a goal, and putting a data strategy into place will cause a lot of changes related to people, processes, organizational structure and skillsets. As your organization transforms, you’ll place intelligent, data-driven AI and analytics copilots, or even pilots, in functions that are not at all data driven today.
What this illustrates is the fact that underneath all of this is a fundamental change in mentality.
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Case Study Manufacturing & Logistics
Manufacturing & logistics have a wide range of data gathering to cover. The entire supply chain should be monitored and enhanced up & downstream. Strategic & operational planning should deliver tactical value & maximize efficiency once optimized and controlled.
Case Study HR & Social Services
HR & social service companies are data companies by nature. With their dual sided business model, they have both company AND consumer data streams to feed & maintain.
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