Looking to become (more) data-driven? Begin by setting a data & AI strategy
No matter where your ambition lies in the data & AI spectrum, it needs to begin with a plan or strategy. Based on our years of experience, data projects that lack a clearly defined plan or goal will most likely fail.
Why is a data strategy essential?
- It informs decision making for strategic advantage.
- It improves operational efficiency and productivity around data& AI projects.
- It ensures a competitive advantage by leveraging data insights and AI in the right and most optimal way.
- It helps explore new revenue opportunities with data & AI while proactively mitigating risks.
- It mindfully fits data-driven innovation within the different pillars of your organization.
Create a winning strategy with Xomnia
Create a winning strategy with Xomnia
1. Define the data value proposition for your company
At Xomnia, we believe that AI and data should be tailored to solve challenges in your company, and not the other way around. Therefore, the journey to create and execute your data-driven strategy and deliver useful data products should start by clearly answering 3 fundamental questions:
- WHAT are the data opportunities for our company & why should our company chase these data opportunities? Define your Data & AI value proposition
- HOW might we achieve the selected use cases? Conduct a Data & AI capability assessment
- WHICH data products and organizational enablers are to be developed and when? Set your Data & AI road map
Get in touch
Get in touch
2. Develop data and analytics products
Focus on executing the data strategy that you have defined, and iteratively develop the selected use case(s):
- Use case canvas: onboard your selected use cases for development by filling a Data & AI use case canvas and initiating backlogs.
- Proof of concept: develop prioritized use cases into a PoC to establish technical feasibility and involve stakeholders at an early stage.
- Proof of value: as soon as possible, field-test your data & AI products to establish proof of value. This may result in pivoting a project or canceling it altogether.
- Deployment and operationalization: deploy and operationalize proven data products, prompting further iterative developments.
Get in touch
Get in touch
3. Build a data & analytics team
Build and grow your data team, by attracting and retaining the best talent in the following ways:
- Define good profiles and recruitment plans: Building a team can be tricky! To go from strategy to execution we provide the right people, but in the long run you want to have your own team running. With all the knowledge in house, we can create profiles that fit the desired projects.
- Focus on the development: those are the goals and ambitions of your data team and provide an open atmosphere of feedback and coaching. You can do that for different teams in your company with the help of our Academy’s different training tracks
- Create a data culture: such culture often revolves around making decisions based on data insights, stimulating experimentation, and encouraging employees to share their work.
- Make learning a motivation: one of the primary motivators of today’s data professionals is learning. It’s essential to invest in training capabilities for employees. Having clearly established growth paths allows employees to have a good perspective on their careers.