Define and execute data strategies

Identify and determine the value of data & AI at your company using Xomnia’s data value proposition method

Not sure where to start with data & AI? You’re not alone

Many organizations nowadays suffer from an AI-FOMO: With so many state-of-the-art use cases and success stories out there, how can we keep up with this fast-paced world?

Common AI-related questions that companies frequently ask include:

What can I achieve with data & AI in my company?
How can I fulfill my data & AI ambitions?
When should I do what?
Where do I start with the development of a data product?
Does my data product live up to its promise?
My data project is stuck - help?!
The data product is mature enough - now what?

Our advice for those companies is to begin by precisely defining how data and AI add value to them (or not) and to plan where to begin implementing them. This can be done by in 3 steps:

Defining your data value proposition
Developing data and analytics products
Building a data & analytics team

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

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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.

More about our approach

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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 your data teams in such a way could be boosted by Xomnia’s data-vast construction, where junior MLEs can be transferred without hassle.
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.