The Journey Towards a Mature Data Organization
Today, over 80% of companies consider data an essential part of their strategy. However, according to our annual data & AI survey, only 1 in every 6 organizations actually succeeds in developing data applications that are adopted and used by the business. Organisations realise that becoming successful with data is not just a technology challenge, but that it also requires a culture and mindset change. Given these diverse challenges in working with data, and how they affect everyone in the organisation, we believe that a company-wide strategy is needed to effectively work with data and analytics at scale.
Data strategy should always follow from your business objectives. Data is a powerful tool that helps organisations make informed decisions, improve operational efficiency, and drive growth.
A data strategy therefore describes how organisations will remain competitive in an increasingly data-driven business landscape. This is what a data strategy should embody for your organisation:
- Having the ambition to become more data-savvy and knows why and how to leverage data, analytics & AI to their advantage.
- Knowing which strategic use cases or business opportunities are critical for business success with data and analytics.
- Translating your organization's data ambitions into a concrete and realistic roadmap to achieve well-defined data and analytics objectives.
Discover Your Data Maturity
Gain valuable insights into your organization's current data practices and identify areas for improvement.
This assessment spots process gaps and provides a glimpse of the various maturity drivers that are essential for business success with data and analytics.
What it takes to translate your strategy into business solutions
Your processes with regards to AI solutions are as important as building them and getting it into production. Ideation workshops, AI literacy trainings, product ownership and management of enterprise-scale projects it's all important. To do this you need the right analytical capabilities, workflows and be ready to scale. All this, with the right foundation in place: A modern data stack and data warehouse.
- Develop business capabilities to discover and prioritize AI use cases
- Maninging the AI products and leveling up AI-literacy
- Developing the right analytics capabilities
- Setting up the foundations with a modern data stack & data warehouse
Our Data Strategy Services
Assess where you are in your data journey. Xebia has a proven data and analytics maturity model that covers both technical capabilities and business adoption factors. Based on the outcomes, a practical roadmap is created regarding people, data, technology, sponsorship, funding, and business processes. Using this model offers you the following benefits:
- Determining your organisation’s data maturity with a full audit.
- Creating scoped reviews, e.g., a technology platform or people skills assessment.
- Checking specific security, ethics, and regulatory compliancy.
A successful data strategy stems from the corporate strategy and organizational goals. Your leadership should understand the rapidly evolving fields of data, analytics and AI, articulate top-down key objectives to remain relevant in a changing competitive landscape, and lead the change. To achieve these goals, we recommend following these steps:
- Inspire and educate upper management through executive (CDO) training.
- Define your organisation’s data ambitions with a North Star workshop.
- Establish your key success metrics for value measurement.
Data Use Cases
Data, analytics, and AI must be monetized to get a return on investment. Xebia has a proven solution framework specifically for identifying, refining, and guiding analytics use cases from idea to product. The process includes:
- Building your data science and data engineering chapters.
- Leading the way with senior experts.
- Implementing the most effective workflow.
To evolve from a well-written document into a well-implemented way of working, a strategy needs to be effectively communicated and adopted. The proof in the pudding is to actively promote and encourage the strategy, while showing concrete results from realising your defined objectives and use cases. To achieve this, we recommend to:
- Assign a program manager or head of data and analytics to lead use cases.
- Define and act on a communication and change management plan.
- Enable your senior management to make the right decisions and ensure progression through strategic coaching and mentoring.
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Together with Xebia Data, agricultural distributor Kramp defines its 3-year data strategy, and kicks it off with a brand-new MLOps platform and first AI use case, resulting in an estimated cost saving of 8 million EURO in the first year.
What's the "Secret Ingredient" Making Paula's Choice Customers Glow? Data!
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Van Caem Klerks needed to update business processes to become a data-driven company. Through high-level data strategy & execution, they managed to gain speed, productivity, sales, and the ability to make better data-driven decisions.
Randstad Building a First-Class Data Science and Data Engineering Organization
Leading staffing company professionalizes its data science and engineering department by improving processes and building internal capabilities