Becoming a data-driven company is not easy. Data plays a role in almost all areas of a company. The first step to making more out of this data: A Data strategy as a basis for future innovations, experiments and projects. The success of a data strategy is influenced by many and very different factors. Here are our TOP10 building blocks of a data strategy that everyone should keep in mind who is trying to make their company "data-driven".
Rank 10 - Clean as-is analysis
"Where do we stand?" This question is more important than one might think when transforming to a data-driven company. A data strategy is always multidimensional - therefore all dimensions must also be considered in advance. The creation of a maturity model for each dimension of the transformation from of the actual transformation is essential for the success of a data strategy. By assessing each area of investigation on a scale from "non-existent" to "value-adding", a company's alignment towards data & AI can be evaluated and further steps planned.
Rank 9 - Precise recommendations for action
"Doing is like talking about it, only better". For a strategy to be implemented, it needs precise and granular instructions. With the help of a Roadmap and a Data and metadata architecture must be quantifiable and well thought out objectives be set and the measures to achieve the goals must be discussed with the teams in the company. The Prevents misunderstandings or even cluelessness in the implementation of the tasks at hand.
Rank 8 - Clear vision & strategy
A data strategy must also be anchored at "flight altitude 10,000 m": The vision and goal of a data strategy play an important role within the company. With a defined mission and vision of a data-driven company, a data strategy can be further specified and considered particular. Only with a data-driven or at least a Data-driven business modelIf a data strategy is in place and established in the company, a corresponding ROI can be generated. With a Data culture and qualitative communication between different areas, teams and management levels, a data strategy is easier to achieve and information and insights are easier to access within the company.
Rank 7 - Use Case Development
A data strategy may sound like a vague concept for handling data in the company - but it is not. Use cases make it concrete: with the help of the Implementation of Use Cases can be used to add value to data. Use cases are Specific areas of application for datawhere real ROI is generated with data. Therefore, a global and consistent Use Case Management important to Driving innovation and democratise knowledge within the company. In workshops, use cases can be identified and developed up to the Data product be further developed. By prioritising different use cases and using interdisciplinary use case teams as well as agile working methods, KPIs can be achieved faster and more effectively.
Rank 6 - AI & Algorithms
Data enables the use of business analytics and ML to gain important insights. Being able to use AI to innovate and truly leverage data significantly impacts the ROI of implementing a data strategy. With well-trained Data Science Teams individual and innovative algorithms and thus ML applications can be developed and implemented. More agile development methods and a collaborative culture help drive innovation within the organisation. Innovation and optimisation of internal data science teams and collaboration with domain experts are critical to the successful implementation of AI applications.
Place 5 - Teams & Skills
Becoming a data-driven company requires more than just technology. The people involved in creating, sharing and managing data should have the resources, competencies and skills to deal with the transformation. Data culture must be promoted and lived throughout the company. A Data team with deep expertisestandards and guidelines can effectively transfer knowledge across the entire organisation. Training and knowledge bases are essential for sharing knowledge about data across the organisation and for supporting new collaborations and ideas. The Establishment of a "data-driven culture is important and can lead to a Intrinsic motivation for the use of data lead to the answering of hypotheses within the company.
Rank 4 - Governance & Organisation
Data governance is what ultimately allows data to be shared across the enterprise. Data governance ensures that computations across the enterprise are based on the correct data based. Data Lineage offers the possibility to trace the origin of the data and the conversion process. In addition, the company must Roles and responsibilities be defined for the care, maintenance and decision-making power over certain data. This ensures that someone also "takes care" of the data. In addition, data governance ultimately ensures that the right people have access to the right data.
3rd place - Technology & Architecture
When working with big data, certain tools and infrastructures are required to effectively implement a data strategy. There will be sufficient data architecture with different layers or zones needed for specific use cases as well as fast and effective data acquisition. Through the implementation of a high degree of automation into the infrastructure provisioning process, a high scalability of the architecture can be achieved. Furthermore, the Data security mechanisms be established, and architecture monitoring and cost optimisation are important parts of keeping the system running.
Place 2 - Data Fundamentals
The most fundamental component of any data strategy is (logically) the underlying data. The Procurement and correct storage of internal and external data is crucial for its effective use. Data storage technologies such as data lakes or data warehouses should be integrated into a Functioning data ecosystem be implemented. With a company-wide concept for the use of this data as well as a uniform and application-adapted data model, an effective and Seamless use of the data ensured. In order to promote a uniform understanding of the existing data throughout the company, a data catalogue with metadata can be implemented and established.
Rank 1 - Return on Investment
We need to talk about money: without a sufficient return on investment, it becomes very difficult to justify the investment in a data strategy at the executive level. Therefore, we need to Use cases for the implementation be explored and the overall return examined. A Broad support in the organisation is important here, especially for this kind of multidimensional transformation. Early Lighthouse projects, milestones and successes underpin the need for a data strategy and continue to drive the speed and success of transformation at all levels.