A first step to an actionable Data Strategy

This is how you can take your first step to an actionable Data Strategy


To stay competitive, companies harness data to enhance customer experience, streamline operations, and carve out a sustainable advantage. Leveraging AI and advanced analytics, they tackle challenges from predictive maintenance and pricing models to organizational optimization and digitizing products.


Success with advanced analytics hinges on data accuracy and sustainable data sourcing. At Opticos, we have found that a pragmatic, focused data strategy is crucial for companies aiming to become data- and analytics empowered. This strategy facilitates the extraction of value from data, paving the road to success.

Many businesses grapple with fragmented IT landscapes, point solutions, lack of data ownership, and poor integration. As a result, analytics teams are burdened with data collection and cleaning rather than focusing on valuable analysis and insights.

Concerns about data quality frequently surface. Businesses periodically launch data quality and master data initiatives. But without robust data governance, data quality tends to decline over time. Hence, achieving accurate data often depends more on luck than a solid business practice. The remedy? An actionable data strategy.


To align the organization on data-related objectives and to overcome the challenges above, Opticos provides three recommendations:

  1. Use Case-Driven Approach: Identify, evaluate, and prioritize use cases that tie business needs to value, targeting relevant data assets and datasets. For instance, forecasting customer order volumes in supply chain management could use historical and weather data.
  1. Prioritized Data Asset Governance: Instead of tackling organization-wide data ownership head-on, establish ownership of data assets for prioritized use cases. Ideally, process or function owners will own the data from source to consumption, regardless of the storage and processing systems. Start with use cases, like customer order forecasting, where assets include product inventory, article details, orders, and sales forecasts. Assign ownership from source to consumption, independent of the data’s system journey.
  1. Transparent Data Architecture: Establish a clear blueprint detailing capabilities to capture, ingest, store, process, share, and consume data for prioritized use cases. Setting up systems for data discovery, monitoring, and governance is also crucial. Emphasize transparency and communication so all stakeholders understand their roles in delivering high-quality data. For instance, use diagrams to map out data flow for each prioritized data asset: from source systems like CRM, through data storage like Data Lake, to Business Intelligence reporting tools.

Furthermore, we recommend a phased data strategy implementation, detailing the first phase and keeping subsequent phases indicative. As you begin implementing, the roadmap’s details become more distinct and defined.


Illustration 1 – Illustrative template for a phased, use-case-driven Data Strategy execution roadmap


An actionable data strategy implements data management practices and governance structures that enable efficient data sharing and continuous quality improvement. Once initial success is evident, the process can be scaled and replicated across other use cases.

Guiding your Data Management Journey

At Opticos, we enable organizations to leverage business benefits by building pragmatic, and holistic data management practices. Drawing from our extensive client experience and methodology, we’re here to guide your data management journey from strategy to implementation.

Tatiana Schön & Mattias Gustrin

Mattias Gustrin, Associate Director with a focus on Data Management, Advanced Analytics, and technology strategies across multiple sectors.
Tatiana Schön, Manager with experience in consulting, project management and business analysis within AI governance, Data privacy, IT and IT Finance.

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