Creating a Data Strategy

Resolutio provides organizations professional support in creating a custom data strategy to support their specific operational needs.

Developing a data strategy involves the dedicated planning, execution, and management of data throughout its lifecycle to drive strategic outcomes. 

A holistic data strategy addresses the collection, storage, processing, analysis, and interpretation of data with the objective of enhancing decision-making and optimizing operations. 

It is useful to keep in mind that each of the steps needed to create a data strategy is intended to provide answers to one or more of the following core questions.

• Why are you creating a data strategy?

• What data asset will you be using to reach your objectives?

• How are you going to organize and process these data assets to ensure accessibility and quality?

• Who will be responsible for each asset and their related processes?

If any of these go unanswered it means that your strategy is less likely to be successful.

Here is an overview of the key components of a data strategy: 

• Alignment to Business Goals
  Align your data strategy with the overarching business goals and objectives. Understand the specific challenges and needs of the organization and how data can help address them.
  
  

• Data Identification and Collection
  Identify the types of data that will be most valuable to your organization, both from internal sources like ERPS and CRM systems or external sources. Establish a framework for consistently collect high-quality data.
  
  

• Data Governance
  Implement policies, standards, and procedures for data management. Define roles and responsibilities for data ownership, privacy, and security. Ensure compliance with data protection regulations, such as GDPR.
  
  

• Data Quality
  Ensure data quality by setting up data cleaning, validation, and enrichment processes. Remove duplicate or erroneous data, standardize formats, and ensure consistency.  Continuously test and verify data loads to prevent downstream errors.
  
  

• Data Storage and Access
  Determine how and where your data will be stored, including the use of data warehouses, databases, and data lakes. Implement a system for efficient data retrieval and access control, ensuring the right stakeholders can access the data they need while maintaining data security.
  
  

•  Data Integration and Accessibility
  Use data integration tools and ETL processes to combine data from various sources and formats into a unified semantic data layer. Ensure your data can be easily integrated with other systems and platforms.
  
  

• Analytics and Business Insights
  Leverage advanced analytics tools and techniques to transform your data into actionable insights. Implement machine learning models, predictive analytics, and visualization tools to uncover trends, patterns, and correlations.
  
  

• Communicate and Support
  Educate employees about the importance of data, the principles of your data strategy, and how to apply data-driven decision-making in their roles. Promote a culture of data literacy and curiosity within the organization and provide proactive support to analytically minded employees with potential to extract great value from data.

Developing a data strategy involves a continuous and iterative process. As the business environment and technology landscape evolve, the strategy should be adapted to accommodate new challenges, opportunities, and goals.