Consideration 1: Identifying Data Types

The first step in developing a robust data strategy is to recognize the types of data your business interacts with. Here's a brief overview of various data classifications:

• Master Data: This refers to essential information about key entities such as clients, suppliers, and products.
• Unstructured Data: Any data that lacks a pre-defined structure, including emails, text documents, and videos.
• Transaction Data: Data that captures events, often associated with a time dimension, such as sales records.
• Metadata: Information that describes other data, like log files that track processes.
• Hierarchical Data: Data that illustrates relationships within a hierarchy, such as organizational structure.
• Reference Data: Data that categorizes other data types, often remaining static over time, such as currency exchange rates.

Recognizing these data types is crucial, as each requires different processing approaches. Understanding how these types of data interact will guide your strategy effectively.

Consideration 2: Understanding Data Characteristics

Next, we need to analyze the characteristics of our data. There are six key dimensions to consider:

1. Volume: The size of the data, which tends to grow with your business.
2. Variety: The range of different data types your organization manages.
3. Velocity: The speed at which data is generated and processed, impacting your responsiveness.
4. Value: The significance of data in enhancing your Key Performance Indicators (KPIs).
5. Veracity: The reliability and accuracy of your data.
6. Variability: The rate of change in your data over time.

Contextualizing your data through these dimensions allows for better management and utilization.

Consideration 3: The Role of Data Governance

Many people misconstrue data governance as a set of restrictive regulations. However, it's more akin to the braking system in a car—it doesn't hinder progress but rather enables safer and faster movement. Strong data governance lays the foundation for growth by ensuring reliable data handling.

Delaying the implementation of data governance can lead to complications. A well-crafted governance strategy should:

• Align business and data activities.
• Standardize data systems and processes.
• Reduce operational overhead.
• Enhance transparency and decision-making.
• Mitigate risks and costs.
• Foster data literacy across the organization.

Consideration 4: Creating Data Synergy

A solid data strategy is built on three core pillars:

1. Business Strategy: This defines your objectives and priorities.
2. Data Application: Transform data into actionable insights through various analytical levels, from descriptive to cognitive.
3. Data Management: Establish a framework for handling high-quality, secure data.

Assessing the sustainability of your operational model is vital. Key considerations include the robustness of your data architecture, governance strength, and data privacy measures.

Consideration 5: Evaluating Your Data Strategy

An effective data strategy should enhance decision-making processes, speed up operations, and enable your business to handle increased data volumes. Regular evaluation is essential to ensure that your strategy aligns with speed, accuracy, and value.