Data Strategy

Accelerate your data journey confidently
with an airtight data strategy

What is Data Strategy?

As you’re building your data platform, you’ll be faced with a myriad of decisions. Answering these questions in a vacuum or without seeing firsthand how leading companies do it is tricky. The typical decision points are deep and can include:

Meanwhile, many data strategy engagements are theoretical and don’t translate to action or impact. Bad data strategy begins and ends in PowerPoint.

Effective data strategies are opinionated and actionable. They are results-oriented and based on the real-life experiences of data practitioners.

With an actionable data strategy, you’ll be ready to build an operational data platform and data pipelines.

Why should you build a data strategy?

What is (and is not) covered in a Data Strategy project?

No two companies deal with the same set of challenges, and an “out-of-the-box” data strategy doesn’t exist. Here are some examples of what is typically covered and not covered.
What is typically covered?
What is not typically covered?

phData's Data Strategy framework is trusted by

Got data strategy questions? We've got answers.

Data strategy may not be required for all your projects, but it’s something you should consider. Let us help you decide whether data strategy fits in before your project begins.

Our blog post, How to Build an Actionable Data Strategy, answers this in detail. However, the high-level process involves the following:

  1. Survey and interview data consumers and business users and document goals.
  2. Prioritize goals considering which have the highest potential business impact, feasibility, and risk.
  3. Assess the existing technology, organizational, and skill landscape to determine actions and investments needed.
  4. Document the business case(s) and generate detailed plans for architecture, training, operating models, use-case pilots, and sprint planning.

After the strategy is formalized. You will implement the operating model and data architecture through agile sprints. Agile enables constant feedback loops to ensure alignment with business needs and business value.

Data Strategy costs are dependent on where a customer is in their data and analytics journey and what their goals are. Our data strategy engagements typically start at about $50k and go up to $150k. The high end is reserved for strategy, architecture, and planning for large or complex projects.
Implementations are dependent on current state and where a customer is migrating to. In general, implementation follows the path of platform build, migration, use cases development, testing/validation, deployment to production and management. Each of these phases need to have clear timelines and cost.

How our Data Strategy framework accelerates your project

We focus on efficient decision making and leveraging best practices to guide Data Strategy engagements. We make recommendations as if we were operating the platform (because in a lot of cases we are).

  • Quicker time to value

    Services leverage a library of reference architectures and best practices to set up organization for success in 4-6 weeks.

  • Investment with immediate impact

    Engagements typically cost between $50K and $100K and you’ll be ready to implement the operating model and data architecture through agile sprints.

  • Built by hands-on practitioners

    Because marketing doesn’t always translate to reality, our data strategy engagements are opinionated and involve Principal Solutions Architects who have worked on dozens of data platforms and projects.

  • Proven best practices

    Developed through working with the most advanced data and analytics organizations and deploying 1000s of data product use cases.

Want to learn more about data strategy? We've got you covered.

How to Build an Actionable Data Strategy

Learn how to develop a more focused data strategy framework that delivers value with the help of this guide.

Data Product Definition Checklist

Get more value from your data with this helpful checklist that helps you define and prioritize your data products.

Getting Started with Snowflake Guide

This guide is packed with best practices that will allow you to get the most out of the Snowflake Data Cloud and deliver results.

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