Data strategies are almost always developed with the best intentions, but all too many of them fail to achieve their desired outcomes. 85% of data strategies fail because of data silos, lack of skilled staff (data literacy), and/or a lack of clear goals for using data. Data is the most important business asset of the 21st century. Companies that plan for integration, storage, protection, and utilization of data will be at a distinct competitive advantage.

In response, Search Discovery developed the Executable Data Strategy as a method for developing strategic plans that can be accomplished in a series of sprints.


A measurable plan with specific tasks, milestones, and goals

Advance data and analytics capabilities to create a stronger ecosystem

Building an iterative approach for sustainable solutions, rather than a one-time success

Use of your data with purpose to drive business results, including making/saving money, improving operations and data literacy, and enhancing customer/employee experiences

Stakeholder alignment on a plan by prioritizing initiatives and establishing accountability


Multinational Pharmaceutical Co. | Comprehensive Strategy Engagement
Top British multinational pharmaceutical company builds a center of excellence through comprehensive strategy engagement.

  • Released first production dashboards within 90 days
  • Built a center of data excellence.
  • Instilled an experimentation culture and optimization capability
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High Tech | Optimization Program Across Channels
Insights from personalization program drive business growth, novel opportunities, and incredible media spend savings.

  • Drove 11% growth of registrations
  • 388 net new contacts, opportunity valued at nearly $7.7MM
  • Potential of $1MM cost savings in media spend
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American Cancer Society | Optimization Current State Assessment & Vendor Evaluation
The new technology and processes created by Search Discovery helped American Cancer Society increase test volume and impact.

  • 5.4% growth in YoY donations
Read More


We take a methodical approach where we assess, architect, and activate each client’s unique data and analytics ecosystem to deliver executable strategies that roadmap the future. Unlike traditional roadmaps, we don’t design strategic plans that have dependencies built in, because they have the potential to derail entire projects. Instead, our executable strategies help clients build capabilities, establish foundational processes, and align stakeholders to the core components that make up a unified strategy. Independently, these strategies deliver stand-alone value. But when aligned to a corporate vision, they help companies establish feasible roadmaps, one leg of the journey at a time.


Organizations must deal with web data, social data, IoT data, sensor data, and a multitude of other sources that accumulate data exponentially. Thus, the need for a data strategy that identifies desired outcomes and amasses data sources necessary to achieve those specific goals in a manageable way. This is foundational for using data with purpose, but it requires organizations to understand their data assets.


The next phase in creating a Data Strategy is architecting your data for processing, storage, and utilization across your enterprise and systems. Ultimately, the decision on how to aggregate and store your data is strategic. The deliberation process should consider specific use cases and the desired outcomes. By defining an Executable Data Strategy, organizations can determine their specific desired outcomes and view data in the context of the purpose by which it will be used.


Data activation can include the testing and launch of personalization initiatives based on consumer profile data and real-time triggers that deliver content and messaging at precisely the right time. Or activation could mean empowering franchise owners within your enterprise with data that reveals the impact of their marketing dollars. These examples and countless more illustrate what it means to activate your data. However, activating your data isn’t possible until there is a high level of trust in data across your organization—trust that depends on understanding your data and architecting it properly.


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