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AIQ: What Data Sourcing Means for You

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Synaptiq has spent the last decade studying data and artificial intelligence (AI) across a wide range of industries. We’ve consulted with clients, conferred with partners, collaborated with competitors, and conducted research to understand how and why organizations leverage these technologies.


Our ultimate takeaway?

AIQ: a novel approach to data and AI, centered on 11 key capabilities shown to facilitate successful workflow integration and return on investment (ROI).

This blog post will deep-dive into one of the 11 capabilities: Data Sourcing. We’ll discuss what it is, how it’s done (when it’s done right), and why it matters. Or, you can read an overview of  AIQ, including all 11 capabilities, in our blog post, “AIQ: What We Mean & What You Stand to Gain.“


What is Data Sourcing?

Data Sourcing is the mechanism by which an organization identifies, extracts, and integrates external data, a.k.a electronic information from outside the organization. Today, data powers many of the digital applications that we take for granted: Internet search engines, social media, communication software, and many more. Data also powers many processes used exclusively in industry—from online marketing delivery to manufacturing procedures. Any organization reliant upon these data-powered applications and processes must define and execute a strategy to acquire data from outside the organization.

The first step to achieving this assurance is to define a data asset strategy (DAS). DAS allows an organization to manage and develop its data as a business asset, which means using data to enhance decision-making, product development, and other business-orientated processes and applications. The second step is to implement data discovery (DD): the process of finding valuable open-source, public, or third-party data or metadata (labeling) services  

Once an organization has DAS and DD, it must collect data to continue increasing its data asset value. For third-party data, oftentimes, this means acquiring data through licensing, partnerships and procurement—a task that belongs to “data hunters.” Data hunters identify and negotiate with potential data partners, a.k.a. third parties willing to provide data. They determine which data partners have the most valuable data for their business and negotiate to acquire it (e.g., What licensing is needed?). Data hunters also work closely with product managers and other roles to integrate the data that they acquire from third parties into the applications and processes to generate value for their organizations.


Data Sourcing Done Right

An organization must know (i) its objectives, (ii) what data serves those objectives, (iii) how to find external data and metadata services a, and (iv) how to partner and procure third-party data to increase its value. Data hunters cannot work effectively without this information, making it a prerequisite for effective Data Sourcing. 

Ultimately, Data Sourcing done right is a well-oiled machine for seeking and acquiring data. An organization should constantly evolve its DAS, update its DD practices to reflect that evolution, and use data hunters to partner (or renegotiate partnerships) to acquire the best data for its dynamic objectives.


Why Data Sourcing Matters

In recent decades, we’ve watched data-powered applications and processes transform the way we work and data-first businesses disrupt entire industries. Organizations, therefore, have a vested interest in Data Sourcing: the mechanism that determines the strategy to wield a data asset, extract insights from it, and increase its value through the thoughtful integration of external data.  Companies that lack this capability will ultimately be disrupted.

You can learn about Data Sourcing and how it fits into AIQ by reading our blog. Or, take our AIQ assessment to determine where your organization stands for each of the 11 capabilities.

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