How Data Marketplaces Can Impact Your Data Strategy

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How Data Marketplaces Can Impact Your Data Strategy

Plans for a data marketplace can significantly impact your data strategy, especially if you’re on the lower end of the data maturity spectrum. As we’ve seen, a data marketplace is usually a route taken after data costs climb high enough to cause an executive to say, “We need to make money on all this data we’re sitting on.”

In this sense, data strategies are often part of the forcing function of considerable data costs. Before diving too deep into a data marketplace’s implications on a data strategy, let’s explore what a data strategy is and how it’s developed and implemented.

What is a data strategy?

A data strategy is a plan for how an organization collects, manages, and uses data to support its goals and objectives. It includes decisions about a variety of concerns:

  • Data governance
  • Data architecture
  • Data integration
  • Data analytics
  • Data-driven decision making
  • Innovation
  • Value creation

A data strategy is critical to any data-mature organization’s overall business strategy.

Data strategies are typically developed through a process that usually looks like this:

  1. Develop data-specific goals and objectives: Understand the business problems to be solved and the critical questions to answer using data.
  2. Assess the current data landscape: Identify existing data assets, including where data is stored, how it is collected and managed, and who is responsible for it. Think of this as a company-wide data audit.
  3. Identify data needs and gaps: Determine what specific data is needed to support the organization’s goals and objectives (e.g., technology, staffing, consumers) and where there are gaps in the data (or usage of) that need to be filled.
  4. Develop a data governance plan: Decide how data will be collected, managed, and used within the organization, including who will be responsible for the entire data lifecycle. This often involves implementing policies and procedures to ensure data quality and security.
  5. Develop a data architecture: Decide on how data will be stored, integrated, and accessed, including what types of data storage and integration technologies will be used and how data will be organized and structured.
  6. Implementation: Implement the data strategy, including data governance and architecture plans. Use data analytics to support decision-making and keep the projects on track.
  7. Continuous monitoring and review: Anything we’ve just mentioned is subject to change. This means constant monitoring and maintenance of the overall plan.

The conditions and circumstances that drive a company to develop a data strategy can vary. They may want to innovate and transform their business, need to comply with privacy and data protection laws, or they’re completely overwhelmed by the amount of data. Whatever the case, the purpose of a data strategy is to know, understand, manage, and govern its data assets.

A data strategy outlines the approach to collecting, storing, processing, and using data. It also defines the roles and responsibilities of stakeholders, the policies and processes for governance, and lays out the goals for aligning data efforts with an overall business strategy. A data strategy demonstrates a commitment to prioritizing and being responsible for the appropriate use of data.

How can a data marketplace affect a data strategy?

A good data strategy should prepare an organization to commercialize or share its data via a marketplace. As such, the system should detail the following:

  • Data Inventory: A clear understanding of the organization’s data and its value to potential buyers.
  • Data Quality: A plan for ensuring the data is accurate, complete, and up-to-date, as well as for validating data against privacy and security regulations.
  • Data Governance: A set of policies and processes for managing data and ensuring its protection, such as data privacy and security, data access, and data retention.
  • Data Architecture: A plan for organizing and storing data to ensure that it is accessible and can be easily shared with buyers through a data marketplace.
  • Data Monetization: A plan for monetizing data through sales on a marketplace, including the definition of target customers and pricing strategies.
  • Data Analytics: A plan for using data analytics to understand the value of data, improve data quality, and measure the success of the data monetization effort.

A company can only use and benefit from a data marketplace if this information is documented. Strategic productization is an amalgamation of all these aspects of a data strategy.

A marketplace is a platform, and as we’ve seen, there are plenty of technical and non-technical challenges to consider before moving in that direction. Here are some of the ways that decisions can affect your data strategy:

  • Access to new data sources: A data marketplace can provide organizations with access to a wider variety of data sources than they may have been able to access on their own. This can help (or potentially hinder) organizations to fill data gaps and gain new insights that can inform their business decisions.
  • Changes to data collection and management: Organizations that transact data via a marketplace may need to adjust their data collection and management processes to accommodate new data. They may also need to update their data governance and architecture plans to ensure the data can be integrated with their existing systems.
  • Impact on data security and privacy: Organizations must be aware of data security and privacy risks when selling (or buying) data in a marketplace. They should assess the quality of the data and understand how it has been collected, processed, and protected.
  • Impact on data monetization: As we’ve seen, a data marketplace provides new opportunities for monetizing data. Productizing data is not accessible or easy, so there will likely be an oversight from finance and accounting leaders to stay within the guard rails.

Preparing data for productization in a marketplace (internal or not) requires some degree of data maturity. The more mature the company, the more sophisticated the data strategy should be. Though some data marketplaces can support simple, low-risk data products, any company considering a buy vs. build decision will likely be further to the right on the data monetization maturity model. Therefore, the data strategy should provide a firm foundation for marketplace-related efforts.