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How Businesses Can Find and Harness External Data



It goes without saying that the enterprise of today is driven by data. Moreso, its success and growth depend on how it is able to effectively gather and utilize data in various aspects of its operations.


Oftentimes, the data the enterprise needs is not an internal resource but has to be sourced from elsewhere. And while many companies have improved their capabilities to collect and extract data from various sources outside the enterprise, only a few are able to utilize such data for the benefit of their respective organizations.


So how can a business maximize its external data to its fullest benefit? Three key steps have been identified toward achieving this goal:


1. Establish a dedicated team for external data sourcing


It is important to have a dedicated data-sourcing team within the company that will focus on gathering all relevant external data for the organization’s use. Ideally, this team shall be comprised of the following roles:

  • Data scout or strategist - works with the data analytics team and business functions to identify operational, cost, and growth improvements that could be powered by external data. This person will also be responsible for promoting the use of external data, planning the use cases to focus on, identifying and prioritizing data sources for investigation, and measuring the value it can generate.

  • Purchasing experts

  • Data engineers

  • Data scientists and analysts

  • Technology experts

  • Data-review-board members

It must be noted that the team members are not required to spend their entire time in sourcing data as many of these roles perform other functions within their respective departments.


2. Develop relationships with data marketplaces and aggregators


While online searches may appear to be an easy way for data-sourcing teams to find individual data sets, it is a time-consuming process, something that many businesses may not be willing to spend on. Because of this setback, there is a growing emphasis on utilizing the data marketplace and aggregation platforms that specialize in building relationships with hundreds of data sources, often in specific data domains such as in the consumer space, real estate, government, or in other enterprises.


Tapping on such relationships can give organizations ready access to the broader data ecosystem through an intuitive search-oriented platform, allowing organizations to rapidly test dozens or hundreds of data sets under a single contract and negotiation. Since these external-data distributors have already profiled many data sources, this saves the external-data team significant time that would have been spent on exhaustive searches. When needed, these data distributors can also help identify valuable data products and act as a broker to procure the data.


While the data gathered from these partnerships are vetted beforehand, it is important to assess the data and determine how it will improve business outcomes. Such assessments should include an examination of quality indicators, such as fill rates, coverage, bias, and profiling metrics, within the context of the use case.


3. Prepare the data architecture for new external-data streams


In order to realize the goal of being able to maximize the value of external data, it is crucial that up-front planning is done and there is a flexible data architecture, and ongoing quality-assurance testing is already in place.


Up-front planning involves an assessment of the existing data environment to determine how it can support ingestion, storage, integration, governance, and use of the data, and whether necessary modifications to the data architecture should be made. It is important that such modifications will ensure the data architecture’s flexibility to support the integration of a continuous “conveyor belt” of incoming data from a variety of data sources.


Lastly, it is important to ensure an appropriate and consistent level of quality by constantly monitoring the data used. This involves examining data regularly against the established quality framework to identify whether the source data have changed and to understand the drivers of any changes. If the changes are significant, algorithmic models leveraging the data may need to be retrained or even rebuilt.


Conclusion


Maximizing value with external data will require a unique mix of creative problem-solving, organizational capability building, and laser-focused execution. Business teams that have proven themselves in demonstrating the achievements possible with external data can provide the business with a needed boost and excitement to scale. And it all starts with having the right team dedicated to laying the groundwork.



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