The Essential Data Requirements for Successful Developers
How to lead a team of successful developers, supported by data, analytics, and DevOps automation techniques?
Leading brands and local businesses alike rely on innovative business and consumer data to market their products, sell directly and advertise through multiple channels, including social media, display, email, and direct mail. That same data is used to support many other functions as well.
Tech companies rely heavily on data to support search and navigation, location analytics, risk assessments and more. While use cases might be different, here’s what all companies and their product developers should look for when evaluating the data that will fuel their solutions.
The comprehensiveness of a data source is a key consideration in determining how well it can meet a company’s needs. When it comes to data coverage, for example, developers should look for data sets that include all active businesses and populated residences in the geographic area their products represent.
In addition to covering the entire geographic area, data sources should accurately represent all types of businesses, institutions and agencies, regardless of size. These sources should further represent all residences and the individuals within a given area.
Comprehensive coverage is paramount in ensuring a good user experience. Products, regardless of the tech that powers them, will not perform unless powered by the right data. Comprehensive coverage means the user of a search and navigation solution can find the right business and easily navigate to its location. It means the users of an analytics solution can rely on the conclusions drawn from their studies because the data accurately represents their subjects.
The results a developer can expect from their data source are also affected by the accuracy of the data. It’s not enough to simply guarantee coverage of a geographic area.
A quality source of data should be current and should accurately define the business or individual. Routing a user of a search and navigation solution to a closed business is a very poor result for a developer’s product. Misrepresenting the type and size of businesses within a building or overstating the number of businesses in a building will lead to poor conclusions when assessing the risk of a commercial property.
The sleekest technology in the world isn’t going to impress users if the data that powers it is constantly turning up incorrect information. In assessing the coverage and accuracy of data, it helps to inquire as to what sources are used to build a database, what processes are employed to ensure the source data is verified and updated, and what ongoing monitoring the potential partner uses to identify changes to the data being licensed. Simply relying on one source of data will not lead to comprehensive coverage. And waiting for businesses to self-report changes is not enough to maintain a useful, accurate dataset.
Ease of Implementation and Recency
Finally, as any developer or data analyst can attest, integrating data into a product or platform is about more than the quality of the data—it’s about the ease of the integration and how updates are made. In this respect, developers should look for sources that provide standardized data, flexible file formats and reliable delivery options that meet their requirements.
Technical support and proper documentation are also critical to effectively onboard new data and to ensure smooth operation of a product.
Data partners should also offer robust APIs that enable real-time data updates. These updates inform product developers of the changes to a business or residence as these changes become available. Delaying such updates by weeks or even days can cause a product or platform to become out of date and subsequently provide inaccurate data to users, leading to a poor experience.
Ultimately, the old adage of “garbage in, garbage out” is incredibly relevant when it comes to selecting and integrating data sources into a user experience. While every product’s or platform’s data needs are different, businesses and their developers can ensure they’re setting themselves up for success by prioritizing coverage, accuracy, ease of implementation and recency when vetting available sources.