The ability to extract value from data has become a business differentiator, dividing disruptive companies from disrupted. The companies that we consider disruptive have mastered the collection and processing of data, and are able to take decisions based on data, and build features driven by data, ranging from simple convenience features to complex services driven by machine learning.
Companies that are successful in handling data all share a common strategy: they collect data into a data platform, a central data storage where data is made accessible for processing. The data platform enables data innovation by decreasing friction to use data and time from idea to putting data flows in production.
Building and operating a successful data platform and data flows has proven to be challenging for many companies, and there have been many reports of big data projects failing to deliver expected value. There are technical challenges involved in working with new types of data processing technology, but there are also cultural challenges involved in sharing data that has been stored in closed systems. In order to work successfully with data, it is also critical to work in an agile, incremental manner, since data-driven products are more unpredictable in nature than other products - you never know what the data will show until the products are in production, and it might change at any time.
Scling offers a partnership solution for extracting value from data. We build, host, and operate data platforms and data flows on behalf of clients. Raw, unprocessed data is ingested and stored in the platform, and data flows, adapted to each client’s needs, produce refined, valuable data artifacts of business value - anything from simple reports to machine learning services such as fraud detectors.
Building data processing capabilities in a partnership allows clients to focus on their core business, while getting value from their data and meeting competition powered by technical evolution. We have built many data platforms and data processing flows, and the base platform code (but not data) is shared between our clients. There are also many recurring data flow constructs, e.g. privacy protection, which are difficult to implement in a data platform without prior experience. We have the necessary tools and components, reducing cost and time to production for our customers.