Your company data has value, waiting to improve your business. But building modern data processing capabilities is a risk-prone undertaking, and often too costly. A partnership with Scling is a rapid and cost-effective path to extract business value from your data.
Scling builds and operates “data factories” for our customers, including a data platform and the data pipelines that run on the platform. From a customer perspective, we provide a data refinement process, where raw data material is ingested at one end, and refined valuable data artifacts are emitted at the other end. Ingested data is stored in the platform, and available for long-term use for applications that benefit from larger data volumes.
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.Read More
All data-mature companies have chosen the strategy of collecting company data into a centralised data platform. Data is collected from systems that contain source data, from client devices, or from external parties. It is ingested to the platform, where it is processed offline and turned into refined, valuable data, which feeds analytics and data-driven features.Read More
Scling’s mission is to enable established companies to benefit from the value in data at a level and efficiency that has so far been restricted to highly technical companies. The journey to data maturity and data-driven features is a path of discovery, where steps are chosen based on learning from working with the data.Read More
Scling's primary business is data-factory-as-a-service - refining data to extract
potential value for our clients, with an "industrial" level of efficiency that is normally
reserved for a tiny set of highly technical companies. Today, almost all companies use
artisanal methods to refine data, centered around databases, data warehouses, or
lakehouses. While modern data warehouses are powerful tools to enhance human users, the
processes are primarily driven by humans, and limited by our ability to
oversee and steer processes at scale.
A tiny set of data mature, highly technical companies have taken automated data processing to an industrial level, where data value extraction and innovation happens on a completely different scale and speed. Scling's staff has built "industrial" data platforms and driven data productivity improvements in such environments, and Scling offers these capabilities to the wider range of companies. We build and run data factories.
Data factories are necessary but not sufficient to compete with the leading companies.
The disruptive tech companies did not become innovation leaders by building factories and data flows
and then collected the gold. Data factories grew over time while these companies applied data
engineering solutions to the product challenges in front of them. Driven by their immediate needs,
they developed enabling technology, but also the ways of working that are necessary for data and AI
success: cross-functional teams, data-guided product development, DataOps, etc. Changing ways of
working is challenging for traditional companies, and failure to adopt new work patterns is one
explanation to the high failure rates for enterprise data and AI projects.
Fortunately, there is a simple path to learn the necessary work patterns. It yields quick and high return of investment, and the technical leaders have taken this path, but most companies nevertheless choose other, more expensive and less rewarding paths. We offer to take you down the path to success that the leading companies have taken. We call it CorpOps.