Journey to data maturity.
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 learnings from working with the data.
Judging from media coverage, the purpose of data management seems to be AI and machine learning. While these are examples of new classes of features that require data to work, few companies master data management well enough to be able to create machine learning features in a sustainable manner. Achieving that level of data maturity is a journey, changing not only technology, but also workflows and team collaboration patterns.
The stages in this journey are known as the AI hierarchy of needs:
Data collection. Measuring or obtaining the necessary data and collecting it.
Move & store. Make the data flow to a storage where it is accessible and can be analysed.
Explore & transform. Reliable basic processing of individual data records, including cleaning, curation, and enrichment.
Aggregate & label. Processing that combines data from multiple sources and over time ranges. This stage enables business insights analytics, but also basic data-driven features, as well as feature engineering, which is necessary for machine learning.
Learn & optimise. Measuring, aggregating, and analysing the effects of experiments and changes to products, in order to guide feature development and algorithm improvements.
AI features, machine learning. Given that all of the above capabilities are in place, it is possible to build and maintain sustainable machine learning products at scale.
It is possible to make proof of concepts or one-off products with simple dependencies to explore higher ladder levels. While such examples are frequently shown off in media, the reality for most companies is a step by step journey during many years. Data mature companies stand out with a high minimum level of data literacy, i.e. all technical teams in the company are able to perform at higher stages in the hierarchy. Most companies are working on getting their baseline to level one or two, with occasional examples of higher levels.
Fortunately, the journey to data maturity and products driven by machine learning is not a long, costly uphill climb. There is immediate value in the earlier stages of maturity, and high return of investment. Scling’s offering is an accelerated data journey through a partnership where we guide you how to best make value from your company data, and also take care of the difficult technical challenges and operational risk.