Organizations, large, medium, or small, operate in a world where every competitor is trying to leverage insights from operational, sales, marketing, or third data party sources. Traditionally, these insights are derived from ad-hoc analysis, spending many resources and efforts using a snapshot of historical data whose origins are usually on-premises silos of information, poorly maintained data warehouses, unorganized shared folders, spreadsheets, documents, and even emails. This problem requires a large amount of human talent to be wasted managing aspects of information systems that should exist only to make it easier for the business to generate insights in the first place.So, how come even though the data that exists within an organization is still cumbersome and challenging to master?
When used correctly, technology is usually imperceptible, but it makes everyone miserable when it fails, and here is wherewe come in. CrunchLab’s approach to the problem is that we use our extensive data engineering know-how and experience to diagnose the state of your information systems and work to improve and optimize the way your data is organized and managed to make it easier to use for downstream decision-making processes. Our approach is value-driven, meaning we organize your information system to match the end business goal. We make every effort to understand your business process before prescribing a solution so you know what you are receiving is molded by your business need.
These optimizations usuallydon’t happen overnight andcome incrementally from alleviating or eliminatingthe dependencies on on-premise hardware by migratingto the cloud usingthe cloud provider that best fits your needs (AWS, GCP, and Azure), decoupling storage from compute requirements for better IT resource management, discovering and consolidating your data sources into a more manageable data lakeand master data,developing or re-engineering data pipelines with faster and more efficient implementations, and help you implement your data governance strategythat will facilitate lean IT operations.
Regardless of the algorithm used, if garbage comes in, then garbage will come out. Our data science approach is data-centric, a concept that places the quality and understanding of the data generating process as a first-class citizen in the data value chain. We are proud to say the track records of our algorithms have proved our method in operation in different industries.
