Data Process Automation
We specialize in the data engineering components, which are the backbone that tie together all of the other data disciplines. We design, build, and deploy a data architecture to structure your data for flexibility and scalability. We build the transformations necessary to make your data FAIR (findable, available, interoperable, and reusable). We collaborate with your architect(s), your engineer(s), your analyst(s), and your data governance officer(s) to update and maintain your data pipeline so that you can get the most from your data.
Transforming voluminous amounts of data from multiple sources into actionable insights is a tedious process fraught with pitfalls. Additionally there are many distinct aspects of getting the most from your data.
- Data governance is the practice of and policies for maintaining the security and integrity of data.
- Data engineering is designing, and implementing the infrastructural data architecture to support the collection, processing, storage, and distribution of the data.
- Data warehousing is the actual storage of the data and must take into account the cost, security, scalability, and availability of the data as outlined by data governance.
- Data analysis is statistical and visual analysis of data to discover what information exists within the data.
- Data science is the process of researching the collected data and synthesizing new intelligence from it and producing actionable insights.
Data Quality Consulting
Data Quality is a challenge and it is one of the most important aspects of data management. Making sure that your data is up-to-date, free of false or inaccurate records, and tagged for exploration are all components of quality. If your process don't automate the cleaning, de-duping, validation, and tagging, you could be wasting a tremendous amount of money capturing and storing data that you can not use at all or do not trust.
Collect
The data you need exists in great abundance and in a multitude of locations. Sometimes it's in your own database, sometimes it accessible through a public api. It may change daily or by the second. The architecture you design should be flexible enough to support all of those modes of collection through a common framework.
Transform
Data Quality is a challenge and it is one of the most important aspects of data management. Making sure that your data is up-to-date, free of false or inaccurate records, and tagged for exploration are all components of quality. If your process don't automate the cleaning, de-duping, validation, and tagging, you could be wasting a tremendous amount of money capturing and storing data that you can not use at all or do not trust.
Deliver
Data Quality is a challenge and it is one of the most important aspects of data management. Making sure that your data is up-to-date, free of false or inaccurate records, and tagged for exploration are all components of quality. If your process don't automate the cleaning, de-duping, validation, and tagging, you could be wasting a tremendous amount of money capturing and storing data that you can not use at all or do not trust.
