Building a Modern Data Landscape
Your organization understands the value of data, but struggles to put the right data in the hands of the right people at the right time. A modern data landscape is essential in your organisation when you want get value out of all you data. To build a modern data landscape you need a modern data stack, including a data warehouse that allows you to build data sets that are: up to date, reliable, maintainable, documented, well-governed and ready for cloud. But also, intelligent tools and the right skills in your team are needed for a foundation on which you build an organization where data democratization is a reality.
Ending of the Traditional Data Warehouse
The traditional data warehouse is moving to the cloud and a new stack of tools, often driven by open source initiatives have changed the playing field beyond recognition. Until recently, data was “owned” by IT departments. Business units such as marketing, used the data to make business decisions, but they always had to go through the IT department to get the data. The goal of data democratization is to have anybody use data at any time to make decisions with no barriers to access or understanding. Data needs to be generally available and a modern data warehouse enables that data democratization. A radical paradigm shift has started in the Business Intelligence (BI) space and the end of the traditional data warehouse is near, ready to be replaced by more relevant architectures that can be deployed on the cloud within the blink of an eye.
The Modern Data Stack
A modern data stack is centered on a powerful data warehouse. Data is loaded directly into the warehouse. A robust and reliable transformation layer is used to turn that raw data into dependable and meaningful datasets. As a company we have partnered with many key players in the ecosystem and below we give suggestions for solutions to start using in your organization.
- Cloud Data Platform: Azure, AWS, GCP
- Modern data warehouse: Amazon Redshift, Google BigQuery, Snowflake, DataBricks
- Business Intelligence: Tableau, PowerBI. Looker
- Transformation and Orchestration: Apache Airflow, DBT
- Data Ingestion: Fivetran, Stitch
We can help you make the best selection possible based on the unique setup at your organization.
Allows you to build data sets
The Modern Data Team
As data tools change, so do the people who use them. Business users want to self-serve and be data-driven so the data has to be ‘ready’ to use. Recently, we introduced the role of Analytics Engineer to act as the bridge between data engineers and data analysts. It is their job to build well tested, up to date and documented datasets that the rest of the company can use to answer their own questions. They are technical enough to easily be able to apply software engineering best practices like version control and CI/CD but also need to be able to communicate effectively with stakeholders to teach them about these things.
Doing Analytics right, by getting the right data in the hands of the right people at the right time. Fully automated and Iteratively!
SOME OF OUR TECHNOLOGY CONTRIBUTIONS
Machine learning engineers
Why an Analytics Engineer
A bridge between data engineers and data analysts
The role of analytics engineer recently emerged because organizations now understand the value of data-driven decision making and you have trained domain experts to become analysts and hired data engineers to make the data readily available. However, there is often not enough synergy between the two roles.
Are not domain experts but focus on building a generic infrastructure, so they: Have fewer points of contact with different departments (e.g. finance, sales, marketing). Are less attuned to the data needs of different stakeholders and departments. Have limited experience using the tools such as Excel, Tableau, Power BI, or Alteryx.
Improve the accuracy, reliability, security and speed of delivery of analytics workflows. Speed up the organization-wide adoption use self-service analytics. Start a sane, agile data governance that focuses on value, quality and use of data instead of on process and roles.
Have no or very limited programming skills. Are unsure how to benefit from version control, documentation, automation, or testing. Have limited experience with a modern data warehouse with tools such as DBT, Stitch or those provided by Azure, AWS or GCP.
Work as an Analytics Engineer?
Come join our growing team of Data Experts