Hello! My name is Sandra. I am a Data Scientist in Advanced Analytics, where we believe in constant development, iteration and improvement. Let me introduce you to our data-driven reality in Arla.
Originally one team, currently split into four business areas to fit our stakeholder needs better. Commercial & Corporate Functions (CCF), Supply Chain (SC), Advanced Analytics (AA) and ONE Data Foundation (2DF) teams are helping and supporting other Arla teams in building reports, dashboards and BI solutions.
We are part of Analytics Powerhouse Release Train which has currently over 50 team members. Each one of them are highly skilled and experienced professionals. Analytics Powerhouse is responsible for the Business Intelligence (BI) data foundation that is used across Arla’s organization to measure business performance. Whether you want to follow up on global KPIs or would like to have a snapshot of how the business is performing right now, Analytics Powerhouse ensures that we have ONE source of truth coming from ONE source of data.
Among our multiple responsibilities you can find:
If you haven’t heard about the Data Foundation - it is Arla’s Data and Analytics Platform and is the solution, that enables Arla to execute advanced analytics cases, such as: big data processing and reporting; self-service analytics; near, or real-time data streaming; implementation of Machine Learning (ML) and Artificial Intelligence (AI) solutions; extracting data from internal systems and procuring data from new sources for either analysis purposes or use in other solutions; building reports and cloud based solution on top of ONE Data Foundation, performing Proof-of-Concepts (PoCs) and experiments in order to uncover the value of using Machine Learning and AI.
Current Advanced Analytics competences include:
To become a data engineer you need to excel your SQL, data warehousing, data architecture, coding (Python, C#, Java), operating system, machine learning and Apache Hadoop-Based Analytics.
To become a data scientist you need to understand machine learning, have a good skills with R, SQL and Python programming (especially in data preparation and visualization), have an analytical mind, problem-solving aptitude and good communication and presentation skills.
To become a software engineer you need hands-on experience in designing interactive applications but also with technologies like Hadoop, Hive, Pig, Map Reduce, Spark. You should be able to develop software in C, C++, C# or Java. Experience with test-driven development and SQL knowledge are also significant.
To become a power BI developer you need in-depth understanding of database management systems, OLAP and ETL. You should be also familiar with BI technologies (e.g. Microsoft Power BI, Oracle BI). Knowledge of SQL queries is essential. A good candidate required analytical mind with problem-solving aptitude.
Article written by Sandra Radgowska, Data Scientist at Arla