What we do / Data Analytics

Data Analytics

At Gritera we assist companies in creating value from data by providing data advisory, leadership and capacity. Our expertise covers the data analytics domain from data strategy and organization to data-, BI- and machine learning engineering. Our advisors have a solid and modern toolset to assist you in realizing your strategic goals and solving complex business problems using data.

Data Analytics

Data/BI/ML engineering

  • Data insights requires collaboration with developers and architects who can design, model and implement the right deliverables - either it comes in form of a new data plattform, a data product, a BI report or a machine learning model.
  • Our advisors are experienced as team leads, architects and developers, and will ensure the realisation of your projects.
  • Typical roles and responsibilities:
    - Tech lead/architect (solution design, architecture, data modelling, test management, team management)
    - Data/BI engineer (ETL/ELT development, BI report development, data modelling, data plattform development, test, automation)
    - ML engineer (develop/train machine learning- and/or statistical models, MLOps, ad hoc data analysis)

Data strategy

  • A clear data strategy aligns your data initiatives with you want to achieve. It should be closely aligned with your company's strategy, budget, technical solutions and data literacy.
  • Gritera can assist you in defining or updating your data strategy through deliverables like as-is-evaluation, gap analysis, infrastructure blueprint and roadmap development.
  • Example topics:
    - What future insights do we need, how and when should these be delivered?
    - How do we set up a suitable cloud architecture?
    - What do we need to do to ensure regulatory compliance, e.g. with GDPR?

Organization and process improvements

  • An efficient data organization deliveres high quality insights rapidly.
  • Gritera can assist you in organizing your insight producing resources, raising data literacy and improving processes.
  • Example topics:
    - How should we organize our data producing resources and what measures can we take to improve their process around delivering insights?
    - How can we improve time-to-market and quality by utilizing elements from Dev/DataOps?

Contact us!

Feel free to contact one of the leaders for data analytics, Baste Fladmark or Jhonny Sharma.