Data Architecture

  • Big Data

  • Machine Learning

A well-designed data architecture can help your organisation to get actionable insights from your data and help you make data-driven decisions.

In the past, most data architectures were less complicated than they are now. They mostly involved structured data from transaction processing systems that was stored in relational databases. Analytics environments consisted of a data warehouse, sometimes with smaller data marts built for individual business units and an operational data store as a staging area. The transaction data was processed for analysis in batch jobs, using traditional extract, transform and load (ETL) processes for data integration.

The adoption of big data technologies in businesses has added unstructured and semistructured forms of data to many architectures. This has led to the deployment of data lakes, which often store raw data in its native format instead of filtering and transforming it for analysis upfront -- a big change from the data warehousing process. The new approach is driving wider use of ELT data integration, an alternative to ETL that inverts the load and transform steps.

The increased use of stream processing systems has also brought real-time data into more data architectures. Many architectures now support artificial intelligence and machine learning applications, too, in addition to the basic BI and reporting driven by data warehouses. The shift to cloud-based systems further adds to the complexity of data architectures.

Our data architecture solutions save time and costs, so that your engineers and scientists can focus on improving lives.

Our data architecture prioritises

  • a business-driven focus that's aligned with organisational strategies and data requirements;
  • flexibility and scalability to enable various applications and meet new business needs for data; and
  • strong security protections to prevent unauthorized data access and improper use of data.

Principles of our data architecture

We focus on building a balanced, performant & secure data architecture for your organisation. The 6 foundational principles that we follow are,

  • Data is a shared asset. A modern data architecture needs to eliminate departmental data silos and give all stakeholders a complete view of the company.
  • Users require adequate access to data. Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs.
  • Security is essential. Modern data architectures must be designed for security and they must support data policies and access controls directly on the raw data.
  • Common vocabularies ensure common understanding. Shared data assets, such as product catalogs, fiscal calendar dimensions, and KPI definitions, require a common vocabulary to help avoid disputes during analysis.
  • Data should be curated. Invest in core functions that perform data curation (modeling important relationships, cleansing raw data, and curating key dimensions and measures).
  • Data flows should be optimized for agility. Reduce the number of times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility.

Most companies make decisions either by guessing or by using their gut. They will either be lucky or wrong.

Every company has big data in its future and every company will eventually be in the data business.

Get in touch with us for an initial consult and know how we can help your organisation make more data-driven decisions.

Cloud Optimisation

AWS, Azure, Google