Data services like you’ve never experienced. From migration, engineering and more. Show your competitors what real growth looks like.
In order to succeed, you need to understand your entire data landscape, have a clear strategy that allows for how to source and store your data as well as how to use it for optimal effectiveness.
Sounds simple, but in practice it’s much easier said than done. Many companies are struggling utilise their most valuable asset – their own data. On top of that, big data systems can be expensive. So you want to have the best solution to cater to your size and needs.
Insights can improve customer experience, reduce costs and uncover value, driving innovative solutions.
To be data-driven requires good quality data, made visible, allowing for easy surfacing of those insights.
Benefits of a Data Services
Know Your Customer
With data available you can make the best business decisions to increase the customer experience.
Arranging the data so that everyone can access what they need when they need it.
Reduction in Errors
Having the right data and being sure that it is correct at the time of extraction.
How we can help?
We strive to make data clear, visible and accessible for organisations. We work to enhance data quality and make data platforms robust and scalable, allowing companies to harness their data, whether it be for machine learning and AI, analytics and business insights or simply providing information quickly and easily to applications.
We can help you create a vision for your data-driven organisation with the right assets, governance, talent and culture. We aim to optimise and automate processes, consolidate your data sources and create a business environment that’s prepared for the ever increasing data that systems consume.
We can help you design a framework to break siloes, develop the right skills and mindsets and create new processes and behaviours in order to produce your desired business outcomes.
Here's Some Of Our Capabilities
Data engineering implements, uses and maintains systems and processes to ingest raw data and produce quality information that supports for analysis and business intelligence insights as well as including machine learning.
Data engineers design and build pipelines that transform and transport data into a format that is usable by other end users. The pipelines take data from multiple disparate sources and collect them into a single warehouse that presents the data as a single source of truth.
Data science is the process of using tools and techniques to draw targeted, clean information from large volumes of raw data. It can be used for anything from business decision insights making to complex analysis or predictions.
It’s responsible for bringing us insights, new products and offering us more and more convenience on a daily basis by gleaning valuable insights from vast repositories of data and making those connections and patterns that are impossibly difficult for individual analysts to achieve.
Data semantics is a fairly abstract concept, but essentially it’s the means by which everyone agrees and understands how certain words are used together to convey a certain meaning.
Consider it as a referencing system. It is to data what a cataloguing system is to a library.
A knowledge graph is an interconnected layer of data with a strong semantics structure that gets richer as new data is added. This allows for deep, dynamic context and becomes more and more useful as it grows, bringing together siloed data and connecting it, regardless of its structure.
The purpose of a data strategy is to use data to align with business goals and discover opportunities to create value.
It should encompass the management of all data assets as well as the generation of business intelligence and analytics insights.
Data classification is the organisation of data into categories and types that are useful for searching, storing, manipulating, managing, reporting and understanding it for all types of business needs. It’s critical for security and privacy, managing risk, governance and compliance.
It helps organisations understand where their data is stored, where the most sensitive data is kept, what’s in it and who it belongs to.
Data Risk Mitigation
Data mitigation can be achieved in four main ways: backups, encryption, firewalls and strong passwords. These methods should be detailed in your data management strategy as well as your organisation’s policies.
Determining who can access your data and how, as well as how it’s shared and collected should all go into your risk mitigation planning.
Data Ops Scaling
Data Ops Scalability encompasses an organisation’s ability to grow their data, users and complexity. This includes not only the ability to handle and process large volumes of data, but also increase the number of people who need to work with it as the data ingested increases. Lastly, the people working with it need to be empowered with the right tools.
Data governance encompasses the people, process and technology involved in protecting and maintaining the data assets of an organisation. It aims for a cross-functional corporate framework to ensure the data is understandable, complete, correct, trustworthy, secure and discoverable.
Let's Make The Most Of Your Data Through...
A strategy that’s tied to your business goals
High-quality, accurate data
breaking down siloes that are hard to access or understand
promoting ownership so it’s less clear who owns key considerations: where it’s stored, how it’s cleaned, who has access, etc.
Effective reporting and visualisation
Robust, scalable data systems
AI-based analytics systems (Data lakes can hold billions of data points. Turning this into actionable insights requires AI-based solutions)
Robust data governance
A robust data architecture that aligns with your goals, your culture and surrounding context.
Let Data Transform your Business
Make your data your greatest asset to harness powerful business decisions and excellent customer experiences