Barriers to understanding
With more and more interactions taking place in the digital arena, data is now one of the key assets for most businesses. Success for many enterprises will increasingly depend on the ability to empower business users to analyse and make sense of the enormous volumes of data that flow in each day.
Data science is the fast-growing discipline that seeks to support enterprise decision-making based on data rather than gut-feeling. While tools for data scientists are relatively mature, there remain two important barriers for many organisations. The first of these is the difficulty of understanding what data needs to be processed, and then finding and preparing it for analysis. And the second is knowing how to interpret and democratise the results, so that whatever is learnt can actually drive practical outcomes for the business.
Business and technology
A critical factor in building a data-science capability is to bring together business and technology skills. Hiring in a data scientist who has no knowledge of your business is likely to create an isolated bubble of capability. Beautiful and mysterious graphs and forecasts will appear, and business users will not know how to interpret them. And the real questions that business users have will tend to remain unanswered.
Northdoor offers an innovative data-science-as-a-service function that provides skilled data scientists backed by business analysts with decades of experience in helping enterprises with data analysis challenges. Northdoor’s team understands the potential benefits of applying data science to sets of business data, and we can guide your organisation in going from data to predictions.
Industrialise your data-science workflows
For businesses that wish to build up their own internal data-science function, Northdoor works with leading cloud vendors to provide easy access to sophisticated tools and platforms. We can help you build repeatable data flows, automatically cleaning and transforming new data and then securely transporting it to the cloud for analysis. By enabling your data scientists to focus on the data science rather than the sourcing and preparation of data, we maximise the value to your business.
Our chosen cloud platforms for data science provide pre-configured environments that can scale out in seconds to meet spikes in demand, and that take advantage of the latest GPUs for deep learning. The platforms support multiple languages including C#, julia, Java, Javascript, R and Python, as well as data platform tools such as Apache Spark and exploration tools such as Apache Drill.
Extend in the cloud
One of the advantages of hosting data science in the cloud is the ease of plugging in additional capabilities. For example, the addition of cognitive services such as natural language processing could allow business users to write queries in plain English rather having to learn SQL code, potentially enabling them to work directly with models built by their data-science team.
In practical terms, data science in the cloud also makes good sense. Virtual desktops for data scientists ensure a consistent experience across all team members, facilitating the all-important sharing and collaboration. Users can use pre-built templates and Jupyter notebooks, or take advantage of packaged extensions for machine learning that make it easy to build end-to-end pipelines.
Northdoor has comprehensive experience in helping businesses deploy data-science solutions in the cloud, backed by decades of solving data-related challenges for blue-chip organisations
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