Welcome to the Nightingale HQ overview of data science development services. Here we aim to introduce people to what they need to know.
Data science development is the development of custom data science solutions (algorithms, models) from the data source to the deployment of the models. The development leads to solutions that can be used to gain valuable, actionable insight from your data that improves in accuracy over time and to build intelligent products that generate profit for your business.
Data science can be applied in all areas of your business. It helps development teams to improve the products they build, helps sales and marketing teams with market segmentation and advertising, and supports financial decision-making. Developing custom data science solutions as opposed to using pre-built solutions empowers your team to gain custom, tactical insights and build innovative products.You may need this service if:
you are building a product that could be improved by intelligent features.
your team has access to large volumes of data but is not using it to make predictions or decisions.
you want a solution that is customised to your specific data sources and tactical goals.The KPIs you should consider measuring for this are:
increased sales of your product
improved customer retention
increased lead generation
improved cash flow
Data science development can be applied to your product by integrating custom usage data collection solutions and implementing innovative features that analyse the usage data and apply custom algorithms to the usage data to drive improved performance and user experience.Data science development helps deliver:
improved performance through analysis of usage data.
personalised content that can drive increased up-sells and interactions.
custom models that will transform, analyse and generate insights from your data to drive business goals.
Get this service if you encounter:
missed opportunities to apply usage data to improving performance.
lack of personalised content in your product.
lack of understanding of how your product can be improved.
difficulty integrating off-the-shelf data science solutions that suit your specific needs.Key criteria to consider are:
Do you have appropriate data storage solutions in place that will keep user data secure?
Do you have the time and resources to apply your analysis to make improvements to your product?
Does your product have enough users to generate actionable data insights?
What are the cost implications of developing custom solutions and how will the benefits compare to those using a pre-built solution?