What is data science development? (GoSmarter Documentation)

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Definition of data science development

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.

Executive view

The advantage of developing custom data science solutions for your business is that the models deployed are tailored to driving your own organisation’s data and strategic goals. The solutions can, therefore, lead to better quality products that can be used internally for driving better decision-making or sold externally to gain revenue.

Data science development helps businesses:

to develop custom data science solutions to drive better decision making.
to develop innovative products to build revenue.

Business function leader view

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
increased profits

Technical view

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?

FAQs

What does data science development look like in metals manufacturing?

GoSmarter’s own core products are examples of data science development applied to manufacturing problems. The Cutting Plans optimiser was built as a custom solution to the cutting stock problem — a well-studied combinatorial optimisation problem, but one that needed a purpose-built genetic algorithm tuned to the realities of steel bar stock, order profiles, and offcut economics rather than a generic library. The MillCert Reader was developed to extract structured data — heat numbers, chemical composition, mechanical properties — from mill certificates that arrive in dozens of different supplier formats, a problem generic document AI does not solve out of the box.

Both are the result of the same process this article describes: identifying a data source (cutting orders and stock, or mill certificate PDFs), building and training a model against that data, and deploying it as a production feature that gets more accurate and more useful over time.

Should a manufacturer build custom models or buy an existing product?

For problems that are genuinely unique to a single business — a proprietary process parameter, an internal scheduling constraint no one else has — custom data science development, in-house or via a consultant, is often the only route. But for problems that are common across many manufacturers — reading certificates, optimising cuts, tracking traceability — someone has usually already solved it, tested it against a wide range of real-world data, and continues to improve it. Building that from scratch is expensive, slow, and rarely reaches the same maturity as a product built specifically for that purpose and refined across many customers.