Companies that want to retain an advantage in today’s digital world adopt data science and advanced analytics. In doing so, enterprises gain a better understanding of markets, develop more innovative products and services, and are better equipped to react to a rapidly changing environment. This data modelling tutorial provides the best practices for effective, far-reaching implementation of data science and advanced analytics for your business.
The importance of prioritising understanding business needs, and the availability and nature of data, can’t be underestimated. Every data science project should be ‘business first’, hence the need to define business problems and objectives from the outset. And in the initial phase of a data science project, companies should also set the criteria and parameters that will be indicative of project success.
Whether the goal is to use data to boost sales or something entirely different, defining a robust set of objectives from the get-go is essential if you’re to avoid an extended, expensive fishing expedition. Proper planning is key too since the complexity of a data science project demands airtight management to keep a cap on resource wastage and to yield tangible results. This data modelling tutorial outlines the key aspects of data preparation you should consider to ensure reliable outcomes.
Once you’ve defined your business objectives, the next step is to assess what data you have at your disposal, as well as what industry/market data is available and how usable this is.
Your model is as good as the data you feed it. So an initial analysis of data should provide some guiding insights that will help set the tone for modelling and further analysis. Based on your business needs, your data science expert should be able to understand how much data you need in order to build and train the model.
For some industries, one-shot learning is enough to understand the difference between various classes of data. In other cases – for example, when deep learning is applied – you may need thousands of data sets to gain meaningful insight. The data format is also critical at this stage.
Some types of data are a lot more costly and time-consuming to collect and label properly than others; the process can take even longer than the modelling itself. So you need to understand how much cost and effort is needed and what outcome you can expect, as well as your potential ROI, before you make a hefty investment in the project.
Once you’ve established your goals and gained a clear understanding of the data needed, you can move onto data preprocessing. The best method for this depends on the nature of the data you have: there are, for example, different time and cost ramifications for text and image data.
It’s a pivotal stage and your data expert needs to tread carefully when they’re assessing data quality. If there are data values missing and your data scientist uses a statistical approach to fill in the gaps, it could ultimately compromise the quality of your modelling results. Your data science experts should be able to evaluate data completeness and accuracy, spot noisy data and ask the right questions to fill any gaps but it's essential to engage a domain expert, for consultancy.
Proper preparation from kick-off will ensure that your data science project gets off on the right foot, with the right goals in mind. An initial data assessment can outline how to prepare your data for further modelling.
With a wide array of complex modelling techniques at your disposal, deciding on the best approach for your unique needs, requirements and available data is key.
For the more basic scenarios, using standard readily-available models might be enough to garner a decent set of results. The models can be trained with your data – saving you valuable time and money on model development. At this stage, you will result in a set of trained models, and the next step is to validate the results and pick the model that suits you best.
It’s a critical stage in the project since the model you choose will determine how well you’re able to meet your initial objectives. This is why it’s vital to define said objectives and needs from the very start. Requirements might include model error (bias), complexity, accuracy, data processing speed etc.
Some models, aren’t suitable for real-time data processing so, if speed is a priority, you’ll need to clarify this beforehand. Other models aren’t technologically compatible with things like drones or IoT, or they can’t be adapted to certain programming languages which can introduce extra project constraints. So, at the validation stage, you’ll pick the model that meets your specific requirements then proceed with deployment.
The model itself consists of a set of scripts which process data from databases, data lakes, file systems (CSV, XLS, URLs) – using APIs, ports, sockets or other sources. You’ll need some technical expertise to find your way around the models.
Alternatively, you could have a custom user interface built, or have the model integrated with your existing systems for convenience and ease of use. This is easily done via microservices and other methods of integration. Once validation and deployment are complete, your data science team and business leaders need to step back and assess the overall success of the project.
By starting your project with comprehensive planning and following data modelling best practices, step by step, you’ll have every chance of yielding the right outcomes and expected ROI from your project. And, for extra peace of mind, you could consider engaging an experienced data science partner.
ELEKS has delivered successful data science projects for companies across the globe. Why not tap our expertise to ensure your data science project has the best chance of success? Get in touch now!
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