Data Science Solutions

Adastra is your partner of choice for helping your teams integrate artificial intelligence and machine learning into your business processes to maximize business value.

Organizations have spent a wealth of financial resources and man hours on establishing their centralized data stores and data integration pipelines. Having accessible data is important but the key requirement of most organizations has been deriving insights from this wealth of data. Through the information management journey, Adastra has helped organizations integrate strategic operational reports and dashboards within their business intelligence roadmaps. However, the more recent shift has been in deriving deeper insights beyond summary reports and metrics. This is where predictive and prescriptive analytics is important and, like a number of our clients, has become a fundamental part of Adastra’s landscape.

Data science projects apply artificial intelligence to describe patterns, trends, correlation, and causation within data. The machine learning models implemented can range from simple linear models to complex artificial neural networks. The projects can also be quite varied in their targeted outcomes - algorithm that support operations, forecasting exercises, cognitive intelligence applications for handling unstructured data, graph-based analyses, real-time analytics, among others. The data volumes can range from small data samples (MBs) to enterprise data volumes (TBs) depending on the scope and project phase. In the context of business, data science projects target a business problem and the outcomes are used to promote greater efficiencies and improved profitability.


Data Science Solutions

Algorithm Development: Establishing algorithms to support scalability and diversity of data leveraging tools such as big data platforms and graph-based systems. Building algorithms to support information traversal and retrieval.

Unsupervised Learning: Using inferential methods to discover patterns, relationships, and correlations within the data. Largely built upon two fundamentals – dimensionality reduction and clustering. Solution leveraging these techniques can be useful for uncovering groupings of customers or products that share similarities.

Supervised Learning: Inferring a functional representation for your data from labelled training samples. Applications include a wide range of classification and regression tasks.

Simulations and Optimization: Devising mathematical models with constraints that can be solved to determine optimal business decisions, whether it be for costing or pricing, warehouse management, supply chain and logistics, etc.


Data Science Implementation

Integration with Current Systems: Building machine learning models that can capture results and feed the insights directly to client systems.

Web Development and Deployment: Deploying models that can be communicated with through APIs and deployed through a graphical web interface.

Visualizations and Reports: Summary reports and dashboards can be built on top of real-time information that is past into model interfaces, as well as gathered from model outputs.

The typical data science project follows an agile, iterative model development approach. Below shows our typical data science project life cycle: