Structured Data

Keep your knowledge in safe hands

Driven by data

Many business processes are recorded in tabular datasets like Excel sheets, relational databases, and time series. Thanks to our Machine Learning expertise, we turn your structured data into valuable insights that help you solve a variety of problems.

Regression & forecasting

Predicting the future is hard, but with the right tools, we can forecast trends in e.g. energy consumption or sales volume with precision. We do this by using external data sources and taking advantage of the latest model improvements.

Classification & clustering

Labeling data records adds value to large data sets and automates actions. It involves creating different labels (clustering) and assigning them to new data (classification). This technique can be used for detecting machine failures, sales abandonment, and clustering e-commerce users.

Anomaly detection

We are experts in detecting anomalies in machine and process behavior. Our strategy is to separate "abnormal" events from "normal" behavior to gain insights into causality and explainability and use them to optimize your systems.

Operational research & optimization

Even if a process works well, it can always be improved. This is where our expertise comes in. We specialize in tackling complex problems such as production planning, job scheduling, vehicle routing, box packing and more.

Client cases

Discover how our expertise in Hardware & Sensors leads businesses to success

Typical challenges

With our expertise, we can help you overcome structured data challenges in AI.

Aligning the technical problem formulation with business problem

Before starting machine learning (ML) model training, you need to understand the business requirements and available data. This includes deciding whether the problem should be approached as a regression or classification task, or whether ranking or recommendation is required. The success of the technical implementation is ultimately determined by meeting business expectations, which we always strive to achieve.

Validation is hard

Unsupervised learning uses tools like clustering to identify data patterns, but the results can be difficult to interpret. That’s why domain experts during development need to make sure that the outcome is accurate. It’s also tough to identify causal relationships between variables and labels may not be available in situations like fraud or predicting machine failures. However, once you overcome these challenges with our help, unsupervised learning is a powerful tool for uncovering hidden patterns and gaining insights from data.

Data engineering and change management

Building a successful solution for structured data requires a lot of data engineering and change management effort. Moreover, machine learning system development can lead to hidden technical problems such as poor data quality, model complexity and deployment challenges. To create long-term value, it’s not enough to simply train an ML model. Validation, integration into existing systems, ongoing monitoring and updating are essential to deliver real value over time. We help you do just that.

High level outline of the solution

Data collection

The first step in any structured data solution is data collection. This can be done from a variety of sources, including internal databases, APIs, and third-party data providers.

Data cleaning and preprocessing

Once data has been collected, it must be cleaned and preprocessed to make sure that it is of high quality. This includes tasks like removing missing values, handling outliers, and converting data types. Exploratory Data Science (EDA) is essential.

Feature engineering

Feature engineering involves selecting and transforming the features in the data that are most relevant to the problem at hand. This can include creating new features, selecting important features, and scaling or normalizing features.

Model training and selection

A machine learning model can be trained after data has been preprocessed and features have been engineered. The selection of a specific model is based on how well it performed on a holdout dataset.

Deployment and monitoring

Once the model has been trained and evaluated, it must be deployed into production. This involves integrating the model into existing systems and workflows and monitoring its performance over time.

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Connect with our AI experts in Structured Data

Contact us to turn your structured data into valuable insights that help you solve a variety of problems

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