Make well-informed, fact-based decisions with data science

Data science is a valuable method for solving real company problems using data.  It provides companies with the ability to process and interpret data and it supports companies in the process of making informed, fact-based decisions. Say goodbye to basing business strategy just on a gut feeling and make it complementary to data analytics to uncover and improve business inefficiencies. 

Data science has a multitude of application areas by which it can provide value to every company out there.

Today, more and more organizations continuously gather an increasing amount of data. This data can bring transformative benefits to organizations, but only if we can interpret it. Unfortunately, this data often just sits in databases and data lakes, mostly untouched. Data science provides the possibility to reveal the hidden information in gathered data.  

The power of data science

The power of data science lies in the ability to discover unknown patterns and produce insights into data that cannot be observed in other ways. Collected data helps companies make better decisions, automate processes and provide innovative services. 

Bright Cape, your partner in data science solutions

At Bright Cape, we are experienced in developing and deploying full data science solutions that increase efficiency and decrease costs. Our solutions can be divided into two categories:

  1. Insights
    Data is gathered, and analyzed and the results (information) are fed back to the internal systems of the client.
  2. Data products
    We build a solution that matches your specific challenges, vision, and business strategy. We create a suitable interface that reflects the right KPIs and we deploy it either via the cloud or on-premises at the client.

Guide: How to achieve Operational Excellence with Manufacturing Analytics

Manufacturing analytics is a powerful, data-driven methodology that will help you uncover hidden insights, streamline operations, and reduce costs. It will effectively support you in understanding cost drivers in operations, which will lead you to unexpected insights to improve profitability.

This operational cost optimization guide will provide you with a blueprint to help you unlock the full potential of manufacturing analytics.

State of the art methods and data science solutions

We guide our clients in their digitization journey by building and implementing scalable, sustainable data science solutions while making use of the latest innovations in Machine Learning and Artificial Intelligence (AI). We do this with both standardized and tailor-made solutions. In addition to that, we apply an agnostic approach, meaning that we match the customer’s needs with the best-fit tool.

Data gathering

  • Connecting with existing internal and external data sources.
  • Augmenting existing data with additional retrievable data, e.g. gathered via web scraping.

Data analysis

For all our solutions, we use analysis and prediction methods that are use case-specific and tailored leveraging domain knowledge from the client. Algorithms can both be traditional statistical methods, state-of-the-art machine learning methods, or a combination, depending on what fits the use case best.


All our solutions can be deployed 

  • on-premise or 
  • in the cloud (Azure) 

By implementing Bright Cape’s data science solutions, you reap the next benefits:

  • Hidden information is extracted from large amounts of data → relationships in big pools of data that cannot be found with the human eye data are turned into actionable information that supports business decisions and can be used to steer upon.
  • Decision-making is improved with quantifiable evidence → It can be calculated what the actual impact is of the current situation and what the difference will be after making a decision, as such fact-based, data-driven business decisions can be made, instead of relying on gut feeling 
  • Predictive analysis is performed → Business decisions can be based on information about what is expected to happen in the future, e.g. Is a machine likely to break down. 
  • Prescriptive analysis is performed → Optimal actions to take are prescribed in order to optimize a process, e.g. Which machine parameters to change to what settings to optimize production quality.
  • Manual tasks are automated → By analyzing data from different sources automatically, manual checks can be automated and more time can be spent on unique cases.
  • Processes are optimized → All of these changes result in optimized processes with reduced costs.

Our 3-way support system

Our mission is to support organizations in using their data for good and stay ahead of the curve. Together with our clients, we make sure their wishes and requirements are properly mapped out to ensure that our solutions meet their real needs. We strive for strategic, operational, and deep technical knowledge.

  • Strategic support: Taking the customer along the whole data science maturity curve, starting with data quality and moving up to prescriptive analytics. → With the help of our expertise, we define together your challenges and possibilities in the data domain. We provide strategic advice on new value propositions and revenue streams, resulting in a realistic data roadmap.
  • Operational support: Given all our data science, analytics, mathematical, statistical, and human-centered design experiences, we can give operational support by creating customer-specific solutions. → Thanks to this variety of experiences and skills, we are able to understand your problem and propose a solution that fits the corresponding data maturity.
  • Technical support: Given the experience in implementing complete data science solutions, different coding languages, and technical knowledge of the solution, we provide technical support reliably and fast.

Curious how we do it?

Our proven methods and data science solutions enable organizations to extract valuable insights from their data. Our solutions have proven successful in the banking, finance, manufacturing, and logistics & warehousing industry. We have numerous validated use-cases from our elaborate client portfolio, ranging from Compliance (Know your customer), Predictive maintenance, Anomaly & pattern detection, and Product quality control & prediction, and more.

Anomaly & pattern detection

Pain points
(Potential) issues are detected late, resulting a.o. in a machine or system failure, with the following impact:

  • Downtime results in lost production time.
  • Degradation of machine performance and suboptimal production conditions lead to a decrease in production quality and increased scrap.

Life after implementing anomaly detection solution

  • Ability to detect anomalous process and asset behavior and perform root cause analysis.
  • Able to timely make process interventions in the case of issues.
  • Increased understanding and regained control of the process.

Resulting in increased productivity and profitability

  • Reduced downtime / higher availability.
  • Increased performance.
  • Increased production quality.

Know your customer

Pain points
Before implementing the solution, a lot of manual effort is required to do all kinds of validation checks on a potential customer. These checks are intensive and it is easy to make a simple mistake. In addition to that, not all required information can easily be extracted by a person looking for information online. Last but not least, relationships between different data sources/large amounts of data, are too difficult to see for a normal person.

Life after implementing
After implementing the solution, the employees can use their time more effectively by validating ‘difficult’ customers instead of spending all this time on parts that can easily be automated.

Product quality control & prediction

Pain points
Bad or decreasing production quality

  • Late detection of faulty produced products, hence production of scrap.
  • Inaccurate quality check, as it is based on random sampling.

Life after implementing

  • Automated individual product quality checks instead of checking random samples.
  • Allowing users to timely adapt process in case it is expected that production quality decreases, resulting in scrap reduction.
  • Consistent and high individual product quality.
  • Personnel costs decrease, as quality checks are automated.

Predictive maintenance

Pain points
Reactive maintenance, as a result of unexpected machine/system failure.

  • Lower productivity due to lost production time.
  • Unexpected asset failure leads to increased resource costs, both in terms of replacement parts and labor.

Life after implementing

  • Prevention of future unexpected asset failure, resulting in decreased downtime.
  • Proactive, insight-driven maintenance.
  • Longer product lifetime as wear issues predicted and resolved.

Want to know more?

Want to learn more about how data science can help your business effectively predict product quality? Then request our data science demo!