AI & Data Science

Data Science can revolutionize your business. It can support your decisions, optimise your processes and automate tasks that were so far reserved for human brain.

The term data science is an umbrella of various fields that often work together and overlap with their predictive and learning capabilities.

AI & Data Science in Objectivity

How can you convert data into profit?

  1. Anomaly detection

    • Support humans in spotting anomalies and eliminate mistakes resulting from humans getting tired.
    • Identify fraud, network interruption or any unusual procedures that have a high impact on your business operation.
    • Increase operating margins and reduce costs by understanding and ruling out unwanted behaviours (or promoting the positive ones).
    • Monitor any data source for any abnormalities.
    • Rapidly identify unusual behaviours and rogue users.
  2. Computer vision

    • Automate processes by recognising/localizing people or objects and adjust the steps accordingly.
    • Improve the quality of your products.
    • Speed up the reviewing process of documentation.
    • Easily identify the location of objects or recognise faces.
    • Automatically detect counterfeits or fake currency.
    • Identify disease symptoms from photos.
  3. Forecasting / Predictive maintenance

    • Support humans in drawing conclusions from historical data.
    • Support planning and help business optimise processes to the most probable future
    • Reduce your costs of spare parts and maintenance.
    • Increase productivity and eliminate incidents.
    • React before potential problems even developed.
    • Increase safety creating risk-free conditions at a workplace.
  4. Natural Language Processing (NLP)

    • Support translation, voice recognition and voice generation.
    • Analyse large amounts of documentation in no time.
    • Unburden your employees from the most repetitive tasks.
    • Use a chatbot to improve your customer service.
    • Address customer concerns quickly using sentiment analysis in social media.
    • Automatically detect emotional load, intent or topic.
  5. Optimisation / Recommendations

    • Discover deeper insights, make predictions, and generate recommendations.
    • Automatically choose the best option, utilising the data at hand.
    • Improve business processes by supporting the decisions being made
    • Improve the effectiveness of your systems.
    • Watch trends for a more efficient use of your resources.
    • Build your customers profiles to tailor your offer to their actual needs.
    • Explore correlations for better assets allocation.
Data analytics in business

Our Approach

Our team of data scientists is extracting data out of various systems across an enterprise. They pull it and link it together in order to be able to look at it in as many as dozens or hundreds of dimensions.

Overall Approach

  1. Understanding the business case
    We start with immersion for all the key stakeholders to build relationships and share knowledge about the domains.
  2. Understanding available data
    We move to Terms of Reference to identify pieces of work and the way that we would like to undertake them. Timescales, costs, roles, objectives etc.
  3. Validating feasibility of the idea
    With everything agreed we start work. Often this will be with a Proof of Concept in the early days
  4. Defining the project
    As we reduce uncertainty, we move into a development phase that leads to live deployment.
Data Scientists at Objectivity

Our Team

Our Data Scientists focus on projecting future trends, events, and even behaviours. This gives our clients the ability to perform advanced statistical models and calculations, as well as future-proof their operations. Combining the use of domain knowledge with sophisticated algorithms, statistics and machine learning our scientists can solve even the most challenging business problems.

Our team consists of 12 experts in data crunching, modelling and visualisation. They are experienced in mathematics, statistics, computer science, physics, engineering, economics, neuroscience and law.

Our team works in line with CRoss Industry Standard Process for Data Mining (CRISP-DM), a comprehensive process model for carrying out data mining projects. This model is adopted and augmented for incremental approach and building from solid foundations that is coherent with principles of agility.

Our Team
Data analytics in real life

Example use cases

Digital transformation, built on smarter, more efficient and more adaptable automation systems is changing diametrically the way we work, manufacture and interact with customers. While the technology we offer is more and more straightforward to implement, the business decisions require a greater amount of data to be taken into consideration.

Because advanced analytics is such a broad discipline with a wide applicability, there are various excellent uses across many industries. We keep working closely with our clients’ business to find the most suitable business cases. Data is a critical tool in becoming more competitive on the market. This is true in many industries: IT, manufacturing, retail, energy, finance and many more.

With data and analytics, we can help you transform your business. We have years of experience improving companies in how they work, organize, operate, manage talent and create value.

Recommendation Engine for an E-learning platform:

Organisations that provide an E-learning platform for their employees face challenges when it comes to improving the effectiveness of such a training. A solution that provides recommendations for each worker can help them create their own professional development plan to achieve their personal learning goals.We built the Recommendation Engine PoC which recommends relevant content to the users. The Engine recommends content based on two factors, semantic-analysis of content (a method of Natural Language Processing) to identify an accurate match between users and e-learning content, and user satisfaction, a predictive model that enriches the semantic model by focusing on user ratings. The Recommendation Engine was able to match candidates to appropriate training with 90% accuracy using the data and algorithms built into the PoC.

Office space management which optimises employees’ allocation:

Facility managers face the challenge of employees’ allocation keeping in mind the rising costs of leases, utilities etc. There are many factors taken into consideration when planning an office space: an amount of light, noise level, the character of teamwork, etc. Planning and analysing is thus crucial for optimising occupancy of the building. Data analytics allows you to look for patterns and map correlations to avoid unused spaces and support productivity. The same applies when it comes to effective use of lighting, heating and cooling systems in the office, as well as providing sustainable design solutions that help increase energy efficiency.

Computer Vision used to enhance quality checking on a production line:

Imagine there are capabilities to detect even very minor defects on your production line. By taking standardised photos you can find potential inclusions, label them and use trained Neutral Net to classify and discriminate them. A key feature of the solution involves using computer vision to check for broken or wrongly formed packaging (in this case bottles). As bottles make their way through the production line, pictures are taken and transferred to a dedicated computer that processes the images and runs further analysis to check if bottles are the right colour, size, width, etc. This process helps increase the capability of the production line while at the same time reduces the number of failures and increases productivity and ultimately, keeps end users satisfied.

The core of our toolbox
  • Python, R, Java, Scala
  • Spark, Tensorflow, Azure ML Studio
  • Power BI, Tableau, D3
  • Classification & Regression (k-NN, Naive Bayes, SVM, Linear Regression, Decision Trees, Random Forest, XGBoost, Neural Networks)
  • Time Series Forecasting (Regression, (S)ARIMA, Prophet, Exponential Smoothing, Monte Carlo)
  • Clustering (K-Means, Hierarchical, DBSCAN)
  • Recommendations (Collaborative Filtering, Content Based)
  • Dimension Reduction (PCA, t-SNE)
Machine learning by Objectivity

How can you align your machine learning implementation to business objectives?

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