HIGH LEVEL SERVICES SPECIALIZED ON DATA SCIENCE

The core of the entire process is Data: troves of rough information collected and stored in enterprise databases. There’s much to learn mining them and advanced features and capabilities we can build with them.
Our Data Science is about using your data to generate business value but in different and creative ways.

Data Science As A Service For Your Industry

IoT

Finance and Insurance

Healthcare

Telecommunication

Travel and Transportation

Manufactoring

VALUE FOR YOUR BUSINESS

Do you have extensive amount of data in your organisation misused, underused or even unused?
Are you wondering how to create value from it?
Do you already have a vision of how your data can work for you, but you lack the right skills in your team to proceed?

SKIENDA will provide you a team of dedicated Data Science experts to join your project and work on it end-to-end, analysing your business challenges, implementing a data-driven methodology and ultimately generating actionable analytics to create your competitive edge.

Our skills & knowledge

  • Structured, Unstructured & Semi-structured Data
  • Data Cleansing
  • Data Profiling
  • Normalization, Text Mining
  • Data Extractor
  • Data Transformation
  • Load Data to Data Warehouse
  • Gradient descent
  • Coordinate descent
  • Boosting
  • Grid Search
  • Mini Batch Gradient Descent
  • One-Vs-The-Rest
  • One-Vs-One
  • Expectation-maximization (EM)
  • Locality Sensitive Hashing (LSH)
  • Brute Force
  • KD Tree
  • Ball Tree
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)
  • Stochastic Gradient Descent (SGD)
  • Alternating Least Squares (ALS)
  • Uni/Multivariate Gaussian Distribution
  • Adam Optimizer
  • Relu
  • Sigmoid Language
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • SVM
  • Naive Bayes
  • kNN
  • K-Means
  • Random Forest
  • Dimensionality Reduction Algorithms
  • Gradient Boosting algorithms
  • Deep Learning & Neural Networks
    • Artificial Neural Network (ANN)
    • Recurrent Neural Network (RNN)
    • Convolutional Neural Network (CNN)
  • Model Deployment
  • Model Serving
  • Model Pipeline
  • Managed Deployment
  • Monitoring
  • Evaluation

PREDICTIVE USE CASES EXAMPLES

When a business loses customers, it needs to bring new ones in to replace the loss in revenue. That is known to be very inefficient, as the costs of new customer acquisition is usually much more expensive (and hard to achieve) than existing customer retention.

Customer Behaviour Prediction help to prevent churn in your customer base, by identifying signs of dissatisfaction, and isolating those customers or customer segments that are at the most risk for leaving. Using that information, companies can then make the necessary changes and/or action to keep those customers happy and protect their revenue, even classifying those that are going to spend the most money, in the most consistent way and over the longest period of time.

In many industries, containing costs is as valuable strategy as increasing revenue. And for companies with a major investment in infrastructure and equipment, the ability to manage that capital outlay is critical.

By analyzing metrics and data related to the lifecycle maintenance of technical equipment, companies can predict both timelines for probable maintenance events and upcoming capital expenditure requirements, allowing them to streamline their maintenance costs and avoid critical downtime.

Product propensity combine data on purchasing activities and conduct with online behaviour metrics from things like social media and e-commerce, and performs correlations of that data to provide insight into the effectiveness of different campaigns and social media channels when it comes to your company’s products and services.

This allows your company to predict not only what customers are most likely going to buy your products and services, but what channels are the most appropriate and effective in reaching those customers out, allowing you to insightfully chose those channels that have the best chance of producing significant revenue.

Your customer base is the source of both existing revenue and revenue growth for your company. Therefore it becomes critical to maximize the revenue opportunities that are possible within your market segment and product set.

Through deep Machine Learning and AI data analytics, Skienda can generate patterns and suggestions on which products might be combined to appeal to which market segments, to increase both your value to your customers, and the revenue derived from your customers.

With IoT sensors deployed in a manufacturing unit, every component is monitored in real time. Any impending failure of a part or a process is raised much in advance. With advance predictions, the issue of downtime can be eliminated.

Quality is ensured with analysis of all patterns detected in the manufacturing process leading to fail-proof manufacturing outputs. Quality control is key to not just the customer experience, but also to your bottom line and operational expenses as well. A good predictive strategy, however, can provide insight into potential quality and operational issues and trends before they become truly critical issues.

Risk comes in a number of forms and can originate from a variety of sources. A right predictive strategy can glean potential areas of risk from the massive number of data points collected by most organizations and sorting through them to identify potential areas of risk, and trends in the data that suggest the development of situations that can affect the business and bottom line.

By combining these predictive strategy with a cogent risk management approach, companies can capture and quantify risk issues, evaluate them, and decide on a course of action to mitigate those risk factors deemed most critical.

It’s very difficult to be everywhere at all times, especially in the online world. Likewise, capturing and reviewing everything that’s said about your company or organization is virtually impossible.

However, by combining web search and crawling tools with customer feedback and posts, you can have a picture of your organization’s reputation within your key markets and demographics and provide you with proactive recommendations as to the best ways to enhance that reputation.

Every government agency uses data to make decisions. Most of the time, the analysis of the data does not generate useful observations when they could have been applied in an useful fashion. At present, predictions are available in the form of results from studies that are used to make calculated assumptions.

With a powerful prediction strategy, all future expectations can be double-checked or even based totally on the capability of machine learning. This can help remove totally and completely the slow pace of decision making and implementation that governments are burdened with.

Insurance fraud costs companies and the consumers (who are subjected to higher rates) tens of billions of dollars a year. To add to the problem, attempting to prove claims are fraudulent can in turn costs the companies more than the original cost of the claim itself.

Using machine learning and predictive models to detect fraud, helps pinpoint more claims that should be researched by human auditors with a double benefits that reduces the costs of human hours and increases the opportunity to reclaim stolen dollars from fraudulent claims. With a fine tuned prediction strategy, the accuracy and rate at which your team processes fraudulent claims will increase dramatically.

Associate Partner
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Technological Partner