Most industries are highly regulated and face complex challenges. We've been trusted by industry leaders to help them with their data. The priorities of each case vary, but the challenge is universal – how do you keep priority projects moving, while protecting data from the outside and within? You don’t just need data, you need access to secure, meaningful data.
DataCloak can glean insight from structured and unstructured data, and then make recommendations for implementing better systems. Below are synopses of the many ways smart data science can improve a company or organization's performance and bottom line.
Financial data is considered an essential commodity, valuable for it's ability make predictions in risk analytics, customer management, fraud detection, and algorithmic trading.
Features like Automatic Loan Approvals use the power of AI and Machine Learning to save time and money.
Data analytics can be used to decipher complex crimes and flag fraudulent claims.
Design and build better contact application centres as to improve customer satisfaction and create opportunities to cross sell.
Get the right datasets that enable you to correctly identify a customers’ risk profile. Use data analysis to inform trading strategies.
Predicting the future is hard. Insurance companies need to provide competitively priced polices while keeping profits to a maximum.
Making sure your IT systems will be future-proof, and that your staff will be up to date with the latest skills.
Accurate customer profiles can be generated, with details about loyalty, risk and sensitivity to price. Insurers can use data science to provide clear explanations to their pricing strategies.
Pattern recognition is used to analyze claims and detect fraud. We can show you meaning comparisons between “normal” and “suspicious” activity.
Automated engines can be deployed to recommend cross-selling, up-selling and various marketing initiatives.
Building better societies requires knowing how to create policies that best allocate resources. Data science can help with:
Data analysis can help increase the speed and accuracy of diagnoses, expedite treatments and even identify at-risk patients with no presenting symptoms.
Develop better long-term planning by predicting demands for new schools and courses, based on variables like demographics and technological changes.
Analytics can be used to detect all levels of tax evasion and fraud. It’s possible to identify patterns of behaviour that are red flags.
The right datasets can help governments assess the viability of public projects with great efficiency, reducing the need for costly outsider consultation fees.
Determine how policies impact the economy. Make economic predictions that may influence policy.
Data science can be used to improve patient health, inform drug development and ensure regulatory compliance. It can also make predictions about pandemic risks and trends.
Manage large data sets by Migrations from SAS to R and retrain teams to be updated on new systems.
Allow teams to create, execute, review and compare models through an intuitive, easy to use interface with a short learning curve.
Reduce drug development times, while meeting safety compliance. Determine future trends in Biotech and the way technology is changing healthcare.
Develop user-friendly applications that support an analytics based environment.
Customer engagement is paramount in the media and entertainment industries. What makes them buy and what keeps them coming back? Data science can give you informed predictions so you can up your game.
We can show you what’s really connecting with consumers and what’s not. Real time data can be analyzed to give you a better understanding of customer behaviour and engagement.
The tell-tale signs of a customer losing interest can be unveiled, so that you can create the best retention strategies.
It’s possible to determine which tactics will work to get the highest response rate for each customer.
Fake accounts, hackers and stolen credit card information can be detected by implementing system alerts.
Retailers must adapt to consumer trends and technology to remain competitive. Data science can dissect customer shopping habits and show how retailer practices like chatbot use, drop-shipping, markdowns and promotions all affect business.
Understand each customer deeply so you can market effectively for life.
Match the best promotional strategies to suit the right customers. Create targeted campaigns that keep them engaged.
Identify competitors and know how to price for maximum profits. Learn how different variables influence pricing.
Understand your segment of the industry as it relates to overall consumer trends.
Predict high demand periods and coordinate with suppliers to quickly respond to sales trends to ensure availability and fast delivery.
Challenges in this industry include ensuring consistent supply, meeting environmental targets and remaining competitive by delivering cost effective energy and services.
Determine competitive market prices, with data segmented into customer types and regions.
Assess and anticipate the impact of new tariffs and product developments, analyze strategies that reduce customer dissatisfaction.
Identify risk with forecasting models that use historical data. Know when preventive measures are required to minimize downtime.
Use data to make inform decisions about how to boost workforce output, identify skills gaps and implement the best recruiting strategies.
Determine the ideal locations for base stations, so that the best network coverage and customer service can be provided.
A large volume of highly valuable data is generated via GPS, iot devices, traffic monitoring and e-commerce. This data is often unstructured and complex, making it difficult for companies to extract useful information. DataCloak can turn information overload into smart strategies.
Analyze how delivery trends, daily volumes, climate, and economic data impact efficiency. Use data knowledge to improve delivery routes, better allocate resources and reduce fuel costs.
Get higher profit margins by using advance analytics to match pricing to real-time costs.
Create predictive modelling to prevent accidents. See how variables like traffic, road conditions and weather can be used to establish preventative safety measures.
Identify patterns of high demand times so that you can develop strategies for allocating resources depending on need.
Predict potential problems before they occur. Data from vehicle and equipment sensors can be used to schedule repair work before possible failure.
