Business Analytics

Center of Statistics and Development
It is difficult to envisage an organisation that would not be advantaged by a more efficient data process, whether this be acquiring the specific form of data required, or taking the data obtained and making more purposeful use of it. CSD can work with you to ensure that the process is implemented effectively, allowing perceptive interpretations to be drawn that should inspire improved policy developments and more informed decisions.

Predictive Analytics

Predictive analytics is a technology marketers can use to create disruption using the Big data. Predictive analytics uses many techniques from fields such as data mining, statistics, modeling, machine learning to analyze current data and make predictions for the future. We take your structured data, and data mine through the noise to uncover important predictors . We even can incorporate your unstructured data (e.g.Sales and market forecasting) into our models.

Segmentation

Use your data to identify key customer groups who exhibit certain behaviors you desire, which in turn allows you to create customer-specific marketing campaigns based on pre-identified risks and opportunities.

We cover a wide range of business sectors:

  1. Communications

  2. Transport

  3. Media

  4. Retail

  5. Finance and Insurance

  6. Health care

Actuarial Analysis

An actuarial analysis  is a method  for analysing and managing the financial risks of a business. Actuarial science is a deeply sought-after field that plays a high role in the success of a company. Actuarial analysts help accredited actuaries make strategic decisions and communicate solutions for deeply complex financial issues.

Many types of companies find value in hiring actuarial analysts. Some of the most common employers of actuarial analysts are insurance companies, consulting firms, government, hospitals, banks, and investment firms. There is typically a divide between those in life disciplines (life and health insurance) and those in non-life disciplines (auto, home, and property insurance). 

 

Big Data Analytics

 

The size and number of available data sets have grown rapidly as data is collected by devices such as mobile devices, cheap and numerous information-sensing Internet of things devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.

 

Some Application of Big Data:

 Government. The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation,[ but does not come without its flaws. Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to deliver the desired outcome. A common government organization that makes use of big data is the National Security Administration (NSA), which monitors the activities of the Internet constantly in search for potential patterns of suspicious or illegal activities their system may pick up.

 

Health. Big data analytics have been used in healthcare by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries.

 

To drive real value from big data, we take the complexity out and turn it into something simple and actionable.

 
Banking

Data is the basis of strong business intelligence, helping leaders to make decisions that go beyond surface level-analysis

Data analysis tools such as Splunk and Microsoft’s Power BI can help financial institutions make better sense of the information they’ve gathered and turn those insights into actionable steps.

But finding the right solution to organize a complex amount of data takes effort, and it may take time to find the exact toolset that works best for each bank. Some financial institutions may find it worth investing in a research and development arm to figure out what solutions work best for their needs.

Wherever organizations choose to direct their focus, it ultimately matters that you spend time building out your tools with the future in mind. Financial services firms that analyse many trends ahead of time will be able to act on them.

CSD uses machine learning predictive models and segmentation to learn patterns of different credit risk score, and can be used to predict risk levels of future credit loans. Also, from you data, CSD can build the right campaigns and design the most attractive offers to get new customers and retain them.  Effectiveness of predictive modeling depends on the quality of historical data. If historical data contains information that can predict customer tendencies and behaviors, predictive modeling can be very effective. With CSD, you get the insights you need to make data-driven decisions for your bank.

CSD uses an actuarial analysis  for analyzing and managing the financial risks of a business. Actuarial science is a deeply sought-after field that plays a high role in the success of a company. Actuarial analysts help accredited actuaries make strategic decisions and communicate solutions for deeply complex financial issues

 
Insurance

 

Rates differ for policyholders contracting identical insurance policies depending on several analyzable rating factors. Insurance providers have good reasons for this practice. As part of the analytical procedures, SCD uses predictive models and segmentation to calculate and manage risk when evaluating policy applications and setting premium rates.

 
Telecommunications

 

A call center is a service network in which agents provide telephone-based services. Customers who seek these services can be delayed in tele-queues. CSD looks into management problems of call centers and the opportunity to analyze a large quantity of data collected over a long time period. With CSD, you will get the insights you need to make data-driven decisions for your Business.

 
Retail and Market Research

 

CSD helps an e-commerce business, high street retailer, or a blend of the two, knowing with certainty what customers prefer and what convinces them to buy is the key to prosperity and possibly even survival. CSD helps to structure surveys to achieve a result that can be relied upon. CSD uses statistical techniques to analyse social media posts that can help business understand what people really think about your business, organisation or product.

 
Health

CSD helps the health care industry how to benefit from knowing consumer market characteristics. CSD uses electronic health records from primary and secondary care to measure burden of disease; estimate incidence/prevalence of disease and investigate the variations and impact of current management strategies.

We are professionals in using predictive analytics to answers the question: what is likely to happen next.

We use machine learning in health informatics which can streamline recordkeeping, including electronic health records (EHRs). Using machine learning to improve EHR management can improve patient care, reduce healthcare and administrative costs, and optimize operations. Machine learning algorithms can also make EHR management systems easier to use for physicians by providing clinical decision support, automating image analysis and integrating telehealth technologies. For example, it can help clinicians identify, diagnose and treat disease. Applications of machine learning in healthcare can also streamline healthcare tasks and optimize surgery planning, preparation and execution.