Data Analytics

Data Analytics is hot. With Data Analytics, organisations are able to outperform their competitors significantly. But in recent years this has not become any easier. More and more data is being produced, on an increasingly diverse set of topics. And we see analytics being deployed more and more frequently, which means that a certain level of analytics is now required just to keep up. To rise above the competition, an organisation needs an effective Data Analytics strategy that supports their business strategy. It then needs to identify the crucial, strategic applications and develop the right solution for it.

Triple A Data Analytics experts with substantive domain knowledge help you and your organisation extract maximum value from your data. 

The goal of Data Analytics is to deliver value to your organisation by making better decisions after a thorough analysis of available data. What is of value can vary enormously from one organisation to another and depends strongly on the chosen strategy. It often involves higher revenues (sales) or lower costs, but increased customer satisfaction or product experience are also common objectives. Successful analytics projects start with identifying opportunities and estimating the expected business value, and end with measuring and validating the results. The best analytics projects also focus on strategic objectives.

Visual analytics provides insights to people, who make strategic, tactical or operational decisions. Think of a dashboard showing the development of sales per product or customer group, or a report on the average time taken per step in a production process.

Algorithmic Analytics is the collective name for all customised algorithms to analyse data. This usually involves domain specific applications for which standard predictive algorithms cannot be used, such as gradient boosted trees models or neural networks.

Predictive analytics is about making predictions at the individual level, which can lead to automated decisions. A few examples include fraud detection in bank transactions and insurance claims, or individualised product recommendations and pricing.

Data is the foundation for all analytics. With the increasing production of, and demand for, data, it is a complex challenge to build a reliable foundation for analytics. Common solutions include a data warehouse for dashboards and reports, and a data lake or feature store for machine learning applications.

Domain knowledge adds a lot of value

The techniques for processing and analysing data are becoming more and more accessible. You can hardly find a data scientist who has written a gradient boosted trees algorithm himself; anyone can get straight to work with the most advanced algorithms by using open source software. The best Data Analytics experts distinguish themselves by the way they use those algorithms and techniques. To this end, domain knowledge is essential for making the right choices, both in terms of data and analytics.

Successful with Data Analytics

Triple A’s Data Analytics experts have both the financial and actuarial domain knowledge and the practical experience to apply Data Analytics successfully in a wide variety of business processes, both inside and outside the financial sector. We would love to help you with your Data Analytics challenges!

Interested in our services, or would you like to have an informal chat sometime about Data Analytics? Get in touch!