Value Driven Advanced Analytics

Advance Analytics is the utilization of AI and machine learning to develop self-contained automated solutions, in conjunction with your industry subject matter experts.

Find out more from one of our matter’s experts – Catharina Svenningstorp

About Catharina:

Business and value oriented Advanced Analytics consultant with focus on data driven analytics applications and solutions for actionable business insights. Have over 20 years experience from a wide range of industries building predictive models and implementing solutions for customer analytics, data mining and applied data science.

Could you describe briefly what advanced analytics is?

Advance Analytics is the utilization of AI and machine learning to develop self-contained automated solutions, in conjunction with your industry subject matter experts.

How does a company know when they should explore the use of Advanced Analytics?

The process of developing as a data driven company begins with the exploration of the organization’s ability to use data as an asset in decision making and user understanding. Business decision support with help from reporting can be used to monitor, control and follow-up the business, but to find more complex patterns and get a high business value, advanced analytics is required. Advanced analytics is needed to discover customer behavior patterns, identify potential customer value, and optimize outcome of activities.

Could you give us an example where advanced analytics might be required for an organization?

In some business cases scaling up business with manpower is impossible. It is not cost efficient to add people for routine activities where you need to give 24/7 support. For example, chatbots giving live support to customers on routine questions in multiple different languages and regions. These chatbots can also automatically scale up during high demand times so that there is never a wait time for customers to receive a timely response.

What general benefits do you see in companies that adopt advanced analytics?

An organization with a large customer base will benefit from applying advanced analytics in eg. categorization of customer complaints. By categorizing and exploring the data a company can find patterns in claims and automate processes for handling them and reducing the time spent on each claim.

Use embedded analytics where you can integrate/interact in personalized ways with your target group. Using data to understand your target group better will give your audience a customized experience.

Encourage inhouse innovation by enabling access to an explorative environment, sandbox, where you can test out new data. Using open source will benefit the adoption of best practices.

How would you describe the typical journey a company goes on to become more Advanced Analytics driven?

To move towards becoming more data driven and incorporate a structured way of working to your business, you need a stepwise approach and not only be reactive but also pro-active. Typically, in an organization’s advanced analytics infancy, we see rule-based or ad Hoc decision making which are inflexible and slow to adapt and keep up with increasing business demands. Our focus is on a stepwise development of this to the end goal of machine learning supported automation, where models are trained to fulfill an intent (a goal) based on guidelines from experts and historical data.

There is also another transition that occurs in the move towards more sophisticated AI systems relates to decision-making. In rules-based automation, computers don’t have any decision-making power, they can only take pre-defined actions in specific circumstances. Making the transition from telling computers how to do something to what you want them to do means giving computers decision-making power.