Transforming into an analytics-powered company is the ambitious goal of many of today’s organisations. The key to success relies not only on the adoption of advanced technologies but mostly on people who are able to connect artificial intelligence to the business processes, taking advantage of algorithms, insights and automation.
Among those visionary enterprises, ERGO Group AG is one of the primary insurance companies in Germany, as well as in other international markets, with more than 40,000 employees and sales partners worldwide and a total premium income of 18,7 billion euros. They offer a broad range of insurance and financial products, counting more than 9 million clients in their domestic market in Germany.
By using SAS® Viya® in health claims operations, ERGO put in place another important milestone in its analytics-driven strategy, testing the operational capability of machine learning and artificial intelligence methodology to prevent unjustified claims.
Pursuing an analytics-driven approach
Tim Stettner is on the way to digital transformation with ERGO as Data Analytics Consultant. Together with the business departments, IT and AI specialists, he finds and designs value-adding analytics and AI cases, driving them from the idea to productive use.
"Being an analytics-driven company is an essential component of the ERGO strategy program. The objective is to reach a systemic use of advanced analytics to extract value from our data and provide optimisation for any process along the entire value chain: product design and pricing, sales and distribution, underwriting, risk management, customer engagement, claims, services and operations," Stettner explains.
Building advanced analytics skills
To achieve this aim, an advanced analytics competency centre was established three years ago. "There are 27 highly qualified professionals – data scientists, data engineers, analytics consultants and enablement managers – in charge of communicating with the business units and stakeholders," Stettner says.
The team brings together strong skills in data analytics and visualisation, as well as extensive knowledge of IT solution development and a deep understanding of all insurance processes, in order to identify and build relevant use cases.
ERGO is now working on the medium-term objective – taking competitive advantages from advanced analytics. "We are still at the age of improving processes by using data analytics. Artificial intelligence helps us to make activities better, faster and more efficient, but we are not on the way to invent new things and methods yet," Stettner says.
Transforming business models by an intensive and extensive usage of data is a key step for any aspiring analytics-driven company but requires a profound leap of thought at each level. "We need to change the mindset in the whole company. Reaching an effective analytics-driven approach is a journey, and ERGO is strongly committed to pursuing this vision by investing in highly skilled experts and technologies," Stettner says.
From a technological perspective, the ERGO advanced analytics strategy includes the deployment of a state-of-the-art IT ecosystem with an analytical data lake for modelling process and operational capabilities for AI use cases.
According to Stettner, meeting today’s analytics demands requires the right analytical environment. "The traditional data warehouse comes from the business intelligence era and is not suitable to satisfy the new business needs. Instead, a data lake can support the concrete use cases we are working on, combining historical data and information from external sources."
The idea is to exploit data lake features for model development and training while using data sets with specific purposes to address use cases and give a faster response.
The critical point that distinguishes a truly analytics-powered company is the ability to operationalise the data science projects, taking models out of the labs and migrating them to the real world.
People are the key, and ERGO believes in a cross-functional approach that delivers value in close and agile collaboration among advanced analytics professionals, IT specialists and businesspeople (data and process experts).
According to Stettner, so-called “citizen data scientists” play a crucial role in operationalising models. "I don’t like this term because I think it is reductive. I like to call them domain data scientists. Those people have a strong mastery in their domain and usually perform analytical tasks, working in business units such as actuary, underwriting and risk management departments, where data analytics represents a critical asset and a daily occurrence."
Of course, these business-side professionals need to be supported by the transverse data science team in using sophisticated technologies and methodologies. But they enable the real benefits of advanced analytics in specific processes, adding their own expertise and unique skills.They can drive the company to a higher level of analytics maturity in their field, Stettner summarises. Click To Tweet
Communicating the power of analytics
Collaboration is a key element to succeed in data science projects, as well as the communication of analytics advantages among business leaders, stakeholders and end users. People struggle to understand the huge opportunities provided by advanced analytics, artificial intelligence and machine learning without a clear demonstration.
Tamara Fischer, a Principal Solutions Architect at SAS who is involved in the ERGO project, shares her vision about the importance of communication and engagement. "It’s essential to convince anyone within the company about the new technology benefits. No one wants to invest in something he doesn’t know or understand. Unfortunately in most cases, there is not a clear strategy to show the users the opportunities of analytics," she said. Fischer also underlines the great value of a lighthouse project focused on demonstrating the potential of advanced analytics in specific use cases.
"This is the approach used by SAS with its customers. Seen by anyone in the company, a lighthouse project shows how the technology can be implemented and transform the processes, helping employees in their daily tasks. You can install the most sophisticated software, but you won’t reach any result if users are not informed of its functionalities and possibilities. It’s needed to make people aware about the power of analytics through presentations and prototypes," Fischer says.
Spreading the analytics culture
The engagement of the whole organisation is crucial to finalise the analytics-driven strategy. And a change management plan is needed in order to build up a sustainable organization, based on an upgrade of skills and a solid framework to operationalise the models.
"We must help the business intelligence staff to come out from their cage and get more experienced in the new analytical approach. The people at the competence centre wouldn’t ever be enough to maintain the whole system by themselves. We also need to be proactive encouraging the usage of models and technology. Users typically don’t ask for new solutions because they don’t know the technical possibilities, but they are happy if you show them some tools that simplify or improve their work," Stettner says.
Quoting Henry Ford, Stettner concludes: "At the beginning of the 20th century, if you had asked people what they wanted, they would have said faster horses instead of a new car. Analytics requires the same cultural shift in order to radically innovate, and not only automatise, running processes."