Sudaman Thoppan Mohanchandralal talks to Yiannis Parizas and Phanis Ioannou about how organisations can adopt a data culture, and why it is so important
The overwhelming majority of insurance industry practitioners would agree that investing in data initiatives is important, and that implementation of a data culture leads to more appropriate decision-making. Pricing and pricing optimisation, fraud detection and factory reserve estimates are just some areas where decision-making can be better informed within the industry.
As a thought leader in data culture, Sudaman Thoppan Mohanchandralal, regional chief data and analytics officer at Allianz Benelux, and Global Data & Analytics Tribe business sponsor, is well aware of the challenges of successful implementation – and the potential benefits on offer.
Adding value
Sudaman, who has a masters in computer science and a global MBA in managing global enterprises, describes organisational culture as “a set of habits that a particular organisation performs as routines”. A subset of this relates to the steps that businesses take when making decisions; Sudaman says that, to minimise human biases, these decisions should be data driven. “Data culture is the culture of making decisions based on data,” he explains. “When data is available, is the organisation using the data to make decisions? When data is not available, what are the techniques used to proxy data in order to have a data-driven decision? All these routines and processes are what defines a company’s data culture, which is embedded into its organisational culture.”
He believes that adopting an effective data culture adds value to every organisation, especially in the insurance industry. “Insurance does not exist without data. The price of a product is only known in the future, and in order to assign the true value to a product, and not under-price someone who does not fit in the portfolio or over-price someone who is able to be loyal along the period of the contract, historical data needs to be utilised.”
Adopting a data culture allows insurers to identify patterns and non-linearities, enabling them to become “extremely customer-centric”, Sudaman continues. “Building trust in data allows companies to make educated decisions, leading, in many cases, to securing the future of customers when data culture is pronounced. Additionally, adopting an effective data culture makes it possible to identify key features and key performance indicators that were not even considered in the past, in terms of specialised insurance products. Claims effectiveness and fraud detection also improve dramatically when data culture is enhanced. All of these areas are adding value to the customer and, as a result, to the insurance company.”
An effective strategy
Starting a new data culture within a company depends on its business strategy, Sudaman says. “For example, a market leader in the industry might want to focus on profitability, and hence define its data strategy on its profitability targets. This company might focus on its portfolio optimisation and steering, claims effectiveness and fraud detection.”
“Adopting an effective data culture makes it possible to identify key features and key performance indicators that were not even considered in the past”
A mid-level company might want to gain market share and define its data strategy in building more advanced and accurate pricing, while for a company with very little or no market share, “growth is everything”. “They might want to focus on sales and distribution and use data-driven processes in identifying which segments and products to invest and what expertise they need to bring in,” he says. “Data strategy solely depends on business strategy, and irrespective of the industry, the strategy should be very aligned with the activities that need to be performed.”
Adaptation and responsibility
When it comes to implementing a successful organisational and data culture, Sudaman says the drive must come from the top of the organisation. “Data culture is the responsibility of the CEO and no one else. The first step is sensitisation. Its importance needs to be communicated well and pronounced by the CEO by allocating budget to data initiatives that cannot be touched by anyone else. As a result, the whole company gets the message that something new is happening.”
The second crucial step is to get people throughout the organisation involved by building their data literacy. “It should be a real engaged, committed workload that is mandated to the workforce,” he explains. “This is particularly important to unravel the mystery of data for people in the organisation.” In parallel, business initiatives should be pushed to use data analytics. “This can be done by engaging people responsible for business and data initiatives to work together to kick-start an effective implementation of the data culture within the company.”
Obstacles to adoption
Building a successful data culture is not easy, with many obstacles potentially appearing on the way – data illiteracy among them. “People might be asking the wrong questions,” Sudaman cautions. “For example, if someone asks ‘what do you think?’, it means that they request an opinion. Asking ‘what do you know?’ means they want the facts so that they can form their own opinion. A board member who is data-literate will ask for the data-driven knowledge that will allow them to form their own view and decision.”
A concern among workers is the fear that their roles may become redundant in the move from experienced judgment to data-driven decisions. During the past few years, the cost of prediction has fallen due to advances in technology, while the cost of human judgment has materially increased. The latter stems from the increases in decision choices, such as possible investment options. Far from threatening the need for judgment, however, data-driven decisions free up an organisation’s capacity to make the judgments that are increasingly needed.
“‘Living in the here-and-now’ is another obstacle that keeps people from adapting to data-driven cultures,” Sudaman continues. “This is when people are caught in today’s operational issues, which makes them unable to consider that a particular problem can be solved differently. Examples include Kodak or Blockbuster, which failed to adapt to something new. People who do not want to elevate their skills and change are the ones who lose jobs.”
“‘Living in the here-and-now’ is an obstacle that keeps people from adapting to data-driven cultures. Examples include Kodak or Blockbuster, which failed to adapt”
Avoiding generalisation
Sudaman believes that data culture must be specific to the group of people in an organisation and cannot be generalised.
A standardised framework – the Data Driven Decision-Making framework – was built for his own organisation, and focuses on capturing the organisation’s routines and connecting them to rewards and triggers. “You need to understand the routines of the organisation and why people are not using data,” he explains. “This can be done through interviews and subsequent analysis. Data-driven decisions may not be used because of data unavailability, bad quality data or data illiteracy. Once the causes are identified, the organisation can invest and improve the situation.”
Organisations can provide training to workers to improve data literacy. Sudaman explains how the Accelerated Data Academy at his own organisation, built to improve data literacy, works: delivering training through e-learning and certifying trainees at Bronze, Silver and Gold levels. “Achieving a Bronze Certificate makes you a ‘data citizen’ who knows the basics about data within the company,” he says. “Almost everyone has achieved that level. The Silver certificate teaches not only data, but also techniques and environments that are bespoke to each person’s role and seniority. At the Gold level, you become an ambassador of data, and can provide a point of view on data topics and build data strategy.”
Final Advice
Executive support, such as that from the board of directors or the CEO, is vital for a data culture transformation to take place. “You need to have the support of shareholders,” Sudaman says. “In an organisation of 10,000 people, 9,500 people will be working on the here-and-now, while the other 500 will be working for ‘tomorrow’. Being one of them, as a leader, you need protection from the 9,500 people to succeed.”
There are many market standard solutions for data that aim to accelerate data processes. However, Sudaman argues that, on their own, these technical solutions “will take you nowhere”. As a leader in data culture, “you need to work very closely with business, with actuaries, with pricing people, with risk managers, with claims handlers. Unless and until you do that, you are just not able to help in adoption.”
He reiterates that, while change must come from the top, collaboration is crucial. “If you work on data culture adaptation and do not involve stakeholders, you are just boiling the ocean. You may be lucky initially, but you cannot achieve much result without collaboration in the long run.”
Sudaman also believes that companies must not obsess over data quality, arguing that this can delay cultural transformation. “Data quality is not everything; it is super important, but you don’t have to solve 100% of the data quality issue before you become data driven. You should start the initiative and progressively remediate the gap in data quality over future stages – otherwise you will never start.”
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