Increasingly, financial organizations are describing themselves as data businesses rather than finance firms, a result of the world’s increasing focus on data and an acknowledgement of the potential benefits that can be derived from deeper data insights and analysis. Gartner predicts that in the near future, 50 percent of an organization’s data will be generated from outside their data centre,1 making a data strategy critical for maintaining a competitive edge.
Tomas Turek
Global Lead Data Scientist,
Product Management & Client Experience,
RBC I&TS
Approaching data transformation by simply implementing technology tools is likely to be ineffective unless organizations have a clear strategy that articulates what they want to achieve. A survey from McKinsey & Company found that while eight in 10 respondents said their organizations were undertaking data transformation projects, their success in improving company performance and sustaining those gains was “proving to be elusive”.2 An understanding of the key challenges around data transformation and how these challenges can be overcome can mean the difference between success and failure.
Key insights
- Data ethics policies should be integrated into data transformation strategies, especially in light of recent highly publicized breaches
- Executive sponsorship for data strategies is essential and a compelling vision is required to assure buy-in
- Creating the right mindset for data champions helps with their understanding of responsibility and accountability
- A clear data strategy with realistic targets is essential for managing expectations
Executive buy-in needs a compelling vision of possibilities
A recent seminar at the 4th Investment Data & Technology Summit in Sydney explored the digital journeys of asset managers and the most common issues they are experiencing.3 Panelist Tomas Turek, global lead data scientist, product management & client experience with RBC Investor & Treasury Services believes that one of the main elements for successful data governance initiatives is executive buy-in, and sponsorship. In some cases, those sitting at the most senior levels at asset management firms may be unaware of the steps required to initiate and execute a transformation journey.
It is important that these senior managers support and promote educational and data literacy programs to shift both their mindset and those of their employees in order to help inform their transformation objectives.
Panelists agreed that if senior management do not understand the value that can be extracted from data transformation, then associated funding, sponsorship backing, and project approvals will be challenging. Creating a compelling vision of what is possible by starting relatively small and building on that is one way to highlight the value of a data initiative.
Right mindset creates accountability culture
Getting people to appreciate their responsibilities around data is another major challenge. What is required is acceptance that whoever owns the data should also help make decisions around its uses. While there is general agreement about the competitive advantage data brings to organizations, if its value is not honoured, firms can be exposed to risk.
One of the main
elements for
successful data
governance initiatives
is executive buy-in,
and sponsorship
Panelists reiterated that data owners need to take on the accountability that comes with data ownership. Being accountable often involves setting the right data culture, but this is not something that can be imposed; it requires buy-in from all employees. While a staff member can be appointed as a data owner, they also need to understand the responsibilities that come with that role. The right mindset and focus is very important.
Match goals to governance strategies
Despite the many challenges, data transformations are a strategic imperative for asset managers and, if successful, will yield enormous benefits that contribute to their long- term success. Creating the right culture, ensuring executive sponsorship, and setting a clear data strategy that outlines the expected value will greatly assist in overcoming common obstacles that could hamper a successful transformation.
Ethics are crucial to data culture
According to Turek, a further issue that needs to be addressed as part of the data journey is data ethics. “Many people don’t think about ethics but it’s so important,” he says. “In any data project you need to make sure that you are not in breach of any privacy laws or introducing any bias. It’s more than just what people are doing with data, it is also about who actually owns it and how is it being shared.”
Data owners need
to take on the
accountability that
comes with data
ownership
“The controversy involving Facebook and the now defunct Cambridge Analytica is just one example of the need to have proper data ethics policies in place. Here, Facebook’s policies allowed Cambridge Analytica to access data that Facebook did not realize could have other uses. This highlights the need to understand how a company’s actions can not only affect the data it holds but also the trust of its customers.”
The International Organization for Standardization (ISO) recently published the first international standard for privacy information management, ISO 27701.4 “This standard is designed to protect personal privacy, which is a welcome advancement,” Turek says.
Choose realistic expectations over the unachievable
Another key challenge is managing expectations around targets, including getting business and technology stakeholders to agree on what comprises clear business value. Setting achievable targets that can be agreed on is the best approach, Turek says. “It is important to set clear expectations, particularly with your executive sponsors, and stay focused on those objectives. Achieving realistic targets is highly valued, and a clear measure of success.”
Another key challenge is managing expectations around targets, including getting business and technology stakeholders to agree on what comprises clear business value
There is an additional level of complexity for the investment management industry because, unlike others, it does not have clear parameters for many of its products. Different financial services groups may classify their data in different ways, so when talking about expectations, you need clarity on who owns what elements of the data, and what the data lineage is in order to mitigate risk.
In this age of big data, asset managers now have greater access to insights in real time to help inform decision-making. In order to further maximize the effectiveness of data- driven decisions, consideration also needs to be given to managing expectations and mitigating risk around data analysis. As Turek notes, “Managing the lineage of data provides clarity on the data transformation ensuring accurate and auditable datasets which underpin actionable insights.”