Kush Kahadugoda, Head of Data Analytics, IOOF (ASX: IFL)
In the very near future, businesses will compete based on their data analytics prowess. This will make data the most valuable raw material for businesses and make data analytics one of the most vital competencies. A business’s long-term success, or alternatively its failure, will be directly linked to its ability to access, unify, and process diverse data assets to generate meaningful and deep insights in the most efficient manner.
As Suhail Doshi, the CEO of web analytics service Mixpanel says, ‘businesses that make decisions by either guessing or gut feel will be either lucky or wrong.’ This is a profound statement that needs to be understood at a deep level. The real question is: do you want to be lucky or wrong? I would rather be wrong than lucky for the simple reason that you can learn from being wrong, whereas being lucky often leads to a false sense of ability and expertise. The best position to aspire is to be both lucky and right. Data analytics is a powerful pathway to achieving both. A well-developed data strategy bundled with a well-supported pipeline of investments in people, process, and technology will permit businesses to rapidly build their data analytics capabilities. This benefit will not flow to those businesses that don’t have the right balance between strategy and execution.
Data culture is paramount
So, what is the most prudent challenge to solve in developing long-term enterprise-grade data analytics? It's not the strategy. It's not the data, tools, or techniques. Instead, it's the data and analytics culture. Strategy, tools, and techniques are extremely important aspects of a well-functioning data and analytics operation. But, without a supportive data culture, the most likely result will either be a data strategy that collects dust or a collection of shiny data analytics tools and expensive talent that doesn’t deliver any enterprise-level value.
Leading data and analytics industry surveys and industry thought leaders are now highlighting the importance of developing and maintaining appropriate data cultures within the context of their businesses. When I reflect on the data and analytics initiatives which I have been part of, the determining factor in achieving true success has always come down to the data culture.
A data culture must permeate the entire business
Another factor important to understand is the difference between delivering a successful data initiative for every project approach versus delivering actual long-term value to the business. As an example, a business can implement a data warehouse on time, within budget, and as per its documented requirements, but this doesn’t mean the data warehouse will be embraced by the business.
There is absolutely no guarantee the business will become data-savvy and analytics-driven. While certain pockets of the business will benefit from the project, the real transformation will only occur when the right data culture is developed and nurtured. Accordingly, as a data and analytics leader, I always keep the famous Peter Drucker quote on culture at the top of my mind: ‘Culture eats strategy for breakfast’.
when a business starts to make progress on its data culture, we will see the cohesion between data strategy and business strategy strengthening
What is a data culture?
So, what exactly is a data culture? At the core of any data culture is the decision culture of the business. A highly-embedded data culture demands that decisions are made based on insights derived from data, whenever applicable and practical, rather than 'gut feel' decisions. In addition, a data culture will enable decisions to be overridden when new facts present themselves via data. Achieving this level of decision culture requires the highest level of executive buy-in and understanding.
The costs of a poor data culture are high
Imagine the following scenario: a product development team at Company X has developed a promising new product, which is going to be launched in the coming month. In the meantime, the team that's responsible for forecasting demand for this new product discover an inherent problem in the model used in the forecasting process due to data management gaps. Now, the previously forecasted lucrative new product yields a loss-making outcome. What would typically happen at this juncture? If Company X had no data culture and practice, new product demand forecasting wouldn’t have even been a part of the product development process. If Company X had a mature data culture, the launch of the product would most likely be suspended or the product offering would be altered until there's forecasting certainty that a lucrative outcome can be achieved. Company X will also make additional investments, as required, to further enhance and strengthen data management and analytic functions to avoid future incidents. But, if Company X only had a data and analytics practice without an underpinning supportive data culture, one of the following is likely to occur:
• Forecasting error would be hidden owing to fears of backlash or blame.
• The product will be launched despite what data insights indicate.
• A combination of backlash/blame would be put on the analytics team and the product launch would go ahead as per the original plan.
Data culture should influence business strategy
As leaders in this field, it’s important to understand and recognize that not every decision will be, or can be, made based purely on data insights. But, in a business with a mature data culture, decisions will primarily be driven by data.
When an organization’s data culture and philosophy is detached from its business strategy, enterprise-level analytics is likely to fail. Conversely, when a business starts to make progress on its data culture, we will see the cohesion between data strategy and business strategy strengthening. As the culture matures further, data strategy will be influencing business strategy and its direction.
Achieving a data culture is an ongoing process
It’s important to remember that data culture is not a destination-based journey. Experienced leaders in this space approach and cultivate it as a never-ending endeavor with inevitable ebbs and flows. At times, transitioning to a data culture may constitute a step-change in the business, which requires a fundamental shift in the way it conducts itself. The people who are tasked with leading the company’s data and analytics work should initiate a two-prong approach that focuses on continual top-down engagement combined with a bottom-up focus on curiosity and interest building to develop and progress the data culture. To make the transformation a success, we, as leaders, must be creative, courageous, and think outside the conventional norms and approaches.