The two-way street between data science and business
Thu 28 Sep 2017 | Sigrid Rouam
Sigrid Rouam, lead data scientist at SGX, discusses the role of big data in business and finance, and how companies can change to make the most of the data scientists they employ
Big data and data analytics is becoming increasingly accepted as a vital ingredient in the pursuit of a smarter and more profitable business strategy. Sigrid believes that in the next year, we will see an increase in the number of applications for big data analytics.
She identifies some key areas, such as gaining deeper insights on customers to provide more personalized experiences, improving IT resiliency and operations efficiency, and enabling more real-time analytics beyond finance and trading.
In the dizzyingly fast-paced world of modern business and technology, staying still is effectively moving backwards. Therefore, to get ahead of their competition, businesses need to do more than simply adopt big data practices.
The question is not whether you have big data analytics, but rather how you are using it to get actionable insights
Sigrid argues that the use of data is necessary for businesses today, but this is not sufficient to get ahead of the competition. To do that, the question is not whether you have big data analytics, but rather how you are using it to get actionable insights.
For instance, above and beyond what might be seen as the ‘baseline’ of data analytics, through which businesses gain an understanding of what their customers are doing, she suggests pursuing a more advanced type of analytics. This can include combining data from different sources to build a 360-view of customer behaviour in order to serve them better, or understanding anomalies and outliers to get a nuanced view of operating environments.
Data science and business
Sigrid argues that there can be a problem with how businesses view the role of data scientists. She suggests that businesses need to set data scientists up for success. Many companies hire data scientists and expect magic to happen. Instead, expectations should be realistic and roles should be clear.
To maximize value from data scientists and enable them to perform at their peak, Sigrid believes there are several key considerations; support from senior management, allowing scientists to try new technologies and methods to solve problems, involvement of the data scientist in business problems, and having the right environment such as centralized data warehouses and proper data governance.
Though there are a number of lessons that business staff can learn from data scientists, the same applies in the other direction. Sigrid looks at what she has learned from her time at SGX, particularly in terms of the interaction between scientific and technical staff, and the business aspect of the organisation.
On setting out to achieve goals, she believes it is key to think big, start small, and move fast. If data scientists are working an environment where there is a willingness to push boundaries, she opines, there is huge potential to uncover new insights from big data.
Critical to creating this environment, she notes, is the mindset and culture of the workplace. Mindsets need to change to allow more people to see the benefits from big data. Starting with small use cases to convince the wider firm can help. If they don’t work, it’s best to pivot quickly, and if they do work, it’s best to scale quickly.
Even if we are in a highly regulated industry, it can be important to ask: Is this process really necessary? Can we do it in a more agile way?
According to Sigrid, an important but controversial lesson is the necessity of questioning the rules. Even if we are in a highly regulated industry, it can be important to ask: Is this process really necessary? Can we do it in a more agile way? By questioning the process, it can be improved.
Finally, looking at the role of data and analytics in FinTech, Sigrid has a bold prediction for the future. She argues that artificial intelligence (AI) may at some point replace traders. As it stands, she argues, the trading algorithms are fairly simple. With further development, we could reach the point where we have an algorithm capable of updating its behaviour every time a new data point comes in, producing significantly more advanced results.
Looking at business processes through the eyes of a data scientist provides unique insights, and illustrates the two-way street that is the relationship between business staff and scientific staff. If it is treated as such, the business and strategic gains will be much more significant.
Sigrid Rouam will be speaking at the forthcoming Big Data World, Singapore, 11th and 12th October 2017 at the Marina Bay Sands Expo and Convention Centre. To hear from her and other big data experts from around the world, register today for your FREE ticket.
Tags:Asia Big Data business feature
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