Is Data Science worth it or overrated?
Hcience capabilities. Once dubbed as “The Sexiest Job of the 21st Century” by Harvard Business Review and other leading publications, what went wrong?
A bit of background, I am a seasoned Data Science leader from a fully qualified actuary and quantitative background. In my career, I have delivered successful data science transformation for existing organisation capability or greenfield projects.
A common and honest thread within key organisational business leaders is data science disillusionment. Requirements could be categorised into 2 main categories:
Reporting / DataOps / Cloud transformation: this translates to “I think my analytics capabilities are running too much fat. Could we trim it down to as minimum as possible? At the end, they just provide some basic reporting only. Nothing strategic or useful”.
Predictive modelling: this translates to “I need to showcase that my organisation and team are being involved in cutting edge and digital things. Having a sexy predictive modelling/recommendation engine with a few data scientists is a great idea. They will just do research anyway — and I could offshore the data scientists too to keep cost to a minimum”.
So, is data science worth it?
Data Science allows organisations to interact with their customers
Photo by Daniel Fazio on Unsplash
I like going to one of the local Asian grocery shops. The shop owner is always there. He knows my name, says hi, and lets me know when my favourite items are on sale. When I go to a big shopping centre, the experience is totally different. They don’t know me, they are swamped with customers and I can’t interact with them at all.
The above situation works well for a small business. For large organisations, it is humanly impossible to have a proper interaction with its customers. “Interactions” are limited to transactional notifications such as “your interest rate is down”, “your payment is due”, “your address has been updated” and others.
Data Science provides the mean towards meaningful interactions with your customer. Everyone breaths digital. Organisations that leverage data science enjoy higher customer satisfaction, higher offer response rate, and lower marketing opt-out rate.
In Data Science, the concept is often referred to as mass personalisation. For example, in one of my previous projects we created a statistical clustering based on a mixture of customers demographic and financial profiles. This allowed greater transparency into their behaviour such as young couples with families or young adult travellers or young & risky. Based on this additional information, the marketing team was able to understand their customer base, whether it was within their target market segment, along with the intelligence to develop new campaigns and product offerings. It allows them a digital pathway to interact with customers and appreciate their uniqueness.
Data Science provides a trusted, unbiased and forward-looking business advice
Photo by Stephan Henning on Unsplash
Due to the hype and demand, there is a strong rise of fake data scientists and fake data science leaders. I wrote an article below which delved a bit further into what a true Data Scientist looks like. In summary, a true Data Scientist needs a good grasp of mathematics, business, and technology as it is a commercial profession.
Is Data Science hard?
An honest real life perspective
towardsdatascience.com
There is a strong misconception in the industry to divorce business and mathematics. I recently interacted with an organisational leader who says “I know a lot about the business so we are only looking for strong quantitative PhDs”. On another project, another leader said “we’ll just pair data scientists with Business Analysts because data scientists don’t talk well and don’t understand business”.
I am an Australian, and we have a popular cooking TV show called MasterChef. It is a culinary competition for home cook chefs to develop highly innovative foods. These contestants love good foods. They also have superb cooking skills and outstanding ideas.
In the show, contestants are presented with a challenge (or requirement). For example, it could be a pasta challenge. If we translate this to a data science context, the data scientists never eats real food. They would grab a pasta, cook it in hot water and serve it to the customer. It is edible, and customer (a.k.a. business) knows best. This degrades the data science profession into a master and slave relationship.
Real data scientists are business-savvy professionals with the special power of mathematics and technology. When I was transitioning between jobs, there were many occasions when I was criticised for being “too business” and that “data scientists are supposed to talk maths only”. Data Scientists are able to communicate mathematical concepts for business uses. Having said that, I see value in many PhDs that would like to transition to a corporate life. There is another job category such as Algorithm Engineer or Quantitative Researcher that would be more suitable compared to Data Scientists.
From my real-life observation, organisations that are able to truly realise the value of data science treat their data scientists as business partners. Having said that, there is a strong uphill battle due to an existing stigma or perception that divorces business and data science. I foresee that the upcoming data science revolution would be driven from a consulting background or mindset.
Data Science leads to outstanding customer experience
Photo by Belinda Fewings on Unsplash
Happy customers lead to happy and profitable organisations. Recently, there is a strong organisational push on where executives are now measured on indicators such as Net Promoter Score (NPS), Dispute Resolution, Cycle Time, SLA (Service Level Agreement) Compliance, Opt-Out Rate, and many others.
Once we establish that Data Science allows organisations to interact with their customers and provides a trusted, unbiased and forward-looking business advice, we are able to understand that data science is the key component in achieving the above customer experience metrics.
Recently, I was helping an organisation to improve its customer experience for enterprise customers. These large corporate customers had an extremely variable and length order fulfilment time from 4 weeks to more than 24 weeks! The Data Science team was able to stitch together the process flows from disparate data source systems. This included the discovery of data entry inconsistencies, wrong data structure, revenue leakages, and many more. We were successful in this project because the data scientists are strong business-savvy professionals. Apparently, there were many unsuccessful attempts on this project over the past two years. Overall we were able to significantly reduce the cycle time to an average of 4 weeks and drove higher data quality through a new governance framework.
The list goes on. Data Science is the key to an outstanding customer experience in the digital world. Unfortunately, many business and marketing leaders I work with do not see the potential yet. Usually, it is limited to reporting or digital campaign execution. Most of the decision making is still relatively gut-feel.
Where to from here?
In summary, organisations need to truly invest in identifying true data scientists and data science leaders. The exercise might be excruciating. I recently met a few organisational leaders who are undergoing such a change. They had the courage to hire and identify leaders who understand business, mathematics, and technology. This is a transformational shift from the traditional PhD dominated or “General Manager” type business leaders.
Data Science is worth it.
Check out my other articles if you want to learn more about practical and impactful data analytics topics. If you have further questions or topic suggestions, feel free to connect and message further through LinkedIn.
About the author: Albert Suryadi is a proven leader in enabling advanced analytics and data science capability in blue chip organisations. He is recognised as the leader of the Analytics CoP (Community of Practice) that empowers and motivate others beyond the status quo.
