Introduction
As organizations turn to digital transformation strategies, they are also increasingly forming teams around the practice of Data Science. Currently, the main challenge for many CIOs, CDOs, and other Chief Data Scientists consist of positioning the Data Science function precisely where an organization needs it to improve its present and future activities. This implies embedding Data Science teams to fully engage with the business and adapting the operational backbone of the company
Furthermore, with all the requirements and expectations businesses are having from data science, innovation and experimentation will be key factors moving data science forward. Moreover, let us have a look at the growth of data science in recent years. After that, we will understand how creativity and innovation have accelerated this growth till now and the future prospects.
The Growth of Data Science
LinkedIn recently published a report naming the fastest growing jobs in the US based on the site’s data. The social networking site compared data from 2012 and from 2017 to complete the report. Eventually, the top two spots were machine learning jobs, which grew by 9.8X in the past five years, and data scientist, which grew 6.5X since 2012. So why are data science positions, and specifically machine learning positions, growing so fast?
1. The amount of data has skyrocketed
Not only has roughly 90 per cent of the data created in the last two years, but the current data output is 2.5 quintillion bytes of data daily
2. Data-driven decisions are more profitable
In the end, for many companies, data is not useful unless it is beneficial, which it certainly is. Data not only helps companies make better decisions but those decisions also usually come with a financial gain. Furthermore, a study by Harvard Business Review found that “companies in the top third of their industry in the use of data-driven decision making were more productive and profitable than their competitors.
3. Machine learning is changing how you do business
Machine learning is a type of artificial intelligence (AI) where the systems can actually learn and evolve. Also, it has infiltrated many industries, from marketing to finance to health care. The advanced algorithms save time and resources, making quick, correct decisions based on past learnings
4. Machine learning provides better forecasting
Machine learning algorithms often find hidden insights that went unseen by the human eye. With the vast amount of data in the processing stage, even an entire team of data scientists might miss a particular trend or pattern. The ability to predict what will happen in the market is what keeps businesses competitive.
Why Creativity and Curiosity are Needed for Growth of Data Science?
Data Science is More About Asking Why?
Data science is focussed on querying every result and having an inquisitive mindset. You can not be a good data scientist if you lack the inquisitive skills. Furthermore, an Inquisitive nature in a data scientist plays a major role in bringing out hidden patterns and insights present in the data. Data can be complex and answer to your hypothesis may lie somewhere hidden in the data. But, It is the inquisitive skills of a data scientist which leverages the hidden potential of data in achieving business goals.
Varied Implementations in Different Domains
Industry influencers, academicians, and other prominent stakeholders certainly agree that data science has become a big game changer in most, if not all, types of modern industries over the last few years. As big data continues to permeate our day-to-day lives, there has been a significant shift of focus from the hype surrounding it to finding real value in its use. Also, data science finds it’s usage in the most unlikely places one can ever think of now. Such varied implementations and decision making require creativity and curiosity in the minds of data scientists.
Different Problems — One Solution
This talks about the idea of dealing with multiple problems at hand with one solution. There can be solutions to different problems, but re-using an old solution from different problem space and applying it in the unlikely domains(extreme experimentation) sure has resulted in some great ideas recently. For example, CNN in deep learning is a classic implementation for image processing. But who could have thought that an image processing algorithm can also give strikingly good results in processing natural language? But, today CNN is also widely used for doing natural language processing. Creativity and curiosity take time to innovate things but when it does, it all worth the time invested!
One Problem — Multiple Solutions
We emphasise more on having multiple solutions for a single problem here. Having multiple ways of solving a given problem requires creativeness in mind. One should be ready to experiment and challenge the existing methods of solving a given problem. Furthermore, innovation can only occur when existing methods are challenged rather than just plainly accepting them. If everyone was to accept earlier beliefs, then maybe we could have been stuck with linear regression forever and will not have algorithms like SVM and Random Forest. Hence, It is this inquisitive nature which actaully gave birth to these classic ML algorithms today we have with us.
Examples of Innovations in Data Science in Recent Years
1. Coca-Cola managed to strengthen its data strategy by building a digital-led loyalty program. Coca-Cola director of data strategy was interviewed by ADMA managing editor. The interview made it clear that big data analytics is strongly behind customer retention at Coca-Cola.
2. Netflix is a good example of a big brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects huge data, which is the key to achieving the industry status Netflix boosts. If you are a subscriber, you are familiar with how they send you suggestions for the next movie you should watch. Basically, this is done using your past search and watch data. This data is used to give them insights on what interests the subscriber most.
3. Amazon leverages big data analytics to move into a large market. The data-driven logistics gives Amazon the required expertise to enable creation and achievement of greater value. Focusing on big data analytics, Amazon whole foods are able to understand how customers buy groceries and how suppliers interact with the grocer. This data gives insights whenever there is a need to implement further changes.
Creative Solutions for Innovation using Data Science
1. Profit model
Crunching numbers can identify untapped potential hidden in the profit margins or pin-point insufficiently used revenue streams. Simulations can also show if specific markets are ready. Data can help you apply the 80/20 principle and focus on your top clients.
2. Network
Data recorded and analyzed by one company can benefit others in numerous ways, especially if the two entities are in complementary businesses. Just imagine how a hotel could boost their bookings by using the weather and delayed flights information collected by a nearby airport during their regular operations.
3. Structure
Algorithms to ingest organizational charts with augmented information from thousands of companies and produce models of the best performing. It could offer recipes for the gender and educational composition of a Board to maximize talent. This could end artificial efforts of having more women on the board and produce even recommendations of possible candidates by scanning professional profiles.
4. Process
Data science consulting company InData Labs states that using analytics in the company’s operations is the best way to handle uncertainty by teaching staff to guide their decisions on results and numbers instead of gut feeling and customs.
5. Product performance
One company which already does this through their newsfeed automation is Facebook. They have innovated the way it looks for each individual user to boost their revenue from PPC ads. By employing data science in every aspect of user experience,
you can create better products and cut development costs by abandoning bad ideas early on.
How to Encourage Curiosity and Creativity among Data Scientists
1. Give importance to data science in growth planning
Don’t bury it under another department like marketing, product, finance, etc. Set up an innovation and development wing for research and experimentation purposes which is separate from business deadlines. The data science team will need to collaborate with other departments to provide solutions. But it will do so as for equal partners, not as a support staff that merely executes on the requirements from other teams. Instead of positioning data science as a supportive team in service to other departments, make it responsible for business goals
2. Provide the required infrastructure
Give full access to data as well as the compute resources to process their explorations. Requiring them to ask permission or request resources will impose a cost and less exploration will occur.
3. Focus on learning over knowing
Entire company must have common values for things like learning by doing, being comfortable with ambiguity, balancing long-and short-term returns. These values should spread across the entire organisation as they cannot survive in isolation.
4. Laying importance of extreme experimentation
More emphasis should be put on experimentation tasks and mindset. Having an experimentation mindset gives the ability to data scientists to take steps into something innovative. Experimentation brings you a step closer to innovation and data science is all about it!
Summary
Creativity in data science can be anything from innovative features for modelling, development of new tools, cool new ways to visualise data, or even the types of data that we use for analysis. What’s interesting in data is that everyone will do things differently, depending on how they think about the problem. When put that way, almost everything we do in data science can be creative if we think outside the box a little bit.
The best way I can think to describe creativity in a candidate or in an approach is when they give you this moment of “wow!.” Ideally, as a company or team, you want to have a maximum number of moments like this — keep good ideas flowing, prioritize, and execute.
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