What does the future for Data Science look like?
Data Science has come very far today but it does not stop here, the future can expect a lot more than what we see today. With having spread itself in almost all industries today, the coming era can be predicted to be largely dominated by Data Science requirements.
Everything that you do in your daily life today is probably a result of a good strategy devised by a Data Scientist. It is not incorrect to say that all major firms make use of a wide array of data to analyze their market standing, risk prediction, and customer satisfaction to make major decisions. Even the tiniest bit of interaction makes use of data, checking your social media feed, for instance, requires some of your personal information and tracks your behavior on that particular platform. Companies use this data provided by you to improve the quality of their service.
To understand what the future holds, let us first briefly go over the past of how data science reached here. It all started with statistics, used for data collection, analysis, and management. Then came the digital revolution, with the introduction of computers that enabled the collection of a humongous amount of data effortlessly.
This resulted in statistical operations being performed using the computer. But with the generalization of computers and the internet, the amount of data rose exponentially. To be able to manage this data efficiently, Big Data was created, but this huge data store could not be processed with normal statistical methods. This demanded an even advanced computing mechanism to process that data, resulting in the creation of Data Science.
There is a diversity of current trends that impact what to hope for in the future. A few of these are discussed below.
- Explosion of data
As means and applications are growing, the amount of data gathered and to be analyzed is growing largely because almost all organizations today are dependent on data be it agriculture, sports, manufacturing, or even transport. And this new data serves as an ignition source for new data science models. The major sources of this growth include social media platforms and the Internet of Things. As per recent statistics, approximately 7 billion IoT devices are used and connected globally, anticipated to reach up to 21.5 billion in 7 years. And as far as social media is concerned, it is observed that by May 2012, approximately 72 hours of total video was uploaded on YouTube each minute, but by the next five years, this rose to 400 hours of video every minute. This growth is not something we can easily neglect. And with the use of such huge amounts of data, our current models are only getting better at their performance.
- The growing use of machines
The steadily advancing machine learning algorithms and its applications have made the use of data science easy for even novice users. Given the strong dependency of data science operations on machine learning algorithms, a rise in this domain indirectly also leads to a growth in the field of data science. Consider, for instance, Go, a game of strategy between two players, perceived as even more difficult than chess for artificial intelligence. But with some research AlphaGo was created that played against the strongest Go player in the world. Another notable domain is handwriting recognition that is now at par with human skills, even sentiment analysis has improved over the last few years.
- Cloud usage
With the multiplying amount of data, means to store it have also been created, cloud data stores, and systems used to process it has also leveled up moving from CPU to TPU. As observed in a lot of instances, using CPU to train neural networks was very slow and limited the potential of growth. Nvidia emerged with a solution to this with the CUDA programming platform. And then a few years later Google came up with TPUs. Most of the major providers of cloud services are also providing MLaaS, Machine Learning as a Service. The use of Data Science Dockers is also expanding for development purposes because it is a virtual tool that makes deploying and scaling machine algorithms much easier.
So, what can you look forward to?
- Splitting the data science profiles
The term data science is used in a very wide sense today, not specifying the diverse designations such as Data Architect, Data Analyst, Data Visualization, Machine Learning, and Business Intelligence. So, this diversity is hoped to be better recognized as per the job requirement and tasks performed. This would make the domain broader and encourage more domain-specific skill acquisition.
- The inability of companies to manage data and scarcity of data scientists
Many firms have successfully implemented data gathering mechanisms using transactions, user patterns, and similar but what is still beyond most of them is how to manage and analyze this data correctly to assist with decision-making tasks. This is where they need a data scientist. But despite this need, there are not a lot of people who are employed as data scientists. Because becoming a market ready for a job like this requires a lot of effort and usually saturates new learners. Along with acquiring skills excellently, they also need to master how to use them efficiently and effectively.
- Ascending automation
It is a well-known fact that there are a whole lot of algorithms and models available today that can be implemented as per the demand of the application, but what tomorrow can hope for an easier method to implement these models. It is a dire need of the hour that frameworks permit the use of all pre-built, pre-trained, and pre-structured models. By doing this, shifts the aim of the data scientist from building a whole model, taking up numerous days, to using that model to develop solutions that can be used easily.
- Blockchain modification
The technology that deals with the management of cryptocurrencies like Bitcoin is what Blockchain is. Data security will live up to its name and true function with this as transactions will be secure and tracked accurately. This will benefit even more with a flourish in big data and will also make edge computing a possibility.
Challenges faced by Data Scientists
The growth of Data Science is inescapable but there are a lot of challenges that we need to overcome.
- Job security
With the rise in models like GPT-3 and AutoML, it is highly likely that these will be able to perform data analysis tasks as efficiently as humans, which may lead to the data science industry being dominated by these machines and putting humans out of jobs. But also, machines are a result of observing trends and lack the creativity that the human mind has, which may be one of the reasons why no industry will completely be ruled by machines.
- Blending all data
It is one of the capabilities of data science, to process huge amounts of data, but bringing it all together and representing it in a standard way is a very difficult task. Because all this data comes from varying sources that do not always ensure that the data from all of them are in the same format. There also is a possibility that there may be some missing data or some noisy data. All of this needs to be eradicated before processing begins.
- Identifying the right data and Data Sizing
Finding the appropriate data to be able to accurately analyze it, is a crucial issue. From the large volume and speed of incoming data, sorting it and making sense of it is a tedious task. Another issue is finding an accurate amount of data. If too much data is used, it can lead to overfitting and take the focus away from the actual problem. And if too little data is used, it does not help accurately. Thus, finding suitable data and the quantity of it is not always easy.
- Recognizing appropriate use cases for analysis
As per analytics experts, there are not a lot of analytics use cases that exist in real-time. Thus, identifying what situation demands what kind of analysis and models is very critical to any problem-solving. If an appropriate collection of data is not identified, chances are incorrect insights are received.
But you can’t just sit there waiting for these changes to come around. There has to be some way to get ready for the future of data. Here’s how you can do that
This makes the process of digitizing and automating easy for the future. Using automated methods for data collection making the process of scaling very easy. It is also less error-prone than manually doing it. Ensuring the data collected is in a standard format and complete will make the data science process very easy.
- Data Science Unit
Creating a separate division in a company that works exclusively on analytics-focused activities makes it easier for those employees to centralize their skills and models to obtain solutions and devising strategies. It also tells other employees that data science is a priority and an important part of the functioning of the company.
- Adopt data science
Before data science becomes an absolute necessity and its demand rises further in the market, it would be a smart choice to get into the whole trend already. Using machine learning algorithms for analysis and observing patterns starting today will put you ahead of a lot of companies. But you must also rationalize with the human employees to overcome their fear of losing their jobs with the rise of machines and put their rational thinking and creative business managing skills alongside these algorithms efficiently and to give the best results.
To take great advantage of this rising world of machines, we may need to step out of comfort zones and take some risks to fully explore the world of computing for business development and find what suits your needs the best.