New Year brings many things to look forward to. Data science is an ever-growing area full of things to be excited about. But, with the COVID-19 pandemic still in effect, keeping track of new trends can be difficult. With that in mind, we’ve compiled a list of Top 10 Data Science Trends 2021 will bring to us.
Data Science During the Pandemic
We’ve all witnessed the impact of COVID-19 in 2020. It reshaped the world in ways that we could never imagine. Ongoing data science production setups have experienced a dramatic shake-up. The majority of the segmentation and forecast models have failed.
This was due to:
- A sudden change in shopping patterns
- Interruption of supply chains
- Border lockdowns
However, where others have failed, some have prospered - cloud-based data platforms and data analytics in particular. Their primary role was stabilizing businesses and laying the foundations of future processes. It’s crucial more than ever to have a data-driven plan to make quick and informed decisions.
An example of this is computer vision in healthcare. You can track body temperature via a combination of AI and thermal image processing. This means that medical staff has contactless, real-time insight into patients’ conditions.
Now, let’s take a look at how everything will pan out this year.
1. Deep-Fake Legislation
Over the last three years, deep fake audio and video have been on the rise. While some of them were pretty amusing, others proved to be quite dangerous.
Take, for instance, the election incident that occurred in India. Approximately 15 million people saw an altered video of the president of BJT criticizing the current Delhi government in English and a dialect of Hindi he did not speak.
In the face of recent U.S. elections, the state of California passed a bill that made the circulation of deep fake videos of politicians illegal within 60 days of an election.
In 2021, we can expect more legislation around deep fakes. But what’s missing is the way to identify them. The Rochester Institute of Technology is working on a deep fake detection software, along with a browser plugin called Reality Defender.
2. Adoption of End-to-End AI in Small and Medium Businesses
One of the hottest data science trends in 2021 is going to be RPA. It stands for Robotic Process Automation. RPA uses artificial intelligence methods to mimic the actions of administrators or managers. This issue used to be difficult for computers to handle. Filling out forms, sending emails, or creating documents is done easily with the help of AI.
The benefits of process automation will depend on the type of business and produce varying ROIs. Refer to the chart below to get a general idea of what to expect.
Implementing RPA with your business process management tools can be done with ease. The BPM tool highlights all the tasks the RPA tool can target, resulting in a perfect starting point for automation.
3. Data Science Outsourcing
With the adoption of intelligent automation in business models, finding the right talent for in-house analytics will become increasingly difficult. Having in-house data scientists allows for a smoother understanding of how the business operates. But what most firms fail to understand is that the talent pool is quite shallow.
Nearly 85% of AI projects fail due to inadequate coordination of analytics teams. Instead of wasting resources on building divisions that might not deliver, companies are looking to outsource data science projects.
Vendors offer teams of seasoned data scientists who have experience with advanced data science tools and techniques. Put simply, it’s a more accessible and less time-consuming way to harness the potential of big data.
4. Data Visualization
Amid the pandemic, we’ve been flooded with infection heatmaps, transmission pattern charts, etc. All of these were provided by data visualization tools, and it wasn’t an accident.
With the emergence of cognitive frameworks and multidimensional imaging, DataViz has reached new heights. It enables users to visualize large amounts of data and has become a modern alternative for visual communication.
Businesses rely on it to:
- Optimize their decision making
- Identify the core aspects of impacting business results
- Forecast future patterns and trends
Projections are that, by 2025, data stories will become the most common way of employing analytics. Among these data stories, 75% of them will generate automatically using augmented analytics techniques.
5. SaaS Business Intelligence
The pandemic has forced many companies to go full remote. Flexibility is among the top priorities in such environments, and SaaS BI is what enables it. Accessing data on the cloud, from any device, at any time allows data movement as we’ve never seen before.
SaaS allows for greater insight into the company’s analytics, which has become a primary focus of business management and development. According to the Forbes’ Cloud 100 list, shares of SaaS, databases, and fintech companies have grown substantially. We see this trend continuing well into 2021 and beyond, as large firms resume to rely on the power of big data insight.
From the technical aspect, future business analytics tools will perform analysis independently, with adaptability to various working conditions and demands.
6. Big Data in Business
As discussed in the previous segment, data analytics is becoming a staple in businesses of all sizes, small or big. Adhering to customer wishes and feedback is always a good foundation for building a profitable business. Essentially, it gives buyers a sense of insider connection and security.
A solid example of a customer-centric business is Netflix. They purchase scripts that are similar to the shows that are popular among their viewers. With the use of power data analytics, they can anticipate what their users want. This way, they’ve achieved a growth of more than $50 billion in 2020.
The key points of most data analytics:
- Customer surveys
- Demographic information
- Online reviews
- Individual feedback, etc.
7. The Shift in Attitude Towards Privacy
For a long time, third-party tracking cookies have been relied upon for online advertising. However, recent legislation on data privacy and a change in customer attitudes demand better data collection transparency.
As a direct consequence of that, third-party cookies are no longer acceptable as a foundation for digital marketing. Current plans of Google and Facebook include phasing out cookies within the next two years.
Right now, GA4 of Google presents itself as the new analytics solution. GA4 encompasses a more data science approach to analytics via machine learning and deep learning algorithms. It primarily focuses on events and less on user sessions, resulting in a more contextual approach.
8. The Rise of Metadata
Utilizing metadata, machine learning, and data fabrics to optimize and automate data management processes is set to reduce data delivery time in the future. Most surveys project a 30% faster delivery by the year 2023.
You can use deep learning techniques to:
- Recommend future actions
- Auto-discover metadata
- Auto-monitor governance controls, etc.
All of this is a direct consequence of a concept called data fabric. Data fabric utilizes continuous analytics over existing, discoverable, and inference metadata assets. They intend support the design, deployment, and utilization of integrated and reusable data objects. The process is independent of the deployment platform or architectural approach.
9. Data Scientists as Decision Makers
We are all witnesses to the impact data science is making in the business world. Data science solutions are revolutionizing global business strategies on a level never seen before. As companies become more heavily reliant upon machine learning for data insight, data scientists will move to the forefront of decision making.
This requires the support of the right data at the right time. The untapped potential of big data is beginning to surface, as evident in the shift internal IT organizations’ priorities.
However, if inadequately managed, data becomes an enterprise’s liability. You need to unleash the data in a quick, efficient, and legally responsible way. It will ensure a future for competitive differentiation and consumer confidence.
10. Containerized Application Deployment
For most enterprises, the traditional platform for data lakes has always been Hadoop based. However, in more recent times, containerized application deployment and Kubernetes have started to gain traction.
Companies have realized the benefits of abstracting the physical infrastructure, alongside the adoption of public clouds for agility. Kubernetes saves money and time because it takes fewer resources to manage IT. It is a consequence of a uniform toolset across environments, unlike the case of hybrid and multi-cloud environments.
For a more in-depth overview of Kubernetes, refer to the link below. In short, the pros and cons of using Kubernetes are:
COVID-19 pandemic has undoubtedly accelerated the already rapid adoption of data science in business processes. The world was in desperate need of a quick, dependable data transfer and analysis. Machine learning techniques are continuing their ascend into the IT mainstream, and the future is looking encouraging.
With promising signs of a post-COVID-19 future looking more imminent, data science trends in 2021 will keep evolving. In this article, we’ve predicted the following ones:
- Deep-Fake Legislation
- Adoption of End-to-End AI in Small and Medium Businesses
- Data Science Outsourcing
- Data Visualization
- SaaS Business Intelligence
- Big Data in Business
- The Shift in Attitude Towards Privacy
- The Rise of Metadata
- Data Scientists as Decision Makers
- Containerized Application Deployment
What do all of these have in common? The answer is that data scientists are rising to the forefront of the business and political world. Data is the new oil. It is a fuel that powers the decisions making via intelligent automation and deep learning. It is certain to say that we have many things to look forward to.