As a Sales & Marketing Head, Mr. John’s goal is to boost the sales of his organization by determining the drivers of sales. To achieve the goal, he needs to be clear about the following:

  • The most profitable products offered by the organization
  • The in-house promotional and marketing strategies 
  • The effective deployment of product placements 

To impact the outcome of the sales project, Mr. John hires a data scientist who collects information and derives results based on the company’s requirements. 

In the aforementioned example, the data scientist strategizes the business process in a way that the organization gains maximum profit.  

Gathering information and recommending changes in a business strategy is an imperative step in any data science project. Hence, a data scientist needs to partner with several teams and effortlessly communicate his/her findings to chief decision-makers and stakeholders in the organization so that required steps can be taken based on the recommendations made by the data scientist.

For any successful data science endeavor, it is very crucial for the data scientist to comprehend the recent trends in the data science landscape, which can add value to a company’s business. So, which data science trends are currently popular and widely discussed? Let’s find out!  

  1. AI and ML are Here to Stay

The buzzwords like artificial intelligence (AI) and machine learning (ML) will not expunge any sooner. While both the technologies are still in their infancy, we see advanced AI- and ML-driven applications across a multitude of industries such as education, healthcare, and logistics, among others. Automated ML is gaining traction and transforming data science with improved data management. Those days are not far when automated analytics will enable ML tools to identify the hidden data patterns, giving insights into a customer’s journey with an organization. Data science will have the same impact on consumers that computers had. It will empower people to be more creative and productive by liberating them from trivial tasks.

Also, the incorporation of AI will improve decision-making and enhance the overall business experience. Several applications and services are powered by AI to improve the overall experience; every organization is integrating some form of AI into their applications to refine their business processes. Though ML is not widely adopted owing to its high barrier to entry,  the advent of data science is making the playing field even for business professionals.

  1. The Blockchain Culture

All we know about blockchain is that it is the technology underpinning cryptocurrencies. But blockchain is much more than just the technology behind bitcoins. Due to its highly secured immutable ledger, it can be employed to record tremendously detailed transactions. The technology can have far-reaching implications pertaining to data security. With transparent record-keeping and robust security, data scientists are creating a culture of blockchain in order to accomplish their mission that was previously considered impossible. 

Currently, data scientists are relying on blockchain technology to track and validate information at every point on a chain. Blockchain, being a decentralized ledger, safeguards information through multiple signatures, preventing data leaks and hacks. 

  1. Augmentation of Embedded Analytics

The growth of embedded analytics will metamorphose business intelligence (BI) from being retrospective to proactive. Some of the organizations have already started to take advantage of embedded analytics on a wider spectrum as an extension to operational visibility as well as a way to render meaningful engagements with customers, partners, and suppliers. When coupled with BI, embedded analytics makes data science more approachable to business professionals and key decision-makers.

As more organizations employ AI and ML to gain insights from multifarious data sets, the implementation of embedded analytics will align with the conjunction of different technologies.

Data scientists spend much of their time in selecting the right metrics to realize a program’s success or failure. But today, everyone needs to have a little knowledge of data science in order to make better and more informed decisions. 

When self-service analytics is embedded in applications, users are equipped with data science tools to make predictions directly from their data.

Embedded analytics act as a plug-and-play BI tool for the information that data scientists collect. The collected data is made searchable and provided through a user-friendly interface to end-users. Adding embedded analytics to software saves the time taken to convert raw data into valuable insights, thereby improving customer engagement.

  1. Edge Computing will Make it ‘Edgy’

With IoT devices on the rise, edge computing is increasingly gaining momentum. With uncountable IoT devices and sensors collecting data for analysis, enterprises are running after data analysis and processing close to the source of origin. Edge computing is making strides to gain proximity to the source of information, solving issues related to connectivity, bandwidth, and latency. According to a report, new cloud pricing models will serve particular analytics workloads, contributing to five times higher investment on the cloud vs. on-premises analytics, by 2020.

  1. More Regulatory Standards

Tremendous data is generated every day, and technologies like IoT add to the data generation. With voluminous data, security becomes a concern, following which more regulatory schemes pertaining to data security can be mandated in 2019 both for organizations as well as individuals.

Data regulatory standards such as the European General Data Protection Regulation (GDPR), which was imposed from May 2018, restricted the management of personal data to some extent. So, new protocols and procedures to secure data can be expected in 2019.

What Springboard has at its Disposal?

With such trends considered to prevail in recent times, the future of data science looks bright. Undoubtedly, data science will witness tremendous development in the near future, and organizations leveraging the concepts of data science will have a competitive edge. 

Data science is a broad spectrum. With data science gaining prominence, the level of data literacy will improve across the workforce who will develop a better understanding of utilizing data more appropriately. In order to thrive in the technology-driven era, future data scientists need to keep pace with the data science trends, and this is where Springboard comes as a chaperone. 

To guide professionals to pursue a career in this burgeoning data science landscape, Springboard has developed data science courses under the supervision of industry experts.  We at Springboard, offer data science programs, helping aspiring minds develop job-ready skills and professional portfolios with personalized career coaching.

Check out our Data Science Career Track and jump on the bandwagon.