Data plays a pivotal role in creating tremendous business opportunities. Companies are slowly discovering the importance of data and the way it benefits the business. They are consistently gathering data from different sources and making more sense out of it.
Impact of Data Science:
Organizations are building a competitive edge in Data Science and reshaping the way they work.
Enterprises are increasingly moving into Data Science to investigate internal and external data better and drive efficiencies. There is a lot to learn about the Data Science and the way it applies to industries and organizations. Here are a few trends of Data Science that await us.
1. Data Science Qualifications
The coming years will attract more Machine Learning Data Scientists within the industry. Machine Learning is sure to become mandatory Data Science career as more organizations incorporate AI into their systems. Strong technology and coding knowledge, R or Python, SAS, and MATLAB, are some of the essential skills. SQL Relational Database is another important skill, including Hadoop, Python, and Java.
Data Scientist position will become more prominent and defined. Programmers will now rush to data science skills in order to grow their careers.
2. Machine Learning and AI
Machine Learning is here to stay. Companies have accepted and employed Machine Learning and AI into its analytics. These technological developments impart a major role in the successful decision-making of enterprises. Key trend awaiting for 2017 is fixing the analytic value chain.
3. Predictive Analytics
Predictive Analytics are further going to penetrate across a wide variety of departments, like sales and marketing, e-commerce, insurance, human resources, real estate, pharmaceuticals, etc. Predictive Analytics field are even going to expand to PAW Business, PAW Government, PAW Financial Services. The trend is unpredictable.
4. Internet of Things (IoT)
The hype around IoT is here to stay. Data Science is getting a step closer to IoT. Organizations are making progress in identifying how and where IoT is applicable to drive business value and leverage their business models.
IoT ensures that the collection and analysis of data is safe, secure and ethical. Data Science with IoT even leverages into an RIL (Radio Interface Layer) across edge processing, real-time processing, and deep learning.
5. Graphical Representations
The act of displaying has come a long way from Simple line graphs, box plots to Graphical Representations. Graphical Representations are continuously expanding and displaying data patterns of predictive analysis. This process is newly termed as “Data Visualization Process”.
6. Deep Learning
Deep Learning indicates wide-spread of technologies in areas where data is plentiful. Data Science predictions are generally limited to small data and implicit behavior. Whereas, deep learning aims at more data-rich domains. There is no doubt in denying the fact that Deep Learning is going to be one of the important approaches in solving Machine Learning problems.
Auto Correction, Photo Processing, Facial Recognition, are some of the applications that are going to significantly impact our lives.
7. Industrial and Organizational Changes
Usually, Industrial and Organizational psychologists apply science to workplace issues. In executing the process, they perform surveys, create tests, select procedures and more. Ind/Org psychology purely depends on deductive and statistical methods.
This is good to some extent, but embracing more of Data Science ensures more profits. To avail the major benefits, Ind/Org community is embracing the Machine Learning and Inductive Methods of modern Data Science. 2017 will experience this fascinating fusion of mindsets.
8. Forward Thinking
General hiring in enterprises is based on HR metrics, resume buzzwords and people analytics. This might seem a bit of hectic and tiring process.
Forward Thinking is a stand out in 2017 with billions of HR and workforces at stake. Companies are even ready to embrace an analytical and technical approach as these services efficiently handle employee engagement.
9. Practice among the “Massess”
Major companies are reaping benefits through institutionalization. The big trend in 2017 is the highest ever before the adoption of Data Science among the “Masses”. Small companies are noting high successes due to institutionalization. They believe Data Science can do a lot to them. 2017 is going to witness the explosion of Data Science as a domain among the Masses.
10. Greater Impact of Data Science
Companies have observed a big impact by adopting Data Science. They have developed influential data science teams that make decision-making simpler. They are leading ways in embracing tools and processes that earn big points. Particular workflows include: sharing and collaborating data science and pushing models within production.
Few of these techniques are recommended in employing at business use-cases, ad targeting, and algorithmic trading.
11. Operational Cognitive Applications
Cognitive will move from science projects to operational applications. More enterprises will classify the cognitive tools and apply them to appropriate business problems.
Huge monolithic cognitive technologies are broken to form single function APIs which are combined together to form complete systems.
12. Healthcare Industry
Data Science proves to be accurate in predicting the Virus outbreaks and patient behavior. Data Science usage in healthcare is predicted to grow further and improve day-to-day needs of many.
Electronic healthcare records are resulting in all-time high data. Data Scientists are leveraging at deciphering this data.
13. Move to Cloud
Data is useless if it cannot be utilized to the maximum to evolve at data-driven insights. Organizations are moving their data to cloud driven by integration and flexibility. This process reduces the complexity of computing sources. The movement to cloud is going to accelerate the easy transition of data to action.
Organizations can now employ large data science teams without any massive investment.
14. Autonomous Technology
Now is the notable time for self-driving technology. This advanced technology skills is hitting the market hard.
The adoption of Autonomous technology is on a rage. More and more manufacturers are putting their foot forward in automating their cars that run hands-free.
15. Conversational Interface
Though chatbots have been from time unseen, they are gaining popularity in recent times due to the adoption of AI and Machine Learning.
Gone are the days when humans used to interact with computers by understanding their language. Now is the generation where computers ‘listens’ and adjusts to the user’s instructions. Chatbots are going to be the hottest trend. Most creative solutions are taking them to the next level.
16. Stronger Data Security
Cyber attacks are raising the question of Data Security. This will eventually rise the security analytics costs. Deep learning detects anomalies and other data science security fields in various business domains.
Another quickly developing area is Behavioral Biometrics, which identifies and distinguishes persons on the other end.
17. GDPR Considerations
Regulation is going to change the way businesses perform data science in this era. GDPR builds transparency among the organizations and their customers. This brings actionable insights that benefit the sector.
Though these changes and trends seem to be different and unrelated to each other, they are actually all linked up with data, quickly and conveniently. There is no doubt that, Data Science is going to be mainstream and contribute equally to each part of business needs.
Sirisha Paladhi possesses love and passion towards writing, which brought her into this field. Presently, she is working as a Content Writer at Mindmajix and TekSlate. During her career, she has written many articles on technology innovations. In her pass time, she relishes in making handy-crafts.
LinkedIn URL: https://www.linkedin.com/in/paladhi-sirisha-74987881/