Source: dataversity
The exponential growth of data, partly generated by sensor-driven devices, is making Data Science and machine learning (ML) market differentiators in global business-analytics solutions. With the rising demand in Data Science and ML skills, 2020 may well be a witness to several new trends in the field.
“[I]f our digital universe or total data content were represented by tablets, then by 2020 they would stretch all the way to the moon over six times. That’s equal to 44 zettabytes of data, or 44 trillion gigabytes.”
They made that prediction back in 2014 and it has certainly become true.
Towards Data Science reports:
- Currently, the daily data output is more than 2.5 quintillion bytes.
- In the near future, “1.7 Mb of data will be created every second for every person on the planet.”
- A wide variety of Data Science roles will drive these massive data loads.
Trend One: Growth of Data Science Roles in 2020
IBM predicted that the demand for data scientists will increase by 28 per cent by 2020. Another report indicates that in 2020, Data Science roles will expand to include machine learning (ML) and big data technology skills — especially given the rapid adoption of cloud and IoT technologies across global businesses.
In 2020, enterprises will demand more from their in-house data scientists, and these special experts will be viewed as “wizards of all business solutions.” Another thing to note is that the annual demand for Data Science roles, which includes data engineers, data analysts, data developers and others, will hit the 700,000 marks next year.
This Data Flair post explains the shades of differences among Data Science roles such as data engineers and data architects. If you have just entered the field of Data Science, you may want to explore the 10 questions to ask before making a career decision.
IBM, Burning Glass Technologies, and Business-Higher Education Forum (BHEF) forged a “research partnership” to reduce the existing skill gaps in Data Science and business analytics with the help of actionable insights currently shared between the academia and the industry. These insights can be found in The Quant Crunch: How the Demand For Data Science Skills Is Disrupting the Job Market.
The Data Scientist of the Future: What Will They Be Doing? discusses the gradual evolution of the Data Science role into more of a collaborator and a facilitator role, rather than that of a technical expert.
Trend Two: Widespread Automation in Data Science
As an Analytics Insights article suggests, a Forrester report titled Predictions 2020: Automation includes a warning that “over a million knowledge-work jobs will be replaced by software robotics, RPA, virtual agents and chatbots and ML-based decision management.” In another report, Forrester has warned that automation in untrained hands can lead to potential hazards. A phenomenon called “hyper-automation,” or an uncomfortable blend of multiple ML applications and other technology platforms may render data-technology ecosystems unsustainable in about 80 per cent of enterprises.
Trend Three: Evolution of Big Data in AI-Ready Data Landscape
Big data analytics received a major push across global businesses in 2019 when data scientists partnered with data engineers and data analysts to mobilize the mainstream use of AI and ML algorithms across business analytics platforms. Automation of Data Science tasks was a big thing in 2019. In 2020, this automation frenzy in Data Science will continue, enabling data scientists “to create their own, near production-ready data pipelines.” As data sources become more varied and complicated and automation of Data Science prevails, businesses may experience more innovations in big data analytics.
2020 will also witness the major analytics vendors rolling out integrated platforms with more automated Data Management features and benefits. Data Science Trends in 2019 pointed out that though big data has “taken Data Science forward by leaps and bounds,” AI and related data technologies have now confronted dig data with many logistic issues difficult to overcome.
Other Data Science Trends for 2020
Business leaders can use the following trends to set their business and data-technology priorities; these are predicted to have a disruptive business impact in the next three to five years:
- Augmented Analytics: Major business analytics vendors will incorporate augmented analytics into their solutions by 2020 to provide a market differentiation between themselves and their competitors. The rapid adoption of cloud computing and the growth of IoT and connected devices are major drivers of augmented analytics. Many business clients may prefer augmented analytics over traditional analytics to reduce human errors and bias.
- Natural Language Processing (NLP) and Conversational Analytics: As data and analytics jointly drive the current customer experience, talent management system, supply chains, or financial operations, NLP and conversational analytics will complement augmented analytics in 2020. Find additional information in The Future of NLP in Data Science.
- Continuous Intelligence: Starting in 2020, more than 50 per cent of emerging business solutions will “incorporate continuous intelligence,” which utilizes real-time data to guide business decisions.
- Automation of Data Management: With the sudden exponential growth of data and short supply of skilled data-technology experts, enterprises are increasingly demanding automated Data Science and business analytics platforms. In 2020, over 40 per cent of Data Science tasks will be automated, thanks to the rapid integration of ML in Data Science platforms.
- Graph Databases: Graph databases and graph processing will be used at an accelerated pace in the “next few years” to enable adaptive Data Science. Graph databases have the capability to store both structured and unstructured data and even a combination of them.
- Data Fabric: The data fabric helps in building the “business context” of data, thereby making the meaning of the data comprehensible to the users. The data fabric, conceptually, supports all enterprise data. The data fabric can also be designed to provide “reusable data services, pipelines, semantic tiers, or APIs” through blended data- integration approaches.
- Autonomous Things: This technology indicates the use of physical devices with highly automated (AI-enabled) features to reduce human intervention. In traditional systems, these functions were generally performed by humans.
With the California Consumer Privacy Act (CCPA) put into practice in 2020, data scientists and data analysts will need to become familiar with and knowledgeable about CCPA and other related data regulations impacting data processes. Thus, Data Governance will gain more importance in Data Science practices in 2020.
