Data and analytics are helping change the business world and as we head into 2023, this is the right time to predict how to work with data, by getting ready with the new year’s top data and analytics trends. Some of the top data-related trends driving the market today include advances in Data Science, data analytics, and artificial intelligence (AI), which are collectively transforming businesses around the globe. 

Why Follow Data Science Trends?

Every year around this time, industry watchers, business leaders, business operators, and Data Science enthusiasts wait expectantly for a new set of Data Science trends to brew, emerge, and dominate the business environment for the next 12 months.

Bernard Marr, an industry expert, thinks that Data Science “helps (businesses) to react with certainty in the face of uncertainty — especially when wars and pandemics upset the established order of things.”

As global organizations strive to become more data-centric, and corporate data activities take center stage, many of the Data Science trends that emerged at the beginning of 2022 will continue to dominate 2023. The projected Data Science and technology trends will hopefully help businesses to better prepare their new year’s business strategy and set goals for improved business performance. 

Major 2023 Data Science Trends 

It is practically impossible to include all the trends predicted for 2023 within the limited span of this article. Thus, some major trends, believed to have the maximum impact on businesses in 2023, have been short-listed here: 

• The tremendous growth of data literacy programs: Investments in data literacy programs will see a spike in 2023. The ultimate goal of data literacy is data democratization, which refers to making business data available at every level of an organization, not just to analytics teams and top executives. Reduced reliance on data scientists for day-to-day data processing tasks is the desired outcome.

• The rise of augmented analytics: The widespread adoption of augmented analytics to fundamentally change how data is collected, managed, and processed is on. The main driver of these automated platforms, AI, will not only remove the need to spend time on routine, repetitive data-processing tasks but also enable the business workforce to take action based on insights from data, regardless of role or technical skill. Augmented analytics uses machine learning (ML) and natural language processing (NLP) to automate and process the data and also extract insights from it, which otherwise would have been handled by a data scientist. 

• Automation of data processes: Both ML and NLP processing will be used to better automate Data Science processes that were handled by humans, thereby increasing the effectiveness of the workflow. 

• Automation of big data analytics: ML and AI tools will attempt to control massive amounts of big data gushing out of data centers, intercepting the data for extracting hidden insights, and then saving and projecting the insights in an understandable format. AI and ML tools will together reduce time spent on repetitive data gathering and cleaning tasks, make predictions more accurate, and enable employees to “act on insights” from big data, regardless of their roles and technical skill sets.

• The rise of cloud platforms: The rapidly growing use of cloud-native technologies is giving a boost to autonomous analytics, allowing even tech-adverse consumers and end users to gather, analyze, and interpret their data. AI and ML models will increasingly deliver accurate predictions about consumer behaviors. 

• Real-time analytics and edge computing: As real-time data analytics and evidence-based decision-making become the cornerstones in business and government, an increasing number of enterprises will take advantage of the power of edge computing. According to IDC, more than 50% of “new enterprise-class IT will be deployed at the edges of networks.” Thanks largely to the evolution of cloud-based software, organizations are now able to monitor and analyze volumes of enterprise data in real time and make necessary adjustments to their business processes accordingly. 

• From augmented analytics to augmented BI: Augmented analytics is now performing data scientist-level tasks, ranging from helping prepare data to automatically processing data and drawing conclusions from it. Augmented analytics is likely to see various developments over the next few years, becoming a major player in the rise of augmented BI platforms. 

• Data as a Service: Many cloud providers are now offering DaaS (data as a service). The DaaS technology enables users to consume and access digital assets through the internet. DaaS enables businesses to extract enriched market intelligence and valuable insights from their proprietary data. DaaS enables organizations to design marketing strategies, support corporate growth, and can set them apart from their competitors. 

• Augmented Data Management: AI tools empower businesses to analyze data and extract insights at great speed with the help of automated algorithms that continuously improve as they are exposed to more and more data. Augmented Data Management will also allow data from inside and outside of a business to be combined using advanced analytics. For example, SAP offers agile solutions for setting up autonomous Data Management and analytics across a business. 

• Monitored market intelligence: The practice of capturing and monitoring market intelligence helps global organizations “improve their performance” in an increasingly complex business landscape. NLP will play a critical role in market intelligence monitoring and tracking in 2023, as businesses use data and insights to shape their future strategies. 

Conclusion: Will Data Science Deliver in 2023?

Data Science continues to explode and develop, creating new efficiencies, deployments, and trends that will complement industry-wide growth and innovation in years to come. The corporate attention spans and expectations have dramatically changed over the past two years. As Data Science advances to augment human potential, data will redefine the foundations of business, and drive critical value. 

Companies, large or small, embracing the above trends will be well-equipped to forecast trends, discover opportunities for enterprise growth, and improve profitability. They can then appropriately integrate these solutions in their business processes, and leverage the power of big data. Cloud platforms will play an important role in data democratization, as mentioned above, because they allow businesses to operate with data without investing in costly, specialized Data Science operations. Though not mentioned here, Data Governance (DG) issues will remain a central concern for Data Science practitioners in a highly regulated data-driven world.

This new year will prove whether these Data Science trends deliver on their promises.

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