Over the last decade, the data-driven business transformation has encouraged business leaders to reinvent business models, new revenue streams, customer experiences, operational models, and processes. By extracting actionable insights from business data, organizations can often close the last-mile gap in analytics and drive faster value realization. It’s so popular that Global Newswire market research predicts that the data science market will be worth about $133 billion by 2026.
Given the impact of data analysis on the business world, it is important to identify future trends in this area. The next key trend will undoubtedly play a role in shaping tomorrow’s data analytics industry.
Today’s leading customers want their data analytics partners to have a sustainable, mature, product-centric delivery model and want to move from a project to a product mindset. Data science does more than just create accurate algorithms and build dashboards around them.
Following the Covid19 pandemic, companies have dramatically changed their revenue model. The fixed cost model is a thing of the past, and data analytics solution providers are expected to be at greater risk with a profit-sharing model or a results-based revenue model.
Agile work styles are well established in the software development industry. However, agile methods have only recently begun to build a foothold in the world of data science.
Customers prefer service providers to operate and standardize data science models and transfer them to production. The answer to commercialization lies in the three Ops MLOps, AIOps, and Data Ops. MLOps or ModelOps refers to a collaborative process for building, managing, deploying, and continuously monitoring machine learning models to consistently provide the right input to your organization. AIOps or Artificial Intelligence Ops are implementations of AI in IT operations aimed at controlling hybrid and distributed IT structures.
Overcoming the last one mile of data science can be a daunting task. Following a complete data collection, preparation, research, and modeling lifecycle, the final mile is to operate these data models and transform them into actionable insights that impact your business.
Not only its work, methodology, scope, and speed. Data is at the heart of this change. Large companies use data but are hungry for insights. For successful recruitment, the analysis must be an overall initiative to solve the entire problem domain. A data analysis strategy is best implemented as a large-scale long-term vision, increasing short-term profits and involving all relevant functions. These influential trends will change the shape of data analytics and technology after 2022.
