Data analysis is at the core of many recent inventions. Data collection and analysis are often the cornerstone of organizations, whether in healthcare, decentralized work, online shopping, customer service, or internet banking.
According to a survey, the volume of data and associated analytics will expand at a CAGR of 13.5%. Consequently, the industry could increase its expected value from USD198.08 billion in 2020 to USD684.12 billion in 2030.
Learn about the top data and analytics trends in this article that are changing the way we approach everything from the economy to education to the environment.
1. Data as a service
The demand to exchange data, models and knowledge between companies is at an all-time high as the future of collaboration moves outside of organizations. Companies in the financial services or energy sectors, for example, that have built their proprietary intellectual property through years of study and innovation, will now try to promote their products to their peers. Another example is retail, looking for ways to integrate its supply chain in the face of pandemic-induced challenges. This will encourage companies to develop data-as-a-service systems with functionality similar to SaaS.
2. Agile data operations
The ‘Agile Principles’ for application development is dominating operational data management, responding to the need for organizations to leverage data in day-to-day decision-making. To provide the necessary tools, procedures, and organizational frameworks, companies require their DevOps (Development Operations) teams to collaborate with data scientists and data engineers to respond to business needs. Fundamentally, DataOps aims to increase the speed at which tangible solutions are delivered while also providing a framework for monitoring data health and usability by minimizing downtime.
3. Clean data rooms
Similar to data monetization, many companies are exchanging confidential information and intellectual property in distributed data clean rooms, an environment that is visible to the public. The ability to merge partner-provided data with an organization’s proprietary data while complying with all regulations, protecting privacy, and preserving a competitive advantage is the key success factor of a modern data clean room. Before providing the data and models to clean rooms for cooperation, the data providers must anonymize and encrypt the data. This collaboration could be highly beneficial for the media and advertising sectors, as well as some highly regulated companies such as financial services, energy, and healthcare. It should be noted that the challenge associated with this trend is a mixture of technological aspects and management aspects. Without the appropriate technology to easily and securely share data, management becomes unfeasible. However, just having the technology is not enough, since a robust data operations model must be established to capture the value of data sharing while mitigating associated risks.
4. Increased analysis
Producing insights from data is the goal of traditional analytics, which is often achieved through predetermined queries and derived reports based on pre-gathered customer needs. Otherwise, data scientists and engineers may need to spend weeks or months preparing data for business intelligence, but a current trend in data analytics is making use of machine learning and natural language processing to automatically generate analytical reports. Low-code tools make those capabilities accessible to business analysts who are not proficient in languages like Python or R. Without the need to design data pipelines, augmented analytics will enable business users to get immediate answers to ad hoc queries from designate data sources.
5. Democratization of data (Data Mesh)
Zhamak Dehghani used the word ‘Data Mesh’ for the first time in 2019, referring to the concept of considering data as an asset and democratizing access to company data. Multiple lines of business come together to share and benefit from each other’s data as more companies use data grids. The principles of the data mesh framework will be fully implemented by those using the public cloud, including domain-oriented decentralized data ownership and design, data as a product, self-service data infrastructure as a platform, and federated computational governance.
6. Business intelligence
Business intelligence, sometimes known as BI, has benefited greatly from the tools and methods that the discipline of data analysis has produced in the last ten years. Simply put, business intelligence uses data analytics, including but not limited to AI, to extract meaningful patterns from unstructured data and turn it into actionable insights. In 2023, a massification of practices and tools is expected, especially in SMEs. Thanks to a range of accessible tools, combined with mature practices, complex projects are no longer required to make the first steps at the organizational level.
Data management solutions will be managed by key business users. The reliance on IT specialists is fading with the rise of low-code/no-code solutions. This opens up new career paths, embedded in evolved operating models. Low-value activities, such as data access, storage, and preparation, will decrease in relevance. High-value activities, such as data analysis and interpretation, will increase in importance and sophistication.