Data is essential to corporate operations, fostering creativity, cost-effectiveness, and more informed decision-making. A Wavestone survey last year found that 78% of CEOs made good progress using data to drive innovation, 49% are now managing data as a business asset, and 48% have established data-driven companies.
Nevertheless, most businesses have yet to achieve a plateau in data productivity. Thus, 82% intend to boost business intelligence and data analytics funding in 2025. Where will those investments go, though? Edvantis conducted a market analysis to ascertain this.
Key Trends in Data Analytics for 2025
After shifting much of their data and analytics to the cloud, companies are now concentrating on improving their processing capabilities. Approximately 70% of executives anticipate that more than half of the company will be on the data lakehouse, and 86% additionally intend to consolidate analytics data into a single location.
However, larger data sets also present new challenges, including the potential for “data swamping,” low data visibility, and the resulting silos. Companies still struggle to identify the most significant and relevant information from the enormous amount of raw intelligence available to them.
1. Enhancing the Availability and Quality of Data
Although most corporate executives rank “data-driven decision-making” as the primary objective of their data initiatives, 67% of them still have doubts about the accuracy of the data they use to make these choices.
High data silos and inadequate data quality standards directly contribute to a lack of confidence, which reduces the dependability of analytics models. Accurate, reliable, and relevant data are also necessary for ambitious AI initiatives. Just 12% of companies say their data is easily accessible and of sufficient quality to enable successful AI applications. In the absence of appropriate underlying data, AI model outputs may be biased, inaccurate, or meaningless. The latter raises serious issues with security and compliance.
Synthetic data, or computer-generated, fictitious data, has some traits in common with genuine datasets, is one solution to the data availability issue that is currently being researched.
Data scientists can train and test machine learning and deep learning models on synthetic data before releasing them with restricted real-world insights. Additionally, synthetic data can expose consumers to datasets to compensate for bias and limitations in real-world datasets.
2. The War Against the Data Silos Is Still On
Companies sit on mountains of data, yet it’s as straightforward as drilling for oil in the real world to use these reserves. Data quantities, which are currently growing by roughly 50% annually, are reflected in the growth of data silos. Therefore, eliminating it calls for ongoing work rather than only sporadic tactical measures applied to particular apps or data systems.
In addition to an even larger data management infrastructure estate, 56% of leaders now deal with 1,000+ data sources on average. Thus, it should come as no surprise that 68% of business executives in 2025 are most concerned about data silos, a 7% increase from the year before.
Companies must essentially invest in a platform for data discovery and metadata management to create and maintain a data catalog. DataHub, developed by LinkedIn, is an open-source solution with real-time metadata stream capabilities built in Python and a metadata service and index applications written in Java. For the Microsoft ecosystem, Microsoft Purview is a platform for data discovery that offers additional features for implementing unified data security, governance, and compliance.
3. Implementation of Real-Time Data Streaming
Traditional data analytics systems are built around pre-planned batch ETL/ELT data transfers. However, many big data analytics use cases in the logistics or healthcare sectors require real-time data streaming, which is often referred to as the ability to continuously ingest, transform, and analyse incoming data with low latency.
By 2026, event processing and streaming data will be part of the majority of businesses’ conventional information infrastructures. The main technologies used to build such architectures include open-source Apache Kafka, AWS Kinesis, and Azure Stream Analytics, all of which are excellent options for scalability and security. The Apache framework is used by some of the biggest financial firms, such as JP Morgan Chase and Goldman Sachs, for risk management, fraud detection, and real-time financial data processing.
4. Deployment of LLMs for Accessible Analytics
Due to corporate clients’ fear of complicated dashboards, businesses are increasingly looking for more straightforward interfaces for handling large datasets and presenting analytical findings. Companies may increase the accessibility and interpretability of analytical insights by implementing conversational AI interfaces.
GPT, Gemini, Claude, and other large language models (LLMs) can be fine-tuned to perform various data-wrangling and modelling tasks.
- Use natural language to query business databases.
- Create personalised charts and models upon request. Find the most up-to-date and pertinent accessible data.
- Create projections, trend predictions, and suggestions for the best course of action.
- Describe the intricate correlations, outliers, and diagrams in the modelled data.
5. Compliance with New Regulations
Compliance is a frequent barrier to the application of AI and data analytics. The global regulatory environment is fragmented due to the many approaches to legislation that nations and privacy watchdogs have chosen.
When the AI Act takes effect in 2025, the EU’s “free reign” AI era will be coming to an end. Its enforcement on forbidden use cases, including as emotion detection, social scoring, biometric categorisation, predictive policing, and untargeted data scraping from public sources, will begin in February 2025. Provisions will be extended to general-purpose AI (GPAI) models by June 2025.
However, the UK’s new Data (Use and Access) Bill, which was unveiled on October 23, 2024, includes clauses that loosen regulations about the application of AI in automated decision-making. It still mandates, meanwhile, that “protections for the data subject’s rights, freedoms, and legitimate interests are in place” for data controllers working for organisations. These include informing users about the AI’s decisions and enabling them to challenge or obtain human involvement when necessary.
After the election, there is still ambiguity in the US over federal AI legislation. Approximately 700 AI-related bills were filed in states through 2024, and many of them could become law by 2025.
Conclusion
Through 2025, leaders’ primary priority will be integrating horizontal data analytics into more business divisions and procedures. To achieve that, most operators will continue to invest in improved data governance systems to improve data quality, availability, and accessibility. Additionally, they will incorporate third-party and synthetic data into their data catalogs through enrichment services.
Data mesh, reverse ETL, and optimised LLM networks are the key technologies that will help with it. With a stronger focus on security, model explainability, and data traceability, emerging regulations will also guide the application of new AI and data analytics technologies.
