Stay ahead of the game with the latest predictions for data analytics
Data analytics developments, including AI-powered analytics, that we’ll see in 2024 offer significant benefits for expanding our knowledge and making better data-driven decisions.
Data analytics examines, cleans, transforms, and interprets data to uncover valuable insights to advance the organization’s business plan.
Data analytics is a technology that has become more capable during many years of development. It’s become more prominent in recent years as organizations have embarked on digital transformation. One of the benefits of digital transformation is the creation of more digital data to analyze.
Here’s more of what’s in store for data analytics in 2024. To read the previous 10 predictions, click here.
- Data storytelling
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In 2024, compelling data storytelling will become increasingly important as organizations:
- Strive to make better data-driven decisions.
- Face more complex decisions that are difficult to communicate well.
- Operate in a more competitive environment and a more demanding social landscape.
Data storytelling is communicating data insights and related recommendations concisely and engagingly.
- Advanced data visualization
Data visualization makes data more engaging and informative. By comparison, spreadsheet data is tedious and challenging to interpret. The software has improved gradually over time.
In 2024, we can expect continuing ease-of-use advances in data visualization functionality.
- Stream processing
Until recently, many organizations have been unable to process the vast amounts of data produced by IoT devices in plants and customer mobile devices in real-time. The likely outcomes were misleading analysis, no longer relevant recommendations, and unused data.
In 2024, with new solutions, real-time stream processing and analytics are now possible.
- Augmented analytics
Augmented analytics leverages the power of machine learning and AI to improve data analysis. In 2024, end-users will start to use natural language processing (NLP) to define queries. This capability will further simplify extracting information from data sources, making end-users more productive.
- Data orchestration
The widely used file storage infrastructure has not kept pace with increasing data volumes and advances in data analytics. Many solutions try to manage storage silos and distributed environments by moving file copies from one place to another and losing control over versions and data integrity.
Expect to hear more about data orchestration in 2024 because it helps organizations integrate data into a single namespace from different silos and locations without losing control. It automates data placement when and where it’s most valuable, making it easier to analyze and derive insights.
- Object stores
There are definite challenges with maintaining application performance when tens of petabytes of data must be accessed and processed for various applications, including data analytics. Traditional SAN/NAS data management solutions cannot solve those performance challenges.
Maintaining performance requires the characteristics of modern, highly performant object stores. More demanding performance requirements in 2024 are causing:
- AI/ML applications to use object stores.
- Most databases (DBMSs) to become more object storage-centric.
- SQL vs. NoSQL
In 2023, the NoSQL community finally acknowledged enterprise IT demands standards and quit talking about doing away with SQL. The NoSQL community recognized:
- The value and simplicity of SQL as a standard and powerful query language for data analytics and many other applications.
- Their inability to achieve consensus on another standard syntax was not helping their alternative goal.
In 2024, every NoSQL database vendor is adding an SQL or SQL-like interface to their product to appeal to enterprises. NoSQL databases manage the following data structures:
- Key-value pair.
- Document-oriented.
- Column-oriented.
- Graph-based.
- Time series.
- Data fabric
In 2024, more mid-to-large organizations will build out their data fabric to improve data reliability, access and integration for data analytics.
A data fabric is an end-to-end data management solution for an organization. It consists of these components:
- A defined data architecture.
- Selected data management, integration, analytics and visualization software.
- A unified, consistent end-user experience.
- A priority on sharing data to minimize data anomalies and duplication.
- Real-time data access where necessary.
- Vector databases
Expect to hear more about vector databases in 2024. Vector databases are designed to efficiently process large volumes of data with associated semantic content. Vector databases are advantageous when data analysis applications involve large language models (LLMs), generative AI, or semantic search.
A vector database is designed to efficiently store, index, manage and retrieve massive quantities of high-dimension vector data. Vector databases are particularly suited to conduct approximate nearest neighbour (ANN) searches rather than exact match searches. Vector data consists of mathematical representations of features or attributes. Each vector is defined with a certain number of dimensions ranging from tens to thousands. The complexity and granularity of the available data determine the number of vectors. Dimensions represent patterns, relationships, and underlying data structures that enhance understanding of the data’s semantic content.
- Single-pane-of-glass
In 2024, advanced data integration tools will extend their functionality to create the illusion of integration beyond IT-managed structured data. Single-pane-of-glass integration will include IIoT, geospatial, partner, and external data.
Yogi Schulz has over 40 years of information technology experience in various industries. Yogi works extensively in the petroleum industry. He manages projects that arise from changes in business requirements, the need to leverage technology opportunities, and mergers. His specialties include IT strategy, web strategy and project management.
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