The developments in data analytics, including AI-driven analytics, that we will 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 over many years of development. This has become more important 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 what’s in store for data analytics in 2024.
1. Predictive Analytics
Predictive analytics will gain prominence in 2024. It has been talked about for many years, but is often hampered by the limitations of data and software, both of which are in decline.
Predictive analytics creates forecasts based on historical data and patterns to identify future trends and events. Businesses use predictive analytics to optimize operations, anticipate customer demands, and make data-driven investment decisions. This is a game changer for staying ahead in competitive markets.
2. Generative AI
In 2023, Open AI and internet giants have generated huge buzz around the use of generative AI in many business functions.
Generative AI creates new content and data in response to natural language text queries. Generative AI relies on a vast store of coded information stored in a large language model (LLM) to respond to queries.
In 2024, more and more organizations will use generative AI for data analytics to solve the thorny problem of extracting value and insights from unstructured data. Problems include:
- Data analysis tools have always been designed to query only structured data.
- The use of search tools was hampered by difficulties accessing various PDF databases and by inconsistent metadata.
- Data analysis tools cannot query images, audio, and video.
- A significant portion of unstructured data is still stored on paper.
Because generative AI is trained on both structured and unstructured data, it can answer queries by evaluating an amalgam of the two.
3. Data Analytics vs. Data Science
The line between data analytics and data science will continue to become even blurrier in 2024. Data analytics will become more sophisticated and data science will become easier to understand. Both trends are the result of more sophisticated software that successfully hides complexity to make developers and data analysts more competent and productive.
4. Data lakes are a dead end
In 2024, more and more organizations will realize that data lakes provide no value. Data lakes are a concept that relieves IT departments of having to deliver impossible data analytics applications that are more complex and more expensive than project sponsors can afford or want to pay. IT departments are tired of explaining project difficulties and being perceived as incompetent, lazy or tinkering.
With a data lake, IT can tell management and end users that “we’ve gathered all the data you need for easy access.” Our work is done. Please use the excellent end-user tools we have installed to quickly create the data analysis deliverables you need.
By feeding the data lake without any data transformation or integration, IT has cleverly avoided responsibility for any problematic issues that lead to failed data analytics projects. These include:
- Data quality defects that result in unreliable reports and graphs.
- Inability to integrate incompatible data structures.
- Missing data.
- Difficulty integrating internal data with external data.
- Insufficient end-user expertise to describe complex requirements.
- Project cost and schedule overruns.
Organizations facing a worthless data lake should upgrade it to a data lake as shown below.
5. Data lakes
In 2024, more and more organizations will recognize that data lakehouses are a practical compromise between low-value data lakes and expensive data warehouses.
Data Lakehouses combine the low operating cost of data lakes with the data management and structure features of data warehouses on a single platform. The difference between data lakehouses and data warehouses lies in the manner and degree of data integration that has been developed.
In data lakehouses, data integration is only developed for the small number of data sources where views, queries, reports and dashboards are generated. In data warehouses, all data from various data sources is transformed and loaded into the schema, encompassing data integration.
In data lakehouses, data integration happens slowly and partially as needed, at low cost. The business value is quickly and frequently evident. In data warehouses, all data transformations, loadings, and integrations are significant upfront costs incurred before any value is provided.
6. Data Warehouses
In 2024, no organization will start building a data warehouse. The era of data warehouses is over. Beautifully structured and organized data warehouses are too expensive to build and operate.
Instead, organizations:
- Leverage advanced data integration tools to create the illusion of superior data integration even though the underlying data remains in its original data store.
- Build and leverage data lakes.
7. Ethical considerations
In 2024, as data analysis continues to become more prevalent, privacy concerns and ethical considerations will become more important. Data breaches and misuse of personal information will lead to an increased focus on responsible data processing and compliance with regulations and ethical guidelines. The goal is to protect privacy and protect organizations from lawsuits.
8. Graphical analysis
In 2024, the increasing use of graph databases will lead to increased use of graph analysis.
A graph database is a NoSQL database that stores nodes and relationships instead of tables or documents. Unlike a relational database, a graph database stores relationships explicitly. This feature significantly speeds up the performance of complex queries.
Graph analysis is superior for discovering hidden patterns, making predictions, and gaining insights into complex data structures.
9. Democratization of data
Data democratization aims to make data and analytics accessible to a wider audience within an organization. This is not a new concept. It’s a gleam in the eyes of software marketers who want to license more software and ambitious end users who want to develop their analytical queries without the help of a developer.
In 2024, more data analysis tools and platforms will offer a user-friendly interface and more sophisticated data manipulation features, allowing non-technical end users to access and analyze data independently.
While the data analysis tools are impressive, barriers to broader adoption include:
- Lack of a sufficient number of predefined views of available data stores to simplify query development.
- Insufficient end-user understanding of data structures and usage of available datastore columns.
- Insufficient end-user proficiency in using available data analysis tools.
Sometimes, organizations claim to offer self-service analytics solutions without addressing these barriers.
10. Data Observability
Data observability improves the ability of organizations to monitor, track and ensure the quality, reliability and performance of data throughout its lifecycle. Performing these tasks has always been a daunting task that has led to inconsistent results.
In 2024, as organizations rely more on data analytics to move toward data-driven decision-making, access to high-quality data has become essential. More and more organizations will implement comprehensive data observability which includes:
- Monitor data quality and make corrections.
- Understand data tracing and ensure traceability.
- Perform data governance and metadata management.
- Support continuous improvement.
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