{"id":4614,"date":"2024-02-23T14:04:03","date_gmt":"2024-02-23T14:04:03","guid":{"rendered":"https:\/\/www.biconnector.com\/blog\/?p=4614"},"modified":"2024-04-25T13:29:41","modified_gmt":"2024-04-25T13:29:41","slug":"top-data-analytics-trends-for-businesses","status":"publish","type":"post","link":"https:\/\/www.biconnector.com\/blog\/top-data-analytics-trends-for-businesses\/","title":{"rendered":"Top Data Analytics Trends for 2024"},"content":{"rendered":"\n

The year 2023 witnessed explosive growth of a technology that\u2019s touted to be the greatest of this decade\u2013 Generative AI. <\/p>\n\n\n\n

AI is everywhere now.<\/em> <\/p>\n\n\n\n

It\u2019s no longer a research subject or a topic for tech employees. Gen AI<\/a> has become a key strategy for businesses and tech leaders.\u00a0\u00a0<\/p>\n\n\n\n

As we step into 2024, Gen AI  is a definite factor changing the face of data analytics. <\/p>\n\n\n\n

However, there\u2019s more. <\/em><\/p>\n\n\n\n

Other transformative trends are bringing in dynamic shifts that will shape the future of analytics and insights<\/strong>. Understanding these trends, businesses can stay ahead of competition and drive innovation for sustainable growth.<\/p>\n\n\n\n

In this comprehensive blog, we explore top analytics trends for 2024<\/strong>, helping professionals and executives to stay abreast of advancements in the data-driven world. We will also look at the impact of generative AI in detail, careers opportunities in analytics, and innovations for future and beyond.\u00a0<\/p>\n\n\n\n

Top Data Analytics Trends for 2024\u00a0<\/h2>\n\n\n\n
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Source: Dylan Gillis \/ unsplash<\/em><\/p>\n\n\n\n

Data Fabric\u00a0<\/h3>\n\n\n\n

Data fabric<\/a> is a strategic approach that integrates and manages data across various systems<\/strong> to simplify access, enhance quality, and enforce governance, thereby enabling a unified view that facilitates better insights and decision-making.<\/p>\n\n\n\n

It has emerged as a pivotal trend to mitigate this complexity and integrate data across different systems, including on-premise, cloud, and hybrid environment and edge devices.<\/p>\n\n\n\n

For example, data across departments like HR or supply chain might be residing in separate data environments. Data fabric allows professionals to view this data in a single place and understand correlations. <\/p>\n\n\n\n

This unified architecture provides a holistic view<\/strong> of all data assets enabling organizations to break silos, improve data quality, embed governance, and enhance accessibility. Also, Data Fabric also helps organizations to share data and collaborate effectively to extract data-driven insights and improve decision-making.\u00a0<\/p>\n\n\n\n

Augmented Analytics <\/h3>\n\n\n\n

Augmented analytics is the use of machine learning, artificial intelligence, and natural language processing (NLP) to enhance data analytics<\/strong>. It helps organizations to automate various processes such as data preparation, data sharing, data discovery, and analysis. In simple terms, augmented analytics can do the work of a data scientist.<\/p>\n\n\n\n

With augmented analytics, AI\/ML platforms can interpret unstructured data.<\/strong> For instance, organizations can automatically monitor phone calls and document the interactions. This means saving the time of several employees listening to the calls and noting them down.\u00a0<\/p>\n\n\n\n

A major benefit of augmented analytics is that businesses of all sizes can use it without needing extensive data handling knowledge. This makes it great for businesses who want self-service analytics brought to their organization.\u00a0<\/p>\n\n\n\n

Data mesh <\/h3>\n\n\n\n

Data mesh <\/a>is a decentralized architectural approach that empowers specific business domains such as marketing, sales, and customer service to own and manage their data.<\/strong> It facilitates domain-focused data ownership and governance, enabling teams to maintain control over their data assets, enhancing accessibility, security, and scalability across the organization. <\/p>\n\n\n\n

Thanks to data mesh, teams can take ownership of their data and make data-driven decisions.\u00a0<\/p>\n\n\n\n

Based on the domain requirements, specific technologies and governance steps can be used. The ultimate goal is for different teams within an organization to innovate and experiment within an interoperable environment. <\/p>\n\n\n\n

An example would be the finance industry where data sharing is complex with inherent security and privacy risks. <\/p>\n\n\n\n

Teams will have to extract data from multiple sources to create reports. With data mesh, they can own data and have it in data lakes. Other teams in need can find it via data catalog and request access. It also helps them to trace where the data originated from.<\/p>\n\n\n\n

Data-as-a-Service<\/h3>\n\n\n\n

Not every business is capable of storing and analyzing data as MNCs can. This is where Data-as-a-Service (DaaS) comes in. <\/p>\n\n\n\n

DaaS is a cloud-based data management model where data collection, storage and analysis services are provided on a subscription or pay-per-use basis.<\/strong> Users can access a wide range of data from structured to unstructured, without physically storing data.\u00a0<\/p>\n\n\n\n

Snowflake is the most popular company in this market. Along with data warehousing services, it is also a DaaS provider. <\/p>\n\n\n\n

DaaS ensures high quality data in compliance with security standards and privacy regulations. Clients, especially small businesses, also benefit from the cost-effectiveness of this approach as they don\u2019t have to invest in expensive hardware and software infrastructure.<\/p>\n\n\n\n

Synthetic Data <\/h3>\n\n\n\n

Synthetic data is becoming increasingly valuable in the data analytics field. It is a type of data generated artificially. Synthetic data has statistical characteristics of real data but it’s not tied to actual data identifiers. <\/p>\n\n\n\n

To implement AI\/ML systems, businesses need large data sets for training models and sometimes they struggle to come up with high-quality datasets. Synthetic data can be easily generated and used for analysis. <\/p>\n\n\n\n

Synthetic data comes with a number of benefits:<\/p>\n\n\n\n