By Swapnil Mishra - March 06, 2023 6 Mins Read
Businesses are increasingly preferring data-driven decision-making to intuition-based decision-making, which likely explains why the market for data analytics is expanding.
The “cloud wars” have been succeeded by the “data wars.” Due to the ongoing economic headwinds, enterprises focus on using data to generate actionable insights, but leaders are becoming weary of making technology investments. The talent of today is being used to implement cloud investments.
While some businesses create entire business models around data, others routinely collect, store, and analyze enormous amounts of data to identify patterns, gain insights, forecast business outcomes, monitor consumer behavior, or enhance customer engagement.
Also Read: Reasons Why Self-Service Analytics Matters in 2023
According to Data-Driven Decision Making research by Gartner, businesses increasingly prefer data-driven decision-making to intuition-based decision-making, which explains why the market for data analytics is expanding at a compound annual rate of almost 30%. To accelerate their digital business ambitions, business leaders must consider how their organizations make decisions.
When an organization can benefit from the ability to make more integrated decisions that are better contextualized and operate continuously, digital aspirations can be scaled.
Data and analytics are crucial for enhancing this decision-making processes of executives and business roles, and this business competency helps scale digital ambition. Organizations will devote more time to data analytics in 2023.
Here are some of the top trends to look out for:
Enterprises must create resilient data environments, quicker delivery cycles, and superior employee experiences to respond quickly to market changes. CXOs claim that enhancing employee experience is their primary goal in investing in digital technologies.
The data-to-insights cycle can be accelerated, and resilient data environments can minimize data processing downtime. An improved employee experience is an outcome and, thus, increased revenue.
In 2023, one of the biggest challenges facing businesses will be figuring out how to use people rather than just technology to their advantage. IT leaders anticipate “data executives'” effective use will be a differentiator.
Organizations will need to promote disciplines like data ops, data mesh, and data culture. As a result, data executives’ operational efficiency will increase, and organizations will be able to keep their best employees and intellectual property.
Although it is now commonplace to transform text, graphic, and audio data into usable insights, video data has not made the same strides. The development of vision AI tools is becoming more straightforward thanks to edge technology and computer vision.
Real-world applications include helping senior citizens predict impending falls, law enforcement officials identify crimes before they happen, and professional athletes avoid accidents. Retailers have experimented with vision AI technology with success.
Vision AI helps medical equipment manufacturers, government organizations, and healthcare providers reduce human error and improve operational efficiency.
Most businesses find it challenging to analyze the data they gather. Almost 90% of data is unstructured or lacks an explicit schema. Business organizations can analyze this unstructured data more quickly and intelligently thanks to AI and machine learning (ML) technologies.
These technologies will also uncover hidden patterns and trends in structured data. When AI and ML are embedded in, or combined with, data analytics and business intelligence (BI) tools, businesses should be able to tackle even the most complex data types and find the hidden value in unstructured data at scale. With nearly 95% accuracy, AI/ML capabilities can already find and extract data from unstructured documents today.
It is easy to predict that AI tools will continue to develop and become more common in 2023. The impact of large language model applications (like ChatGPT) on the analytics market remains to be seen. Still, there have already been some exciting developments that make use of these models to produce SQL queries from natural language.
Utilizing first-party (consumer), second-party (supplier), and third-party (paid databases) data effectively has always been a challenge. Enterprises now have a complex problem to solve when they factor in data ethics (as they relate to demographic and socioeconomic factors), data laws (such as GDPR and CCPA), and data transparency (concerning data sources and treatment).
Companies in the consumer sector and supply chain companies stand to gain a lot from developing and using moral AI tools.
Organizations combine traditional data sources and contemporary capabilities as they integrate and automate disparate systems and use AI/ML technologies to analyze massive data sets. This is where the idea of data fabric comes into play.
Data fabric enables businesses to process and analyze information from disparate systems—such as on-premises, numerous clouds, social media, Internet of Things (IoT) devices, mobile applications, etc. under a standard set of objects. Analysts can comprehend data more thoroughly and meaningfully by adding metadata to the data fabric.
This entails giving data context so that it has meaning, comprehending how it relates to other types of data so that more comprehensive business insights can be gained, and, finally, making decisions or taking actions that maximize the value of the data.
Most businesses have invested significantly in digital systems, cloud platforms, and technology. Companies need to combine data lakes and warehouses to create a data lakehouse that brings higher speed, storage, and operational efficiencies to get a higher return on investment.
Real-time insights can be obtained when self-serve analytics tools are built on top of a data lakehouse. Redesigning the data architecture is necessary for this exercise, which is an operational task. Companies in the pharmaceutical, automotive, and consumer sectors that handle much data stand to gain from operationalizing their data lakes.
Also Read: Why Businesses Should Give Every Employee Access to Data Analytics
With AI and ML technologies, analytics should become more adaptive as it becomes more continuous and contextual. As a result, analytics should not concentrate on historical data but rather process data in real time, comprehend context, and modify its behavior as necessary.
The main advantage of adaptive analytics is that businesses can make highly accurate decisions based on real-time data. Data is continuously and the moment analyzed, so the system shouldn’t become dated or obsolete.
The end of pre-defined dashboards that satisfy the needs of all employees is one of the significant developments brought about by big data because employees in various departments may view data differently.
Employees will soon be able to build and interact with their data however they see fit to make the most of it. Non-technical people can increasingly create their dashboards and analyze a subset of data that makes sense to them using simple and creative tools.
Data is the new oil, but one needs a potent engine to extract, refine, and harness it efficiently. Businesses that establish a strong analytics foundation, culture, and competency will undoubtedly be able to innovate and make more informed decisions.
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Swapnil Mishra is a global news correspondent at OnDot Media, with over six years of experience in the field. Swapnil has established herself as a trusted voice in the industry, specializing in technology journalism encompassing enterprise tech. Having collaborated with various media outlets, she has honed her skills in writing about executive leadership, business strategy, industry insights, business technology, supply chain management, blockchain and data management. As a journalism graduate, Swapnil possesses a keen eye for editorial detail and a mastery of language, enabling her to deliver compelling and informative news stories. She has a keen eye for detail and a knack for breaking down complex technical concepts into easy-to-understand language.
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