WANT A BUSINESS FLARE? FOLLOW THESE TOP DATA ANALYTICS TRENDS
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In order to get the maximum out of technology, businesses are adopting data analytics trends
The power of data and analytics is no longer hidden. Today businesses of all sizes, starting from small to medium and big are availing data analytics in their routine to streamline operations. Without data analytics, companies are blind and deaf. Data analytics allows businesses to understand the market and their customers’ preferences and suggests solutions that could yield big profits. A rough estimation suggests that data analytics in business will increase five-fold by 2024 because of the rapid rise in technology adoption. Once upon a time, data analytics was confined to the tech industry. Only IT professionals, data engineers, and top-level enterprise executives got their hands on the technology. But things changed when laymen started embracing artificial intelligence. Today, big data, machine learning, cloud computing, data analytics, and many more technologies are a part of our everyday life. Many companies unveil data analytics in business to optimize business processes, cut costs, increase revenue, improve competitiveness, and accelerate innovation. In order to get the maximum out of technology, businesses should adopt recent data analytics trends. Data analytics trends such as decision intelligence, edge computing, data storytelling, etc are unraveling a world where businesses can understand their customers and address their needs like never before. In this article, Analytics Insight takes you through some of the top data analytics trends that businesses should follow in 2021.
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Top Data Analytics Trends for Business
Moving to Scalable AI
Post the Covid-19’s first and second wave, people’s preference has drastically changed. Businesses can no more use the historical data they have collected so far to optimize business decisions. Therefore, companies are moving to scalable and responsible AI that could pave the way for more data analytics and decision-making. Gartner predicts that 75% of enterprises will shift from piloting to operationalizing AI by 2024, driving a five times increase in streaming data and analytics infrastructure. Besides, healthcare and pharmaceutical companies are using scalable AI to expand their medical supplies and manage the supply chain.
Decision Intelligence as the Powerhouse of Decision Making
In modern times, many companies make decisions based on what machines suggest. Yes, we are already there. Artificial intelligence-powered machines are created by humans to analyze the overall performance of the company and its outcomes. Therefore, they have better knowledge than human employees in decision-making. Decision intelligence is a composite field containing artificial intelligence and data science along with some concepts of managerial science. It helps company executives and stakeholders pick the right choice based on reliable data.
Augmented Data Management to Shorten Data Delivery Time
The next goal for the business is to get data in real-time and acquire answers at the earliest. To move further with the motive, companies are adopting a new method called augmented data management. Organizations are now utilizing machine learning, data fabrics, and active metadata to connect, optimize and automate data management processes to shorten the time of data delivery. In the future, augmented data management will help companies reduce the delivery time by 30%. They can also convert metadata with the help of machine learning and artificial intelligence techniques from getting used in auditing, lineage, and reporting to powering dynamic systems. Considering its impacts, data analytics leaders are working on augmented data management to simplify and consolidate their architecture.
Edge Data and Analytics at the Core of Operations
The inflow of data has increased tenfold in recent years, thanks to the spiking adoption of IoT devices. However, businesses are in the positive end when it comes to benefiting from data. But a complex task here is their role to analyze the incoming data in real-time. Unfortunately, companies don’t have the leniency to decide on what data they want to be processed, instead, the concept has moved to how they are implying edge data analytics to come up with decisions rapidly. It also reduces data latency and enhances data processing speeds.
The Stronghold of the Cloud Continues
Initially, cloud architecture came into the business picture when companies moved from office spaces to the remote mode of working due to the pandemic. Although the pandemic is half gone and the world is preparing to get back to normal, cloud computing seems to have a stronghold on business operations. According to Gartner, public cloud services are expected to underpin 90% of all data analytics innovation by 2022. Besides, cloud data warehouses and data lakes have emerged as go-to data storage options for collating and processing massive volumes of data to run artificial intelligence and machine learning projects. Even research and development initiatives are moving to cloud methods to minimize cost and fast-track trials.
No more Big Data, Let’s go to Small and Wide Data
For almost two decades, big data was the center of attraction. Big data was vastly hailed for its nature to provide answers. Although it can’t perform alone, big data was often seen as the core of any decision-making process. Finally, businesses are moving from big data to small and wide data. The emerging trend in data is expected to solve a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases.
Automation at its Best
Business outcomes rely on data. But over the past few years, big data is getting more complex. For example, the inflow of data is in various forms like videos, images, documents, texts, files, etc. Besides, there are also two other categories called structured and unstructured data, which makes data processing even more hectic. The only way out of this is by automating the process of data discovery, preparation, and blending of disparate data. Besides, automating the data discovery and analysis process helps analysts focus on high-value-added activities.