The world is becoming more digital, ever increasing velocity and scale of data volumes. Each day humans and machines together product 2.5 quintillion bytes of data. The pace of data generation is incessantly growing as well. Over 90% of the world’s data produced by now was generated in the last two years. This means that the flow of data coming in different formats and largely unstructured is bound to further multiply, making it more complicated for people to understand the information and drive value out of it.
The use of data analytics tools is an effective way to deal with masses of incoming data. The availability of this type of software enables businesses to handle data-related processes quickly and effectively. Data analytics solutions are an integral part of many business models nowadays as the ability to process data quickly can be an unfair advantage for lots of businesses.
Let’s have a look at some of the latest trends in data analytics that will shape the global market in the coming years.
Top 10 Data Analytics Trends to 2025
1. Commercial AI & ML
Gartner predicts that by 2024, 75% of enterprises will operationalize AI and ML for business purposes, driving 5X increase in streaming data and analytics infrastructures. The complexity is that AI techniques massively used in production and business cannot be easily adopted, but only through integration of streaming data environments. The new approach to AI and ML is to learn from experience rather than building on historical data. For this reason, traditional techniques are no longer relevant and workable. Embedded learning and reinforcement learning are now employed to solve complex business situations and allow for scaling a business faster and with less risk as interpretable ML and AI systems let you control risks, detect bias and protect yourself against wrong decisions.
2. Decision Intelligence
The next discipline that is among the current data analytics trends is decision intelligence. It is a blending of ML techniques, optimization and operations research techniques, and BPM techniques. This framework uses complex decision algorithms that are a result of human and machine intelligence collaboration. The purpose of decision intelligence is to streamline decision-making by reducing the time needed to implement business logic as well as modifying it without disrupting other business processes. Companies that start employing decision intelligence modeling are likely to benefit from faster time-to-solution and reach higher ROI without undertaking new risks.
3. X Analytics
This trend can be defined as a type of AI analysis that is meant to process different types of unstructured and structured content at once. In fact, X Analytics is capable of analyzing text, video, and audio content at the same time with a view to detecting the common patterns in it. The anticipated rapid growth of X analytics derives from the fact that modern AI tools strive to cover a wider array of information sources to consider up-to-date changes rather than just being focused on historical data solely. The biggest benefit of this technology for the organizations is that it can ensure full coverage of diversified data points and consequently provide a vision of the whole picture.
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Another prediction from Gartner’s data science trends report is that the use of augmented data management practices will free up to 20% of time IT specialists usually spend doing repetitive work. The demand for data analysis automation and metadata analytics is explained by entrepreneurs’ interest in data insights. Now it becomes more important what value you can elicit from data rather than the manner data is stored and organized. For this reason, data integration solutions are intended to help humans process a continuous flow of data, creating a sustainable analytical environment where analysts and IT professionals can make data-driven decisions to fuel growth.
5. Cloud
Cloud data solutions is one of the essential analytics trends that allow organizations to move and manage data in the cloud. This approach to data management provides flexibility and agility in dealing with large data volumes. As a result, a cloud-based enterprise has a cloud data ecosystem that helps to achieve optimization in business processes and reduce data costs. However, cloud should be approached with caution, as some companies are faced with data silos when data is integrated into the cloud in parts and at different pace, for example, with multicloud or intercloud deployment.
6. Data Marketplaces and Exchanges
Out of all the analytics trends, this prediction touches upon major changes to data governance. The interest in data and analytics platforms is steadily growing these days. The use of analytics for better decision-making in business is a necessity. However, the combination of augmented analytics and BI will allow for fast transition from data collection to insights generated within data management platforms without need to involve third-party tech specialists in the process of data integration, profiling, or cataloguing. Data marketplaces are expected to give more businesses a chance to achieve agility in their business processes by ensuring a direct link between data and action.
7. DataOps
This innovative data analytics trend is already bringing order to the process of managing big data in modern organizations. The main idea of this data management system is to take the core points of DevOps, Agile development, and Statistical Process Control and use their strengths accordingly to amplify the speed and accuracy of data analytics. As a result, it improves code verification significantly and arranges and controls data libraries to create and provide advanced analytics. With its help, businesses get an opportunity to better monitor their data, measure all metrics in real-time, identify and fix bugs faster, as well as respond timely to potential problems to maintain data integrity.
8. Data-as-a-Service
By 2022, 35% of big enterprises will be involved with selling or buying data through online data marketplaces. Data as a Service (DaaS) model is among the important data analytics trends for companies facing difficulties in complex data analysis. It allows a company to have its data compiled and processed for a specific business purpose as a result of which it receives insights into an area being under complex analysis. This approach is fit for enterprises that seek advanced data-centric decisions and enhanced risk control.
9. Data visualization
This phenomenon in the latest data analytics trends is a manifestation of data democratization that is aimed to make data more accessible and intelligible to the masses. In particular, the trend for data visualization is of much value within organizations that have to transfer tech data to non-tech workers. Data patterns visualized in images or charts simplify the process of understanding analytics insights and make it easier for the company to apply the value driven from data analysis.
10. NLP/conversational analytics
It is the last one from data analytics trends that is tied to text and voice search processing. The need for conversational analytics arises from the growing use of chatbots and other conversational-based interfaces that have to understand the user’s queries not only at a semantic level but also in the context of user behavior. The ability to gather and analyze real-time context data is the reason why NLP is gaining new importance in businesses relying on AI-driven assistants and bots in their sales and marketing channels.
For further information
Deploying emerging technologies seems to be the best way to respond to a crisis. If you are interested in analytics trends because you are searching for smart digital solutions that will help you become a key player in the market, contact Computools’s expert team via info@computools.com to know what data analytics software tool is best-suited for your enterprise with regard to your business value and goals.
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