Ten Analytics Trends to Harness in 2023

Ten Analytics Trends to Harness in 2023

  Jul 10, 2023 15:35:00  |    Joseph C V   #AI #analytics #trends2023

                  Data is the collection of information gathered in various forms and today it is being generated by the second. Such raw data need to be cleaned up, compiled, and gathered to make meaningful interpretations. Data Analytics is the process of inferring meaningful information from such raw data.

Analytics has been a pioneer in radicalizing the way businesses are done. It can rightly be called the game-changer today. It is no longer about gut instincts or hunches that drive businesses. Driven purely by data raised in momentum, analytics has the power to make informed decisions that are more than just on a whim or a sly.

Analytics is truly a disruptor capable of taking business data to intelligent levels. It is storytelling in a more visual way driven by intelligently drawn insights. No longer is it about how many companies are drawing their decision from analytics, the real question here is if companies have a choice when it comes to being data-driven.

Several trends have emerged in the recent past, that have contributed to this growing field. These trends only go to substantiate the fact that Analytics is a field here to stay and grow. Whether they are combing the potent power of Analytics, Artificial Intelligence, and Machine Learning to innovate offerings. Some of the trends to watch out for in the year 2023 are:

  • Artificial Intelligence

Placed clearly at the top of the drawing board, Artificial Intelligence, or AI as it is fondly called has opened a new spectrum to every stream.  AI has been the buzzword for quite some time now. AI has recalibrated business systems and the way they function in recent times. Cleary touted the winner on every metric, AI rises to the occasion to supplement complex human intelligence. Add to it, the ability to scale up in many required situations, AI is a worthy adversary to human intellect.

  • Data Security

While we are dealing with data at such high levels, security concerns are bound to rise. There is a constant threat of vulnerabilities, data breaches, data theft, malware, and PII (Personally Identifiable Information) being stolen when we deal with data on such a scale. On that note, It is pertinent for businesses to make data compliant with Information Security Management Systems (ISMS), Personal Information Management Systems (PIMS), and HIPAA. Cybersecurity is another issue that arises with data being stored in cloud stances. At the risk of data leaks and breaches, measures need to be adopted to keep the data intact and safe.

  • Cloud Data & Data as a Service (DaaS)

Data available on the cloud or DaaS has led to tremendous data transfer and sharing growth. It has also led to seamless data-sharing mechanisms. Companies no longer need to store and manage data. Here analysis tools are also provided along with the cloud data. They work on a pay-as-you-use method or subscription mechanism. But they also pose security risks while transferring data from the cloud to other instances. There is the likelihood of data being prone to attacks on certain devices and exposed to attacks. There is a major financial implication in this decision that needs to outweigh adopting the cloud model.

  • Data Governance

Data is the most lucrative business proposition available to companies, there are issues concerning its usage and storage, and access. Laws that help the use of data while not infringing on personal rights need to be promulgated. While certain European and other countries have made strides in this direction, there still exist gaps in its implementation around the globe. Stronger processes need to be in place for the usage and manipulation of sensitive data. Governance enables compliance with set standards that can mitigate risks.

  • Edge Computing

In a day and age where data is wealth, it is pertinent that the right amount of data is employed for decision purposes. It’s not just any data, it’s the right data, coherent and complete that can provide actionable insights that make it the decisive element. This provides much-needed flexibility, scalability, and reliability in the data processing.  IoT devices coupled with Edge computing can provide speed, agility, and flexibility as computing and data storage moves closer to their source.

  • Natural Language Processing (NLP)

NLP or its various forms like sentiment analysis, the syntactic or semantic analysis goes a step further to extract and interpret human language in written or spoken form. Whether they be in the form of the car navigation system, voice assistants, AI-enabled chatbots, or auto-complete text features all go to interpret natural languages to silos of extractable information.

  • Data Democratization

Empowering all levels of employees to interact with data regardless of their technical expertise allows for bidirectional information flow and better insights. AI embedded into the systems allows for the democratization of data and armed with the right analysis tools allows the most lay person to provide the much-needed foresight.

  • Internet of Things (IoT)

Data collection at source adds to the growth of real-time data. Armed with software, sensors, and the latest technology, IOT enables data gathering among multiple devices across the network. Analytics allows us to leverage the information gathered in real-time by this technology whether they be wearable devices, sensors, connected electromechanical systems, or handheld equipment.

  • Data Visualization & Collaborative Business Intelligence

Data Visualization provides complex data in bite-sized chunks of information in a visually appealing manner. Visualization helps consolidate all the required information into dashboards that are intuitively designed to engage the reader to spot trends and reveal perceptive information. Collaborative BI is a combination of tools, social media, and other online Business Intelligence tools.

  • Predictive and Prescriptive Analysis

While we endeavour to see into the future, predictive and prescriptive analytics help businesses get a glimpse into what can be the likelihood of events. Predictive analytics can be used to spot patterns in consumer behaviour and make likely assumptions about the future. It helps businesses upsell and cross-sell products based on insights. Prescriptive analysis on the other hand sets up a graphical and simulative expression of the course of events and how it shapes out.  

Although we sum up most of these trends under the above heads, there is a lot more lurking in their shadow, whether they be Augmented Analytics, Blockchain, Embedded Analytics, and more. Truly we have come to an era to sit back and relax and let the data do all the talking. It’s time to employ the compelling power of well-informed wisdom to have the last say.


“Information is the oil of the 21st century, and Analytics is the combustion engine.” - Peter Sondergaard