I was always a debater, so much so that I was a constant presence in intraschool debate clubs. I feel the activity of debating to be invigorating enough to open up new avenues of thought while truly liberating one’s soul. But wait, the topic is not about my debating experience, is it? However, this one small piece of information about me has everything to do with the topic. Coming back to the story, it was in one of those debating sessions that I heard this one retort, “Can you back your claims up with data?”. It hit me hard, and that introduced me to the world of data.
Data suddenly brings context to your claims, your decisions, and your thought processes. Imagine if a small bit of data can make much difference for one’s personal pursuits, what can data at scale do? It can propel growth, topple regimes, win wars, and more. Data is the only entity that empowers everyone with information to take decisions without bias. Or simply put, data is truth.
Given the irrefutable power data wields in the modern world, it’s a given that organizations will be heavily invested in utilizing it for furthering their objectives. Businesses have been doing that for the best part of the past century and there have been some interesting advancements in data analytics. Right from introducing the vast power of computing capabilities, to leveraging AI, the world of data and analytics has grown exponentially. So it’s quite natural that you as the reader would be interested in knowing the future state of data analytics. Experts suggest the following 4 mega data analytics trends one must watch out for in 2023.
The self-serve model albeit with its limitations continues to be the crowning achievement of data analytics. Self-serve analytics is one concept that truly moves towards democratizing data access. Ensuring everyone has access comes with its own fair share of challenges. But the difficulty does not mean it’s unachievable. Organizations across are looking to ensure the front-row employees feel empowered to take decisions as analytics become more pervasive in nature.
Data has penetrated across major business functions and the demand for data-driven insights keeps rising. This will challenge even the most seasoned leaders and data technologists to keep up with the pace. The advances in cloud computing, and real-time analytics platforms grow in tandem to meet these demands across the globe. However, the cost to set up and scale is a major point of concern for many organizations, and this may lead to more composability in designing data analytics systems.
The quality of insights is only as good as the quality of data being fed into the systems. To ensure the quality of data and to improve the trustworthiness of insights, organizations will try to invest in data governance systems and practices.
However, the end goal is not only to improve the trustworthiness of insights delivered but there is an additional factor also amongst the decision to implement data governance initiatives. It is to do with the tightening regulations in data collection, storage, and use by governments and regulatory bodies across the world. We can expect that tighter laws like GDPR will be passed and more organizations will turn to automated data governance tools to improve upon the trustworthiness on both the insight as well as the regulatory front.
Given the growth of democratized, pervasive, and composable data analytics, data fabric is required to act as the foundation of the entire operation. A data fabric offers unified access to data assets, databases, and database architecture within an enterprise. In simple terms, it helps in breaking data silos in a way that makes it more contextual and convenient.
Data fabric helps organizations process information from disjointed sources such as the cloud, on-premise sources, different smart devices, and more by unifying them in a set of objects. Data fabric helps users understand the different relationships between the data themselves. Contextually unifying data from disparate sources to serve the objectives of business functions (both vertical and horizontal) and feeding them into the analytics system helps users access momentary and meaningful insights.
Almost 90% of the data collected by organizations today are unstructured by nature with no defined schema. It can range from customer testimonials and other forms of textual data to images, videos, and other formats. Artificial Intelligence coupled with machine learning augments organizations with the capability to analyze these data in a faster and better way.
Embedding AI within modern BI tools will also help them find hidden trends in structured data as well. IDC suggests that today’s AI/ML solutions have the potential to locate and extract data at 95% accuracy levels and more. However, whether solutions with large language models like ChatGPT for instance can make a more lasting impact on data analytics is given to be seen.
Quoting a Forbes article, “Data is the new oil, but one needs a powerful engine to extract, refine and harness it efficiently.” Organizations across the globe understand the role data plays and will play for time immemmorial in business decisions. However, there are obvious gaps in ensuring access to everyone. The trends outlined clearly point to a future where data is contextual, available, unified, and easy to consume. This is achievable through a simple switch. Using language as an interface to access insights, more commonly called conversational insights.
If you would like to see conversational insights in action, head over to our webinar where we show the power of simplified insights with real-world use cases.
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