Introduction
Since the COVID-19 pandemic crisis, businesses across the globe decided to make a digital transition. In the quest for digital transformation, there are various tech innovations that can improve and evolve the use cases of data analytics for good.
Data analytics paired with AI and other technologies have made it easier to prepare, respond to, and predict data. In the event of a crisis, businesses can now be more proactive and maintain efficient and cost-effective operations. On the surface, it may seem like two or three tech innovations. But there is a long list of tech innovations that can fuel data analytics in the coming years.
Keeping that in mind, here are the essential tech innovations that have the long-term potential to transform and improve data analytics for years to come.
- Cloud Services
As mentioned earlier, the public cloud has had an all-time high adoption. In short, the innovation of data analytics now ties together with the public cloud. You can expect more than 90% of data analytics to jump to the cloud. But the burden of responsibility now lies with data analytics leaders to ensure a flawless transition to the cloud and align data flawlessly.
Moving to the cloud means enterprises don’t have to deal with integration overhead and extraneous governance. As the public cloud evolves, data analytics leaders will be able to meet the performance requirements of the workload beyond traditional standards.
With the public cloud, data analytics leaders can better prioritize their workloads and make the most out of cloud capabilities. Moving to a dedicated cloud is a perfect approach to ensure cost optimization, accelerate operations, and drive innovation at the same time.
- Visual Dashboards
Gone are the days of traditional dashboards that used to make data look far more complex to analyze. Today, analysts use personalized and automated visual dashboards with point-and-click features. In visual dashboards, the exploration and authorization are easier.
With more visual-based dashboards, the decline in traditional data dashboards is inevitable. Besides, visual dashboards allow analysts to paint a clear picture of the data, contextualize complex data, and spot interconnected patterns. Enterprises now want to leverage technologies like augmented analytics, streaming anomaly detection, and natural language processing.
With dashboards also create more dynamic insights and allow companies to integrate new technologies. In short, visual dashboards have made data analytics more context-driven and will pave the way for more dynamic and accurate insights.
- More Practical and Responsive AI
Ai has become more responsive, faster, and smarter in the last few years. In fact, multiple reports suggest that over 70% of organizations will use AI technology to move their operations digitally by 2024. Furthermore, AI has had a significant impact on analytics infrastructure and streaming data.
AI subset like machine learning optimizes data analytics algorithms through natural language processing. These tech innovations provide accurate predictions and valuable insights that allow businesses to make countermeasures and ensure effective operations.
You can expect AI and ML to realign more data analytics processes and become more grounded. This will further make it easier for analysts to identify key patterns and establish connections across datasets. More AI and ML models will become highly transparent that will minimize poor business decision-making.
- X Analytics
X refers to the unstructured and structured content analysis in the form of video analytics and audio analytics. This type of data is at the forefront in the digital age and businesses want to reap its benefits. X analytics has the potential to solve some of the most complex challenges of the world like wildfire protection, disease, disease prevention, and climate change.
After the pandemic crisis, experts profess that X analytics can contextualize hundreds and thousands of social media posts, research papers, and news sources. Similarly, healthcare institutes can use X analytics to predict capacity plans, disease spread, and figure out new treatments. X analytics, however, have to be paired with technology like AI to determine, plan and predict natural diseases for businesses and how to turn crises into opportunities.
- Smart Intelligence
One of the tech innovations that large corporations use is to develop and improve their decision modeling is smart intelligence. In fact, analytics now practice decision-based intelligence that involves decision modeling. To make things simpler – decision or smart intelligence decision support and decision management.
Ultimately, these two aspects simplify complex applications and combine advanced and traditional disciplines. Smart intelligence is all about focusing on the “right” framework so that data analytics heads can create, design, model, monitor, execute, align, and execute processes and models in line with the business outcomes. It gives enterprises the chance to leverage decision modeling technology through mathematical tactics, logical sequencing, and automation.
- Augmented Data Management
Augmented data management is another tech innovation in the data analytics space. It uses a combination of AI and ML tactics to optimize operations. Also, augmented data management converts metadata for reporting and auditing purposes.
With augmented data management, you can examine an entire product and large samples of data. These samples can include performance data, queries, and schemas. Once you take into account workload data and current usage, augmented data management tunes overall operations.
It also optimizes security, performance, and configuration parameters. Analytics leaders can use augmented data management to simplify and compress the architecture of active metadata in the system. When it comes to conventional data management tasks, data analytics leaders can leverage augmented data management to increase the level of automation.
Final Thoughts
Ultimately, it dawned on companies the post-pandemic requires a reset and a swift way to handle data. In a traditional sense, businesses had no choice but to store extensive data in physical data warehouses. The major pitfall of this approach is that it incurs mountainous costs and offers redundant security at the same time.
Since the advent of hybrid cloud solutions, there is an innovation of 90% in the data analytics field. After all, cloud technology offers more flexibility, scalability, and efficiency to handle day-to-day business operations. With cloud-based data analytics alone, businesses can ensure agile operations.
Explore IDA further and find out how IDA’s data analytics platform is designed to stay on top of industry innovations.
REFERENCES:
- https://www.hico-group.com/data-analytics-future-trends/
- https://techbullion.com/what-are-technology-trends-going-to-shape-the-future-of-data-analytics/
- https://www.forbes.com/sites/forbestechcouncil/2021/05/11/data-days-current-and-future-trends-in-ai-and-analytics/
- https://www.gartner.com/smarterwithgartner/gartner-top-10-data-and-analytics-trends-for-2021
- https://www.linkedin.com/pulse/top-5-big-data-analytics-trends-predictions-2022-arsr-technologies?trk=organization-update-content_share-article
