The analytics trends that will change how you use customer databy David Hunt Data Geek and Analytics Fanatic
Data is the new oil. It has become the engine of transformation and innovation for enterprises and still continues to accelerate and evolve, bringing in more innovation and more sophisticated approaches to big data analytics than ever before.
Here are some of the trends that will transform how businesses use data in the near future.
The advent of augmented analysis
Augmented analytics refers to the use of machine learning and AI to understand complex patterns across data sets and user behavior to help discover critical insights to aid business intelligence. Causal inference, which is a more recent development, is the next big thing in analytics. Causal inference will utilize advanced statistical methods to isolate the most likely causes for particular user behavior. For instance, people who frequently write product reviews buy more online than people who do not write reviews.
Greater focus on localized data strategies
The significance of localized data governance and strategy will be more in the new age. Geographic relevance to data privacy is becoming increasingly important. While multi-national privacy regulations such as GDPR or California Consumer Privacy Act (CCPA) make headlines, smaller, regional laws must not be overlooked.
In the new decade, multi-national enterprises will be compelled to adopt regional data strategies which have often been overlooked. With this set-up, personal information will be stored within a specific geography where that data is processed in accordance with the local laws, customs, and expectations.
True artificial intelligence is far from reality; however, machine learning can help people make smarter decisions in analytics today. For instance, machine learning algorithms can monitor all of your business metrics and raise an alert if there is any change from the parameters that are set. Machine learning can also detect anomalies in businesses and help identify the root cause and help sort things out.
Looking further, AI can augment human intelligence and might be widely used for visual recognition and natural language processing, which is the ability to understand human language.
One contemporary application of the above is sentiment analysis which can help us judge the emotions based on a speech or text. Companies are already using sentiment analysis in social media to gauge emotions out of a post or speech.
Machine learning combined with AI has created a new paradigm of prediction called predictive analytics. It is the prediction of patterns based on historical data thus predicting an outcome. Many enterprises are already using predictive data analytics to reduce their losses and increase their business efficiency.
Stronger demand for IoT analytics
With the rise in IoT devices, more complex data sets are created and the need for analytics of this data is even more. This new environment demands tools and skill sets that most companies don’t yet have, and they often have difficulty adapting without IoT analytics.
In B2B, companies will use IoT analytics to mine data with the permission of their users. This includes data from mobile apps, fitness trackers, mobile devices, vehicles, and appliances. This will be used to create behavioral models thus enabling greater customer experience.
In the B2B space, IoT analytics will help drive greater productivity, reduce errors, improve understanding of smart city infrastructure and drive efficiency improvements.
Created on Jun 27th 2020 04:26. Viewed 379 times.