Along with mobile, social media and cloud, Data Analytics like technologies have also earned a strong place in this Digital age. Everything nowadays is captured: Your smartphone service providers may track your location and who you call, your car can track where it goes, your PCs captures which ads you click on, your FitBit captures how active you are, etc.
Data Analytics and Business Intelligence is focused on the future and answers to the questions such as-
– What will happen in the future?
– How can we make it happen?
– How can we avoid unwanted situations?
Some of the trends that can be seen in Data Analytics and BI are as follows:
Analytics of things
Analytics of things is next buzzword after the popularity of Internet of things(IoT). Internet of things (IoT) generates a massive amount of data which Analytics of things (AoT) analyzes to make a decision relevant to business. Analytics are decisive to make connected devices smart and to make perform intelligent actions. Analytics of IoT devices makes them more efficient. AoT analyzes the huge data generated by IoT and only by analyzing the data becomes meaningful and not by collecting them.
However, IoT itself is evolving and AoT is at an incipient stage. One of the major challenges that AoT faces is Data Storage issues of real-time data that IoT generates. The data generated by each sensor is sizable and managing such huge data is a difficult task. Two major challenges faced by businesses are avoiding junk data and ensuring data privacy. It is vital to protect the data generated from devices, especially at confidential places.
Combining and developing human and machine intelligence can disrupt decision-making procedure. This can mean two different things: First, “smart machines” can replace humans. For example, Artificial Intelligence may replace doctors, marketers, lawyers, etc. Secondly, Artificial Intelligence will help humans in making better decisions with a reduction in workload and human errors. With the rise of Cognitive analytics, we can automate analytical thinking through machine learning. Cognitive analytics is not a replacement for the traditional analytics programs but a better version giving improved knowledge-intensive undertaking.
Evolution of Self-service BI
Self-service BI tools are successful for many companies. Business professionals are benefited from being empowered to explore new data sets without much IT support. Visual data discovery tools have become interchangeable with self-service BI and are getting popular largely. With Visual data discovery you arrive at unexpected data insights which reduce errors while makings business decisions. BI as a service provides a solution to its users for 3 critical functionalities: extracting and desegregating data from huge databases, organizing it into a high-performance data warehouse, and giving business users the capability of accessing and processing this data via purpose-built interfaces and apps.
Because self-service BI is used for the people who may not be tech-savvy, it is imperative that the user interface of BI software be simple with a dashboard and navigation that is user-friendly. Self-service BI offers an environment in which Business professionals can create and access different sets of BI reports, queries, and analytics themselves without IT interruption. This approach increases the reach of BI applications to cater to a vast range of business issues and demands.
Recently cyber-attacks are creating a lot of hype. Using Analytics, businesses can begin to proactively identify possible threats and enable timely detection and mitigation of such attacks. Big data security analytics helps the enterprises to scrutinize through a hefty amount of data generated inside and outside the organization to unwrap the hidden relationships, detect patterns and remove security threats. Privacy policies are enhanced with security analytics. Analyzing data from the Internet, smart devices, and social media can help law enforcement detect criminal threats better and collect evidence. Instead of waiting for a cyber-attack, organizations can address it proactively. Innovations in appliances, security software and services have automated many detection and blocked tasks, resulting in improved protection from unwanted cyber-attacks and intrusion-prevention systems.
Rise of Open Source
R, Hadoop and Python continues their march into the mainstream of enterprise-scale data science. Open source tools were not really accepted initially and were considered risky but are now appreciated for bringing tangible value. Coupling of open source and data science is a trend now which is helping marketers to better understand their target market behavior. Database and analytical software vendors will likely incorporate open source functionality with their products. For example, many commercial databases and statistical computing platforms are now supporting the integration of R programming. Open source could enable new modes of collaboration and innovation.
With the data and apps en-routed towards cloud Analytics and Business Intelligence can’t be far behind. The presence of cloud everywhere is nothing new for those who stays up-to-date with Business Intelligence trends. With time fear of moving crucial business data online is reducing and entrepreneurs learned how to embrace the power of cloud analytics, migrating most of the elements- data sources, data models, processing applications, computing power and data storage to the cloud. Some of the examples of cloud analytics products and services include hosted data warehouses, SaaS Business Intelligence tools, and Cloud-based social media analytics.
As we look into the future powered by Analytics, Cloud computing, Business Intelligence, driver-less cars, and emotionally aware robots, real life technology is rivaling and even outweighing everything we’ve seen in science fiction. It’s thrilling to watch how these trends will play around and change the way we work and live.