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4 Data Analytics Trends that are Fading and Why



Data Analytics Trends

Data has been coined by popular business magazines and forums as the “New Oil”. It is rapidly becoming the leading growth-fueled sector in the tech industry. Companies focusing on Data Science and Data Analytics are spearheading the Data movement with new innovations. With new developments in techniques and technology, there are bound to be casualties. These are the outdated systems, old practices, and even down-trending terminologies people who work with Data are avoiding.

We have listed downtrends in Data Analytics that have begun to disappear. We will also discuss the reasons why these trends are waning out and enumerate alternatives if there are any. Let’s start with the least obvious one: on the topic of Data job titles:


#1 Legacy, on-premises systems are switching to Hybrid systems


Inertia is a concept in physics that states that the more massive an object, the more resistant it is to act. The same concept is seemingly applicable to large companies that have legacy platforms that use in-premises systems. It’s very difficult to convince them, if not downright impossible, to switch to a purely cloud-based infrastructure, and with good reason. Despite the numerous advantages, financial and operational, of cloud systems, large companies are loathed to let go of their on-premises assets.

Industry-leading Data Analytics companies have bypassed this dilemma by offering an alternative that gets large companies the best of both worlds. Hybrid systems, those who utilize both on-premises and cloud services are gaining traction among corporate giants. Having the scalability options afforded by cloud-based systems while keeping their tried-and-tested infrastructure relevant.


#2 Pure Data-driven approach is being replaced by Business-Driven models


Businesses are realizing that Data Analytics integrated into their business processes are necessities if they want to succeed ahead of the curve. It’s not enough that companies create software or features, deploy and then deliver updates as needed. Today’s applications need to address customers, process transactions and interface to different devices in real-time and on an individual level. Healthcare and related companies must process valid claims and dismiss false ones. Retailers and their sites must feature individualized product recommendations in real-time.

These cases require rapid feature release cycles with an agile platform that can deliver both analytical and operational processing to increase value. It’s not just enough to design features and software based on modeled data from source systems, development is now gearing towards supporting business processes to deliver more value.


#3 Delayed or post-collection Data Analytics is being replaced by Proactive Analytics


Data Analytics allows businesses to make informed decisions about how they should update or modify their systems. This used to be done after a significant time collecting, processing and analyzing data collected from their customers and transactions. This time-lag between data generation and feature release can be substantial in the way that a business may no longer be working with up-to-date information.

Businesses have overcome this issue by employing preemptive or proactive Data Analytics, as against a reactive one. By making use of pre-collected data sets, preemptive analytics drives transactions as against just merely modifying and updating features. This gives businesses the momentum to define new revenue sources, reduce overall costs and ultimately, improve customer experience.


#4 Data as a product versus Data as something that is freely collected


Business forecasters predict that the age of free data utilization and collection is drawing to a close. Industry leaders are now figuring out ways to monetize the data they collect. New business models, paradigms and processes are in development to create systems specifically targeting those who need data as a product. There might come a point that even individuals can put a value on their own data.

So that is 4 trends that are going out. Do you have any other analytics trend that’s fading or gaining momentum? Leave a comment and let us know.

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