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4 Long-term Trends in Big Data and Analytics for the Twenties

4 Long-term Trends in Big Data and Analytics for the Twenties


Chances are, you have already got on board with the benefits of using big data for business intelligence. It’s a complex world, however. Big data is still in relative infancy and many businesses are still finding their feet with it. If you are wondering what to do next, here are four trends in the collection and storage of big data as well as analytics that are set to shape how businesses approach this task over the remainder of the decade.


1. Capitalising on the explosion of data

Data generation is accelerating. This is in part down to the wealth of personal devices such as smartphones, home voice-activated speakers and wearables like fitness trackers. However, cloud systems and database transactions both contribute a huge amount of data daily. Leveraging data from all these technologies is opening up great opportunities. Disruptive forces like Amazon and Uber have all exploited big data to identify gaps in the markets.


With the Internet of Things (IoT) set to increase the acceleration of big data even further, now is the time for businesses to evaluate their data processing capabilities and strategy. The growing diversity in data is demanding that businesses look beyond the more traditional data warehouse method of processing big data.


As a result of the explosion in big data, there is an urgency for the devices that can handle the data processing themselves. This is known as edge computing. Ever more sophisticated devices are able to store data without putting additional strain on the network. Demand for these devices will only increase. Take driverless cars, for example. Data from sensors must be analysed immediately.


Top priorities for big data strategy

Departments need to head up the facilitation of the use of next-generation technology to make sure all the right data is being collected and processed. This will mean moving data away from older, defunct systems that don’t enable capitalisation on big data.


2.  Reimagining data architecture using the cloud

Prior to the big data revolution, businesses took care of their own data storage. This would involve vast data storage facilities that needed substantial resources to operate and manage them. However, the switch to the cloud has relieved businesses of this burden. Now it is more simply a matter of paying for storage and using the cloud infrastructure provided by companies like Google and IBM.


The ability to access vast data sets without needing to operate an expensive infrastructure means that data analytics are more accessible than ever before. Thanks to the cloud, however, whole new models of data architecture are required. These must allow businesses to cope with the diversity in big data discussed above but also promote its authenticity. A solution that is being widely adopted is the notion of a ‘data lake’. This involves storing data in a more native state than might have happened previously.


Both structured and semi-structured data can be stored in the cloud. In fact, it is entirely possible to collect, clean, store and analyse data on the cloud and more and more businesses are doing so. A cloud-based data lake offers wider opportunities for utilising big data.


Top priorities for big data strategy

If your business is still operating data warehouses then these should be replaced as soon as possible with cloud computing if you want to remain competitive. Strategy should focus on creating a flexible, dynamic data architecture model.


3. Implementing AI and machine learning

Big data is challenging if your business does not have the right processing capabilities. Automation of data analysis is now essential. Using open source platforms like Hadoop and Spark, it is possible to process vast amounts of data at incredible speed. Machine learning and AI technologies can be used to get insights into patterns and trends and ultimately use data to elevate the customer experience.


Machine learning and AI have widely taken over the world of data analytics in competitive business. Organisations of all sizes have adopted the technologies to perform advanced data analytics and to provide predictive analysis, too. Machine learning can detect trends and anomalies in real time so that organisations can be sure they always have their finger on the pulse.


The shift away from data warehouses and the slow reporting technology that accompanied them to cloud and edge computing has given businesses incredibly smart and responsive applications for data analysis. It is now possible to analyse data from image, video, text recognition, voice and chatbots. Businesses are getting better-than-ever insights into consumer behaviour and operational efficiency.


Top priorities for big data strategy

It is vital to have AI and machine learning expertise within your organization. The difference between traditional data analytics and AI data analytics is that the first uses coding whereas the second needs to be trained.


 4. Focusing on ethical data collection and security

Research by Statista has shown that 5 out of 6 people are worried or unsure about the collection of personal data and its use. This is something that organisations cannot ignore. Most consumers are aware that businesses are using data from almost every aspect of their lives, from their streaming services, social media and even their connected devices at home.


Of course, there are strict regulations in place when it comes to personal data and compliance is vital. However, with vast amounts of data in the hands of businesses, compliance is a complex issue. The sale of personal data, collected by large organisations and sold on, is fading fast. Organisations have needed to change their approach to data collection, putting more of the onus of compliance onto themselves.


Top priorities for big data strategy

The ethical aspect of data collection and storage needs to be a focus, going hand in hand with compliance.


What’s next?

The world of big data analytics is moving first and new trends will emerge. It is essential you have the right team of people who individually specialise in machine learning, AI, data analytics, compliance and all technology relating to today’s business intelligence methods. Seeking the expertise of an IT recruitment agency  that can provide both local and remote workers is your next step.


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