What analytics will mean in the next 10 years?
The landscape of data analytics has continuously evolved over the last 10 years. This article will explore key changes that are predicted to take place and how they will shape the world of data analytics.
Growth of key areas of analytics
– Rise of regulation: The introduction of GDPR has significantly affected the way we deal with data and additional regulations are set to occur over the next few years. The rise of GDPR can be a potential benefit to firms, if it enables them to build trust with consumers giving them more confidence over how their data are handled.
– Cloud computing: There is emphasis being placed on developing cloud computing in order to store Big Data so that Advanced Analytics and Machine Learning can be successfully implemented.
– Machine Learning: Machine Learning is the use of algorithms and computer programs that automatically learn and evolve by carrying out tasks without the need for instructions (i.e. without being ‘explicitly programmed’).
Machine Learning automating processes means we can extract conclusions at a quicker and more accurate rate. This is crucial as according to Gartner ‘by 2020 over 40% of all data science tasks will be automated.’
The rise of Blockchain
Blockchain is a public ledger that records a series of ‘time-stamped’ transactions across a range of computers that are linked to form a network.
Blockchain in the past has solely been used in cryptocurrency, but now there is scope to implement Blockchain in ‘predictive analytics’. This is because Blockchain has the computational power to handle Big Data and help forecast future trends. This means that Blockchain can be implemented in marketing if ‘data gained from market realities’ about predictions of consumer behavior and demand can help firms develop more successful campaigns.
How does Blockchain work?
Blockchain works as new data can be continuously added to a ledger and forms a new block to the chain of data. A network of computers support the ledger, if one computer fails it has no impact on the ledger.
Blockchain helps to improve validity in the data stored as the combination of the database being centralised as well as relying on ‘cryptographic’ signatures makes it a highly transparent way of carrying out transactions reducing any threat or risk from hackers.
Developments in Augmented AI
Augmented Analytics is a term established by Gartner which is the use of ‘Machine Learning and Natural Language Processing to enhance data analytics, data sharing and business intelligence.’
Augmented analytics is key for businesses which receive data from various streams and sources and the future potential of this type of analytics can be highlighted from a report by Allied Analytics which states ‘due to growing adoption of next-generation technologies, such as augmented analytics, the global augmented analytics market-size is expected to reach $29 million by 2025.’
What does Augmented Analytics involve?
There are three main stages to the process of Augmented Analytics and can be highlighted on Figure 1 below:
- Augmented Data Preparation: using ‘machine-learning automation’ this allows data to become more accessible and reduces the need for assistance from data scientists to carry out tasks such as data profiling and testing theories.
- Smart Data Discovery: i.e. automating analytics which means that users can visualize and extract findings without the need to produce models to do so.
- Natural Language Processing: allows individuals to understand big datasets by producing a ‘written summary of insights’.
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