6 Data Analytics Trends to Look out for in 2020

6 Data Analytics Trends to Look out for in 2020 featured image

The data analytics landscape is fast-changing and constantly evolving. With new technological advances, there is a greater understanding of how data analytics technologies can positively impact an organisation.

In this blog, we’ll consider new techniques within transfer learning, augmented intelligence and knowledge graphs.

We begin by looking at the data analytics market itself.

Commoditisation of Algorithms

The race is well and truly on, as large organisations like Amazon, Google and Microsoft seek to further expand their portfolio of owned data analytics and machine learning algorithms.

In 2019, Google acquired Looker and Salesforce acquired Tableau, a trend that will not only continue in 2020 but will likely become much more prevalent. Which leads us to what we believe, will be another trend that will emerge in 2020.

Democratised Data Analytics & Ease of Access

The commoditization of the algorithms within a unified platform, provides a range of opportunities, not least of all, greater ease of access for the end-user.

Gartner identifies “Democratised Data Analytics” as one of their top ten strategic technology trends for 2020. Organisations will no longer be restricted by the same financial, hardware and staffing requirements that would have previously prevented them from incorporating data analytics and machine learning into the business operations.

The cost of running workloads on Amazon Web Services, for example, dropped by around 73% between 2014 and 2017. Aron Brand, CTO of CTERA predicts that as cloud storage becomes more accessible, as much as 80% of enterprises will shut down their traditional data centres and shift to the public cloud by 2025.

This accessibility is underpinned further by developments in the Machine Learning Space, specifically in relation to Transfer Learning and Self-supervised Learning.

Transfer Learning

In a recent blog, we looked at transfer learning and the opportunities that it presents for organisations. In particular, we discussed how recent developments within the space have made complex machine learning algorithms more accessible than ever before.

2020 will see more organisations utilise Transfer Learning as a cost-effective and efficient means of incorporating artificial intelligence and machine learning into their business operations. In a 2019 report, Gartner outlined that 14% of global CIOs had already deployed AI within their organisation and that up to 48% would deploy it in 2019 or by 2020.

Self-supervised Learning

In a similar vein, Self-supervised Learning will also become much more prevalent. A major goal for machine learning practitioners is to train models with much smaller datasets than are currently required.  An approach called ‘self-supervised’ learning has proved useful in this respect.  In a self-supervised learning approach, the model training begins with a ‘pretext’ task that accustoms the model to your data without trying to solve your actual problem. Once the model performs well at this task then you proceed to the actual object detection task.

Traditionally applied to text analytics, recent advances within the space have reinvigorated interest in self-supervised learning, applying it specifically to image recognition.

Knowledge Graphs

Knowledge Graphs are not necessarily a new concept, in fact, the concept has its origins in ontology, a branch of philosophy that looks at the categories and definitions of entities within a particular subject area and the relationships that exist between them.

Today, the term typically refers to data analytics systems that unify, categorise and define entities within disparate sets of data and that can identify the relationships that exist between them.

Knowledge Graphs frequently appear on Gartner Hype Cycles, appearing most recently at the apex between the “Innovation Trigger” and “Peak of Inflated Expectations” stages of their Hype Cycle for Emerging Technologies 2019. Knowledge Graphs certainly are a trend to look out for in 2020.

Augmented Intelligence

Augmented Intelligence is a topic near to heart at Analytics Engines. Many of the solutions that we provide are at their core, augmented intelligence systems, that seek to empower human processes through the intelligent use of data.

Consider our recent case study for Innovate UK. Our solution, COBALT Grant Manager, utilises machine learning and text analytics to automate the process of grant application checking and assessor matching and to assist in identifying fraudulent submissions. Amongst a host of other benefits, our solution has been able to reduce 4 days’ work to just a matter of minutes!

Augmented analytics, which comprises machine learning and NLP, is predicted to grow significantly over the next few years, reaching an estimated value of $18.4 billion by 2023. A staggering growth from its current valuation of $4.8 billion.

Conclusion

AI, Machine Learning and Data Analytics present a host of extraordinary opportunities for organisations and individuals. To find out more about Analytics Engines’ solutions and how they have empowered organisations to do more with their data, contact us using the form below.

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