A look at the biggest data analytics trends for 2019

As our understanding of data analytics has developed, data analytics is being used in wave of innovative and exciting new ways. From IoT analytics and augmented analytics and DataOps we look at the top data analytics trends for 2019

If you’ve ever taken the time to shop online with a major retailer, everything from your customer journey to your buying decisions will have been quantified in some way. Your data will have added to a larger data set filled with other customers data. Data engineers will then use data analytics platforms to try and extract insights.

For many enterprises, these datasets are the key to creating a more efficient service that delivers a more targeted customer experience. The growth of data analytics has been particularly dramatic. In 2018 alone, big data adoption increased to 59% from 17% in 2015.

However, the ways that data analytics is manifesting within the workplace is undergoing a quiet revolution. New data analytics methodologies like DataOps and hybrid solutions like IoT analytics are coming to the forefront of the next wave of development. No longer is data analytics being used in isolation but in tandem with other disruptive technologies.

Data analytics has become one of the driving forces for digital transformation efforts around the world. Though with respect to modern enterprises, the changes have only just begun. There are a number of emerging trends that are important for CTOs to watch out for this year.

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IoT analytics: data analytics and IoT come together

One of the most disruptive technologies making its way into modern workplaces is Internet of Things (IoT) devices. IoT devices are devices that can connect to networks. These devices can be connected to the edge of a network and include everything from smart lighting to health monitors. Gartner anticipates that there will be 20.4 billion IoT devices by 2020.

Everyone single one of these IoT devices will be generating data that organisations can monitor with data analytics platforms. Data analytics is integral to help data scientists realise the potential of this data. Manually reading through this data would be impossible as the abundance of data would be too great.

Digital transformations planning to incorporate IoT devices on a large scale are starting to incorporate data analytics as a supporting technology. Providers like Amazon Web Services have started to deploy their own IoT analytics solutions like AWS IoT Analytics. As the adoption of IoT devices increases we can expect more focus on these type of edge analytics solutions.

Companies that intend to collect data from IoT devices will inevitably incorporate some form of data analytics. Data analytics and IoT devices are an ideal match as analytics solutions deliver transparency over the data gathered by connected devices.

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The growth of augmented analytics

Two years ago, Gartner predicted that by 2020, augmented analytics will be the “dominant driver of new purchases of business intelligence, analytics and data science and machine learning platforms and of embedded analytics”. Forecasts of the augmented analytics market seem to agree with this outlook as well.

The global augmented analytics market is expected to grow from USD 4.8 billion in 2018 to 18.4 billion by 2023, at a Compound Annual Growth Rate of 30.6%. The outlook looks promising because augmented analytics offers organisations faster insights, combining machine learning and natural language processing together to automate the process of generating business analytics.

The biggest area where augmented analytics will be of use is business analytics. The BI industry is being disrupted because augmented analytics threatens to lower the barriers to entry in the industry. Augmented analytics solutions can help data scientists to automate the process of preparing data, analysing data and building models.

As Rita Sallam, VP analyst and Gartner Fellow notes “data science is evolving in the same way where machine learning and some AI techniques are being used to automate the feature selection process, the model selection process, even the code generation once a model is selected”.

Commenting further Sallam states “these new augmented analytics capabilities that are applied to data science and machine learning platforms automate a lot of that”. In other words, augmented analytics make it easier for companies to interact with the insights that they generate.

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DataOps data analytics

The changes taking place with data analytics are not only transforming its use cases, but its methodology as well. DataOps is an agile operations methodology that is being used to combine DevOps teams and data engineers together. The term DataOps was inspired by DevOps in software engineering which combined software development and information technology together to build and deploy software products faster.

Organisations are using DataOps to build data analytics platforms more efficiently. In 2018, Nexla found that 73% of companies were investing in DataOps. The increase in investment has come as a response to the challenges of developing and maintaining data analytics pipelines.

A collective definition of DataOps is hard to come by because the term acts as an umbrella term for the development and deployment of data analytics pipelines.

According to Christopher Bergh, “DataOps is the recognition that a set of problems have crept into organisations over time and slowed down productivity”. DataOps “encourages data driven organisations to begin with a similar practice of testing their data pipelines to build trust and evolve best practices”.

DataOps and the Data Platform

By using DataOps organisations can build more efficient data pipelines to improve the accuracy and speed of analytics delivered. For enterprises DataOps acts as a potent alternative methodology for managing data analytics strategies.

Data analytics in 2019: evolving use cases and methodologies

As enterprises have traversed further down the road of digital transformation, our collective understanding of data analytics has reached new levels. Now we know that there is not only one way to use data analytics. Data analytics can be applied in a range of ways according to the requirements of the company using it.

Organisations can pick and choose the type of analytics they wish to deploy and ignore those of limited value. For some companies, IoT-driven analytics solutions will be the most useful application of data analytics whereas others may take on the DataOps mindset. The take home message is that data analytics is more malleable than ever before.


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