Automation, ML, and analytics today

The capabilities of Data Analytics have evolved drastically in recent years, with advancements in technology shifting the landscapes of Cloud data storage, the overall complexity of architecture, the vastness of datasets, and more.

Some emerging fields that have gained significant attention in recent years are the development of Machine Learning, Predictive Analysis, and Artificial Intelligence – providing analysts with the opportunity to automate and model complex manual processes.

However, as these automation capabilities continue to advance and grow, many have questioned the impact on the rest of the analytics industry – with fears over the need for continued human involvement in doubt by some.

Below, we examine the continued impact of automation in traditionally manual processes, the current advantages of ML, as well as what the future of human involvement in Data Analytics may resemble.

Automation in analytics – the advantages

For enterprises that possess the computational capacity required, as well as a reasoning for integration, automated Machine Learning functions can provide a wealth of benefits. Two of the core advantages of enabling Machine Learning functionalities are in being able to take a proactive, rather than a reactive stance, as well as possessing the ability to eliminate traditionally resource-intensive manual processes.

The benefits of taking a proactive stance

As markets become increasingly competitive, and the margin for error grows finer than ever, businesses must reduce the chance of falling behind the competition to maintain essential leverage and stance.

Previously, this has contributed greatly to the shift towards analytics, as businesses recognise the need to rely on data-driven approaches rather than gut feelings and instinct.

For those wishing to maintain the greatest possible lead over key industry rivals, they may find that Machine Learning is a highly useful and necessary tool.

With strategies such as Predictive Analysis, Machine Learning processes may model future outcomes and possibilities for the greatest success possible. These models may include consumer reaction to promotional activity, the likelihood of seasonal trends affecting sales, and other future shifts in behaviour that may affect strategy.

Eliminating the need for time-consuming manual processes

Previous Data Analytics processes could be repetitive, and often, restrictive – issues only worsened as datasets become varied in structure, complexity, and size.

These processes are also traditionally time-consuming – demanding significant time spent aggregating and rendering datasets for analysis. Automation can significantly accelerate this, pre-rendering and crawling through vast datasets with ease, before reporting on findings to users. Although this reduces the need for human involvement, the advantage to teams of analysts are clear: data on-demand, and insights pre-visualised.

Automation as a means to empower

As more and more businesses realise the potential advantages of shifting to ML-enabled analysis, many question the need for the same level of human involvement as previously thought necessary.

However, an alternate viewpoint is that rather than negating the need for human impact, automation seeks to enable existing teams to reach their full potential.

With less time spent on repetitive data entry and manual analytics processes, analysts can utilise their full skillsets – becoming more involved with strategy, detailed analysis, and planning. Empowering teams and enabling the use of full skillsets is possible due to two core factors: the ability to act on trusted data, and the shifting of time spent on analysis to a more strategic viewpoint.

Shifting involvement in analysis

Before the capabilities of Machine Learning was fully realised, analytical roles traditionally devoted a large majority of time to detailed reporting on collated data and information. However, this responsibility detracts largely from the potential of the analyst behind the role, since reports are often manually performed and must be extremely detailed to accurately depict the results gathered.

However, with capabilities to automate analysis processes before creating intuitive visualisations, ML allows for any user to gain a thorough understanding of the data being presented to them with the use of mediums such as colour, space, and graphs.

As a result, analysts can allocate more time to enhancing and adapting their current strategy – developing the overall growth of the business while increasing job satisfaction and productivity in the process.

Enhancing the quality of collected data

More and more organisations are realising the empowering abilities of ML, with teams ranging from sales to marketing able to use their skillsets to the highest potential possible based on the most accurate data available due to being able to place confidence in intelligence gathered.

Gaining access to the highest quality of data has previously proved a difficulty for analysts due to several factors, such as inconsistent data governance and complexity of structure. However, with automation increasing reliability in results through enterprise-wide integration and collation, users gain access to reports they can trust in – and can act with confidence.

The future of human involvement in analytics

Both businesses devoted to enhancing the analytics experience, as well as the analytics community itself, appears devoted to further developing the landscape of Data Analytics – collaborating and creating solutions in innovative ways.

One of the most recent driving forces emblematic of continued human involvement in Data Analytics is the large community behind Power BI.

From establishing weekly visualisation challenges to attending Power BI conferences – this community can voice their opinions on the platform, recommend improvements, and assist in testing. In turn, their impact allows the visualisation software to truly evolve and adapt to ongoing change and requirements.

As we look to the future of human involvement in Data Analytics, we expect the emphasis to shift toward enabling innovative strategies than ever before – as specialists with the time necessary can plan and utilise predictive analysis to forecast powerful proactive campaigns.

Moreover, we expect to see an increase in collaborative environments currently visible within Power Bi and Tableau – as users find platforms to voice concerns and recommendations, as well as any enquiries and ideas, to a wide, like-minded audience.

Get in touch today

At DataShapa, we are committed to providing the best consultancy on a wide range of specialist requirements from integrating successful Data Warehouses to installing Machine Learning capabilities.

To learn more about the possibilities for ML within your business, or for any questions or enquiries you may have, contact us here. One of our specialists will respond as soon as possible.