There’s no question that organisations are excited about the potential of generative AI for accelerating the pace of time to insight while eliminating errors and increasing accuracy by automating the data processes.
Companies have long recognised the benefits of using data and analytics to improve revenue performance, manage costs, and mitigate risks. But with global data volumes forecasted to continue increasing, achieving data-driven decision-making at speed and scale is impossible without automation paired with accessible AI.
In all of today’s hype around AI breakthroughs, new applications for automation, replacing humans and impacting jobs – we should try and stay rooted in real-world use cases. From helping to discover new business opportunities to speeding up the processes that lead to insights, AI and automation have vast business potential for simplifying and streamlining repetitive manual tasks.
Automation enables data workers to answer big questions with slashed time to insight, reduce errors and even benefit from enhanced collaboration and career development. This matters because, increasingly, we’re all cast into the role of the data worker – no longer the sole reserve of technical coders – non-technical domain experts are now extracting value from data through accessible analytics.
This amounts to the powerful potential of automated analytics. But, of course, automated analytics must be tailored for specific organisations, their needs and individual end user personas – from data analysts to data scientists to business users.
Important questions include: where does the potential of data analytics lie, what are the broader benefits and where should businesses get started?
The need for analytics automation
Automated analytics is the use of software and AI — typically machine learning (ML) algorithms or generative AI — to automate end-to-end analytics. Applied to business use cases, automated analytics can stretch as far as covering every stage of the analytics lifecycle – data collection, preparation, accumulation, analysis, report building and beyond – to move the needle in what data and its applications can achieve.
Automated analytics takes multiple forms. Data workers might harness automated machine learning (AutoML) to automate code-intensive steps in creating machine learning models or generative AI to benefit from natural language interfaces and summarisation to accelerate the delivery of enterprise analytics. Whatever the use – the overall trend across various industry verticals is increased uptake of automated analytics.
The powerful applications driving adoption
The reasons to automate analytics are extensive. Perhaps the biggest common gripe it addresses is dramatically reducing the time workers spend on data preparation and enrichment in manual spreadsheets. It’s no exaggeration to say that such manual data entry can take up to two hours per day for a typical data worker – that’s 500 per year.
Automated analytics can cut data processing time by more than half, doing the same work in seconds while freeing up time for data workers to focus on more strategic work.
Beyond significant time savings, analytics automation can simply process massive quantities of data in comparison to worksheets ill-equipped for modern applications of data. Automation through the analytics lifecycle also helps control and limit errors in analytics – errors that commonly occur in huge spreadsheets consisting of complicated calculations.
Solutions with a visual interface for building analytical workflows make it simpler to avoid errors with rich integrators and connectors pulling in disparate data without the need for copying, pasting and manually entering data.
From a strategy perspective, automation strengthens collaboration between teams. Facilitated through cloud platforms, it’s feasible for those without extensive knowledge of machine learning and predictive modelling to use no / low-code interfaces to build their own analytical workflows and applications that put automation into play.
Data workers who utilise automated analytics can offer overall better business value armed with real-time insights that contribute to cost reduction and data that more accurately reflects the business. Subsequently, data workers are in a better place to put impactful initiatives into place and get noticed by managers, which helps to advance their careers.
With the applications of automated data analytics clear, it’s important to establish how to put automated analytics in place.
Structured thinking to maximise chances of success
Every business will have different needs behind the adoption of automated analytics. It’s important to link those needs to the specific business problem statements that analytics can feasibly solve. Keeping such end goals in mind helps IT leaders stay focused and guide the selection of an automated analytics solution with the right priorities.
On selecting the perfect solution, businesses should make sure to make sure the solution that addresses their needs is also enterprise-grade. Adherence to required security and governance policies is a good example.
With a solution deployed, the onus falls on businesses to ensure they are inputting the best data to fuel analytics. Depending on the type of business that could entail pulling in data from CRM tools, financial systems, web analytics or all three. The important part is creating the optimal analytics workflow. From there, let the entirely automated process run its course.
Once workflows are in place, it’s going to take a lot of time before sophisticated predictive capabilities can be put in place. In the short-to-medium term, more gradual improvements should be prioritised. The possibilities for automation are endless, and the longer data workers automate and improve their processes, the more opportunities they’ll open up to dedicate time to strategic tasks.
In conclusion, analytics automation and its applications are a no-brainer for most modern businesses to put in place. The ability to access data in any location, at any time, and analyse it in seconds rather than 500 hours annually is not an opportunity that can be passed up. The snowball of benefits for businesses from a strategic standpoint, from improved collaboration to greater efficiencies, serves as only further proof that automated analytics are the future for businesses to carry out a range of functions quicker and easier than ever before.
