No-code, self-service analytics are the shortcut to a data-driven business
The vast majority of businesses have already recognised that data is not just a by-product of their operations, but a resource that must be mined for actionable insights. Tapping into years of customer, supply chain and business operations data can uncover costly inefficiencies and opportunities to increase competitiveness.
However, many employees don’t have the right access, tools and skills to effectively use data to support their decisions. The more people that can make data-informed decisions across an entire business, the better outcomes organisations will drive. This means that building an internal data culture has become a top priority for businesses.
Data science has, therefore, been bumped right up to the top of the list of employable skills. Unfortunately, supply lags far behind demand. A recent Government report (Quantifying the UK Data Skills Gap 2021) found data skills have now become an essential entry requirement for two-thirds of UK occupations. But the report also found that in the last two years, almost half (46%) of UK businesses have struggled to recruit for roles that require data skills.
It identified 178,000 UK vacancies requiring ‘hard’ data skills – where work is centred around data and requires more advanced knowledge of it. However, little more than 10,000 data scientists graduate from UK universities per year. This means that businesses cannot rely only on incoming graduates to bridge the data skills gap. Instead, it requires significant investment in upskilling existing employees too. A Salesforce and IDC report found that by 2030, nine in 10 workers will need to learn new skills to do their jobs at a cost of £1.3 billion a year.
This upskilling will be critical to the future of business, but it will not be achieved overnight. And so, businesses are faced with a problem: how can they take steps towards creating a data culture and ultimately become a data-driven organisation more quickly?
The answer lies in hyperautomation, and specifically low-code /no-code data and analytics platforms. These platforms enable more parts of the business to mine their data assets at scale and rapidly operationalise insights, predictions and recommendations by building ad-hoc dashboards and analytical toolkits that support every key business decision. Hyperautomation focuses on immediacy and context first and delivers rapid iterative gains to business users. The aim is to eliminate the data science bottleneck by enabling business domain experts to work with AI to answer important questions that the business is facing right now.
The quick wins in this space will be in augmented experiences rather than models which deliver fully automated workflow. Business users will need a variety of experiences that empower them to work alongside an AI to derive value from their data assets. The best tools can quickly and efficiently pull together large data sets from multiple sources into an analytics workbench that lets users understand “what has happened and why”, “what will happen”, and “what is likely to happen if a specific action is taken”.
This enables business users to sift through massive data sets and explore millions of combinations in seconds to automatically surface insights.
