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Skills shortage and technology should not be a barrier to entry to become a data-driven business

There must be investment in technology and tools to become a data-driven business, but tight budgets and a lack of technical expertise should not stop organisations from starting their data journey.

Setting the foundations 

To enable data-driven decisions, an architecture and system that fuels the target objectives for the organisation must be in place – but how can this be achieved? Despite the abundance of different solutions, businesses are grappling with ever-evolving market trends, inflation, team member churn and geopolitical turmoil, so it is imperative that any data tools being selected are fit for purpose.  


“Today’s environment puts leaders under pressure to make the right decision faster at all levels of the business – to optimise costs, manage assets and invest in commercial growth,” said Melissa Burroughs, director, product marketing, Alteryx. “It has become clear across industries that analytics and AI are the crucial ingredients to making better decisions as quickly as possible to protect and grow business. That is why organisations who embrace analytics automation and AI for all their employees, decisions and systems are poised to develop durable competitive advantages.” 


There are numerous examples of businesses investing heavily in the development of a custom data system, only for it to be rendered unusable shortly after completion as it cannot efficiently interface with other emerging technologies. This risk is avoided with drag-and-drop solutions and immediately allows smaller businesses with limited funds to get involved one way or another. Part of the appeal of utilising data is to seek efficiencies and improve return on investment, so it is slightly ironic that (historically) implementing data tools was seen as cumbersome and costly.  


Expansion for the future 

One of the many benefits of low-code and no-code tools is that they can be easily scaled. This provides a huge range of possibilities for all types of businesses. The obvious one is that smaller companies with ambitions of expansion can easily add more building blocks to their technology needs as and when needed almost indefinitely.  

For existing legacy businesses that are looking to amplify their data capabilities, the use of drag-and-drop low-code solutions means that data tools can be placed in the most suitable areas without any major changes to existing architectures and operations. This can be done on a team-by-team basis, starting with one or two departments – most likely data and IT – and then the tools can be trickled into other areas such as operations, sales and finance when the data culture of the organisation is maturing. It will still require a change in data culture and the need for data literacy education across the business, but the difficulties of introducing new tools and technologies are drastically reduced.  

“In practice, many organisations have failed to leverage analytics and AI at scale, missing opportunities to improve the speed and quality of decisions in their business,” said Burroughs. “This occurs due to skill gaps, data silos and an inability to govern (and thus trust the results of) ad-hoc analytics processes. Unlocking the disruptive value of analytics and AI for organisations requires an approach that is easy for non-technical professionals to adopt, integrates data from siloed and legacy systems and provides enterprise-grade governance and automation to ensure analytic insights are trusted and quickly accessible – so they can power business decisions.” 


Businesses that are eager to embrace AI and analytics are poised to find success even if they feel they lack the technical tools or team members to compete with established businesses. By utilising the findings of existing data – and future data sets – businesses of all shapes, sizes and niches can get ahead of the competition and thrive in an ever-evolving marketplace. 

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