Trevor Schulze, CIO, Alteryx, outlines how CIOs can utilise automated analytics and build a modern data stack.
The volume of data being generated and used today is constantly growing. According to the European Commission, it is expected to reach 175 zettabytes (ZB) by 2025, up from the 33 ZB generated in 2018.
Despite this, harnessing the hugely valuable insight held in an organisation’s disparate data sources can be extremely challenging due to the complexity and scale of modern data.
The solution, then, lies in the automation of discovering, preparing, and analysing that data.
Data can be valuable to a business in many ways. It can improve the efficiency of routine processes and, by reimagining those processes, can improve how services are delivered.
And, of course, it’s a crucial source of information and insights, not just to the business itself but to its customers, partners and suppliers, too.
Take customer experience, for example, which can be a critical point of difference for many businesses.
By understanding the needs, preferences and actions of their customers and by anticipating their future behaviour, organisations will be better able to tailor their products and services and improve the overall experience they offer. Doing so, though, depends on data.
From legacy databases and applications to modern cloud data warehouses and cloud platforms, today’s enterprises pull data from a range of different input sources in a variety of structures and formats.
Organisations must be able to extract value from data at the speed and scale that will allow them to develop personalised offerings, sharpen their analytics strategies, and more confidently modernise their data management.
Without the right analytic automation tools in their tech stack, CIOs can find it challenging to analyse this volume and variety of data and maximise the value it represents for real-time insights.
CIOs have access to a wealth of technologies designed to modernise the data management journey across their tech stack.
One need look no further than the recent rise of cloud-based data warehouses and lakehouses for proof of this, not to mention the growing confidence with which IT teams are using internal training data to start experimenting with building their own LLMs.
But, while this trajectory to data maturity is certainly encouraging, many organisations simply don’t have the ability to unlock the full capacity of their ever-increasing data to Drive business value.
Indeed, the European Commission describes this as untapped potential: 80% of all industrial data goes unused. With many businesses still overspending on cloud, or saddled with tool sprawl or redundant software licences, their data stacks just aren’t configured to make the most of the technologies available.
What’s required is a data stack that more effectively addresses the many different use cases and roles found in today’s businesses.
It should be sufficiently flexible to support multiple deployment scenarios, for instance, managing data pipelines whether they are on-premise, private, public or multi-cloud or hybrid.
It should also allow operatives to transform their data in the data warehouse of their choice and enable them to build data workflows in one place and execute them in another.
Importantly, the right data stack is one that can empower an organisation’s employees to get the most from its technologies.
Low-code – or even no-code – it should be accessible, making it simple for anyone to compile and analyse data, rather than just data scientists or engineers.
Data enables outcomes for every area of a business. So, whether it is improving the customer and employee experience or complying with rules and regulations, each line of business should be able to creatively solve its own analytic problems and apply its own specialist domain knowledge to the use cases which matter most.
By way of illustration, consider how a tech stack capable of automating data analytics can be used to ensure regulatory compliance in the financial services industry.
Under regulations like MiFIR and MiFID II, financial institutions are required to report applicable transactions to the relevant regulatory authorities.
But millions of transactions can take place every minute, especially at larger institutions; the manual preparation and cleansing of this data for quality assurance (QA) purposes can therefore be a tremendously slow process.
In fact, it can take as long as up to two months to notify regulators. This can leave financial institutions vulnerable.
If any part of the system should fail and require correcting during that time, they risk facing regulatory fines.
These risks could be mitigated by employing an automated data analytics stack.
A combination of data engineering capabilities and a mixture of visual analytics tools can be used to create a streamlined workflow which will alert institutions in real time if they need to make corrections to ensure compliance.
By transforming the QA process from reactive to proactive, this allows teams to address any issues as they arise, instead of possibly having to wait months for results.
Of course, this is just one example of how automated data analytics can be applied to a business use case.
Their potential scope is much broader, though, enabling organisations across all industries to deploy an analytics strategy to identify new opportunities, improve efficiencies, meet ESG criteria, and more.
Data is hugely valuable to every business. As the data generated and used by businesses continues to grow in volume and variety, a modern automated data analytics stack with a simple and accessible interface is key to allowing everyone within an organisation to unlock that value for the benefit of internal and external stakeholders alike.Click below to share this article