Defying Data Barriers: How IT and Value Define the Business
Dr Karen Luyt, Expert Solution Architect: Business and Digital Advisory, and Stefan Steffen, Executive: Data Insights and Intelligence, at BCX unpack the challenges facing the digital C-Suite when it comes to extracting and proving the value of data
The digital C-Suite – Chief Information Officer, Chief Data Officer, Chief Analytics Officer – has to solve for the business.
Every investment, technology and methodology is defined by the value they deliver to the organisation and its bottom line.
As Harvard Business Review puts it – these roles are tenuous and set up for failure because they were originally defensive, focusing on control and risk and not on business value. It was not a role that would deliver the commoditisation of the data that the organisation wanted.
This is reflected in data released in the Data and Analytics Leadership Annual Survey 2023 – the CDO role grew from 12% to 82.6% from 2012 to 2023 and yet only 35.5% of companies believe the role is successful and only 40.5% say that the role is understood within the business.
It was a sentiment echoed at the Gartner Data and Analytics Summit 2023 where less than half of data and analytics leaders (44%) believed their teams delivered value to the business with limited funding (13%), resource limitations (29%) and talent (39%) proving the biggest obstacles to success.
Technical leaders need to find ways of working with the data and the technology to ensure it is oriented more closely to the business.
The business hat, so to speak, must be firmly on the digital C-suite’s head when looking at how they can optimise data, analytics, and systems to support every unit within the organisation.
HR, finance, supply chain, marketing – every unit requires a slice of the digital insights pie to ensure they too are optimising for success.
Value is the bridge between IT and the business. Now, CDOs, CIOs and CAOs must reimagine their architectures and approaches to ensure this value is found throughout their digital transformation and investment strategies. However, there are challenges.
The first is the impact of the cloud. Often perceived as an extension of the data centre, the cloud is a risk factor. Digital leaders need to understand the impact of having data in private and public areas while deftly navigating the challenges of linking and managing the data seamlessly between different cloud implementations.
This is further complicated by data security, change management and data sovereignty. Teams are struggling with vast quantities of data that’s not linked or is duplicated and their data governance is still in progress.
This is a complex landscape to navigate and manage, made even more challenging thanks to limited skills availability. There are not enough people with the expertise and training to get the value from the data.
Teams need to align the business roadmap with the technology investment to ensure the value created aligns with expectations.
This should be further balanced with a focus on improving processes to ensure that the business can better leverage the data through analytics tools or AI capabilities.
In addition, there is value in focusing on storage solutions that reduce the cost and complexities associated with vast quantities of data – instead, using tools that refine governance while increasing value and accessibility. These tools must help teams reduce data duplication, improve movement, and optimise costs.
BCX has developed an agile stable of data and analytics tools and capabilities designed to clean, refine, and manage the data for the organisation.
This high-level expertise translates into providing data-as-a-service capabilities with teams that have exceptional skills and a deep understanding of the challenges faced by the digital C-suite.
It’s the support that goes into the granular with any organisation, allowing for the business and the teams to create a data culture on a strong foundation of data literacy and visibility.
The BCX skillset and technology repertoire ensures the organisation can effectively build data value with the right levels of scalability and with AI, machine learning and intelligent toolsets optimising processes and streamlining data capability.
