As the world grapples with shifting powers, technology revolutions and market peaks and troughs, businesses small or large are under constant pressure to evolve whilst keeping their lights running. With AI revolution being imminent, innovation continues to be on the radar for all organisations. Even though predictions for top line growth are under scrutiny, it’s getting equally important for businesses to keep their costs efficient. For organisations intending to move the needle leveraging AI, data operating model becomes a key lever to pull for nimble technological advancement.
Many organisations have old technology platforms, archetype modes of operations and redundant data processes. Australian Cyber Security Magazine claims that government organisations in Australia spend 80% of their costs on maintaining legacy systems.
Incumbent systems and legacy ERPs may be highly critical for organisations to respond to customer and stakeholder needs. However, as dependencies on these systems continue to increase, complexity of re-platforming systems continue to spike.
System knowledge gets limited to a selected few and integration to advanced technologies become challenging. System maintenance takes up a bulk of operational costs making it harder to capitalise on new projects.
These limitations continue to hinder processes, talent, and culture from evolution.
With Forbes highlighting the value of AI-based services to rise to 1 trillion by 2031, and AI being a reality for two third of the planet’s population, it becomes essential for businesses to review their operating structures and models to increase responsiveness.
A continued focus on enterprise’s technical capabilities, their interconnectedness to wider organisations and current data architecture needs to be brought to the forefront. Gaps between strategic objectives needing state-of-the art data capabilities to current limitations need to be addressed through architectural governance forums. If current architectural governance is inadequate, it can create obstacles to implementation of advanced technologies, particularly when AI is being adopted at an accelerated pace. This, in turn, can have a significant impact on growth and revenue.
Instead of creating long-term roadmaps, a steady shift in direction of strategic objectives leads to reduced strain in technological reform. This also mitigates risks of unexpected costs to attain the future state data architecture.
Employee motivation is essential to maintain organisation’s operating rhythm. Employee disengagement is observed when change is not on the horizon for long periods of time causing complacency and lack of motivation to shift perspectives. This thinking may continue to challenge new modes of operations until change agents influence the behaviour through employee involvement and inclusion. Cultural shift needs attention to behavioural changes and measurement at specific points in time to gain traction.
Costs of employee engagement tend to increase over time. It therefore becomes critical for businesses to constantly review and refine talent management initiatives for alignment to strategic imperatives. AI operationalisation must include employee skills gap assessment and fulfilment.
Processes are fundamental not only for technological advancements but also in terms of daily employee engagements. Ability to adapt to advanced technologies needs a thorough review of existing processes. Interaction of employees with existing technical tools and methods for releasing data products provide a metric for technical productivity. This can be used as a lever to determine the pace at which organisation processes will adapt to new ways of working.
“Data operational excellence is fundamental to technological revolution”
Modes of operations between data and business form a key operational component. Proactive modes of communication, continuous alignment to business strategy and ability to fail-fast and learn-fast determine the success of data initiatives. Cultural alignment, clear roles and responsibilities, behaviours and governance are additional considerations to improve operational effectiveness.
A shift in paradigm with regards to customer experiences, engagement and retention leveraging AI are inevitable. Therefore, organisations need to remain vigilant of their data operating levers to be able to move at pace. Whilst these levers may differ based on organisation structure, footprint and jurisdictions, the ability to identify and leverage these at the right points in time will help being a market differentiator. Data operational excellence is fundamental to AI and technological revolution. Done right, this can enable businesses to be leaders in driving technology adoption.
Bhavisha has more than 16 years of experience in business, technology, data, and advanced analytics. Bhavisha has worked with organisations across industries to develop data strategies and execute large-scale transformations.
https://www.linkedin.com/in/bhavishasharma/
Note: Ideas articulated in this article are author’s own views and are not to be considered for professional investment advice.
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