Ipsos Data Council Report
Future data plans High hopes meet barriers of cost, regulation, and skills shortages
Ipsos Data Council Report
Future data plans High hopes meet barriers of cost, regulation, and skills shortages
Ipsos Data Council Report
Future data plans High hopes meet barriers of cost, regulation, and skills shortages
Context
An emerging and major hurdle in realising the full potential of data is the financial cost of data management and AI solutions. Balancing these costs with the need to show tangible ROI will prove essential and, without measurable results, securing ongoing investment is likely to be difficult.
Another challenge is the perception of data as a long-term goal, rather than an immediate priority. Treating data as a future aspiration could lead to delays and as competition increases, businesses that don’t prioritise data may fall behind their more data-driven competitors.
Additionally, as new data privacy laws emerge, businesses will need to remain agile to comply, adding complexity to data management and AI adoption. Data silos may continue to impede progress, with teams managing their own data, leading to inefficiencies and limiting the potential of AI and automation.
Finally, the skills gap is likely to remain a barrier. The demand for qualified data scientists and AI professionals may continue to outpace supply, meaning organisations could need to prioritise upskilling and reskilling their workforce to stay competitive.
Key challenges from the Data Council
Looking ahead, many organisations are optimistic about the potential of data and AI to drive future-focused insights. As one interviewee noted:
“I would hope that we would get to a point where we are looking more at leading indicators and predictive modelling. Both predictive analytics, but also the tools and ability for people to scenario plan in a way that they don't today.” Senior Director, Global Business & Sales Operations, multinational technology company
While the tools and infrastructure may exist, our council members shared the scale of work to do in aligning them in a way that unlocks their full potential. As one of our senior stakeholders commented:
“I would expect us to have more standardisation, more automation and be able to easier get to the insights that we need.” Head of Global Digital Commerce Performance, multinational alcoholic beverage company
Perhaps the most pressing barrier that we heard about, though, is the ongoing shortage of skilled professionals, particularly in fields like data science and AI.
As one interviewee explained:
Businesses that don’t prioritise data may fall behind their more data-driven competitors
“There are a finite number of people that have tools and all sorts of coding expertise that goes straight into data sets and extracts and then plays back, but the problem I have is it is not democratised.” Customer Lifecycle Manager, multinational banking organisation
Ipsos Data Labs learnings
While there is significant enthusiasm for the future of data, organisations must approach the journey with honesty, open-mindedness and a long-term commitment to making data a core element of their future. The excitement around data and AI is understandable, but for true transformation, organisations need to recognise that there is a right way, and a wrong way, to think about data. Consider what is working, what is not and where resources need to be allocated. It will likely require revisiting core structures, both physical and cultural.
Organisational processes may need to be reassessed to ensure they align with the new reality of data-driven decision-making. This could involve rethinking long-standing practices, challenging assumptions, and exploring new ways of working. It’s about asking the difficult questions - are our current processes still fit for purpose? How can we ensure that everyone is aligned with the vision of where data can take us?
It’s vital to build a space where leaders and teams are open-minded and willing to take risks in how they approach data. Leaders must be prepared to admit that the old ways may no longer serve the organisation’s evolving needs.
But this willingness to change also requires accountability. Clear leadership must guide the organisation through the process - from setting a bold, long-term vision, to making decisions about investment and resource allocation.
The excitement around data and AI is understandable, but for true transformation, organisations need to recognise that there is a right way, and a wrong way, to think about data.
Key takeaways
Make smart investments
In order to futureproof, companies will have to overcome financial constraints by prioritising the most impactful projects and finding cost-effective solutions.
Change is the only constant
Staying alert and adaptable to an ever-changing environment when it comes to regulation, recruitment, customer needs, market forces, or any other external factor, will be more critical to success than protecting any long-standing internal policy.
Plan, but be ready to pivot
Start with a clear view of where you want to go, and identify the steps to get there. Then future gaze and identify potential risks and their mitigation plans. Any future looking plan needs to be agile enough to pivot to capitalise on opportunities and overcome unexpected hurdles.
Thought starters
Where should you start? Does your organisation first need to overcome financial, regulatory, or talent challenges to accelerate its data and AI adoption journey?
Who are the most critical stakeholders to win over?
When you look across your industry, what does best in class look like?
