Maneuvering the Global Economic Crisis: Top Priorities for CDOs in 2023
The past year has been a roller coaster ride for organizations across the globe as leaders and the workforce struggled to adapt to new working norms and conditions introduced at the back of the pandemic. While it appeared that we were nearing the light at the end of the tunnel as the COVID-19 situation improved, we now find ourselves stuck in another perfect storm, with the global economy taking a turn for the worse.
With an economic recession on the horizon and so much pressure to innovate, it can be challenging for leaders to determine what a smart investment is and why. But what is clear is that achieving decision accuracy and integrating siloed and distributed data sets to see the big picture in real-time will be vital to survival and future success.
That’s why we’ve outlined five key trends that Chief Data Officers in every data-driven enterprise should act upon as we enter the new year.
1. Embedding AI in front of the data pipeline
Many will see a pullback in investment and hiring as economic uncertainty continues. However, with the global skills shortage continuing to impact companies of all sizes, ensuring technologies such as Artificial Intelligence (AI) and Machine Learning (ML) can automate some of the more menial data preparation tasks will be crucial. By moving AI deeper into the data pipeline before an application or dashboard has even been built, we can finally shift the breakdown of time spent on data preparation versus data analytics.
Right now, less than 20% of the time is spent analyzing data, while just over 80% of the time is spent collectively on searching for, preparing, and governing the appropriate data. Doing so would enable hard-to-come-by data talent to focus on value-added work, cross-pollinating, and generating new insights that weren’t possible before. A far more productive use of their time.
2. X fabric holds connected data government together
Investment in data and analytics has dramatically accelerated thanks to the pandemic, and will continue to do so with 93% of companies indicating they plan to continue to increase budgets in these areas. But rapidly shifting rules and regulations around privacy, as well as the distribution, diversity and dynamics of data is holding back organizations’ abilities to squeeze the best competitive edge out of it. That becomes especially challenging in a fragmented world as data governance becomes even more complex.
Improving access, real-time movement, and advanced transformation of data between sources and systems across the enterprise is crucial to organizations realizing the full power of data. That is why an increasing number of enterprises are turning to data control plane architecture, an “X-fabric” not just for your data but also for your applications, BI dashboards and algorithms, enabled by catalogues and cloud data integration solutions. That is a critical component in the modern distributed environment for any organization that wants to act with certainty.
3. Real-time data key to alleviating supply-chain disruptions
The aftershocks of COVID-19 and continued global conflicts are still compromising supply chains. Anyone who has attempted to buy a new car (a computer, or even something as basic as toilet paper) in the last few years knows how seriously supply chains have been impaired. Things show no sign of abating over the next few years, and so too does the need to react quickly, or ideally “pre-act” to forecast issues before they even start.
Having the power to analyse data in real-time and in context is key to this. It’s no wonder that IDC predicts that by 2027 sixty percent of data capture and movement tech spending will be about enabling real-time simulation, optimisation, and recommendation capabilities.
4. Growing importance in derivative and synthetic data
If the last few years have taught us anything, it’s the value of investing time and resources into risk prediction and management. Unfortunately, before COVID-19 there wasn’t enough real data on pandemics readily available to the average operation to prepare for such a crisis, but this is precisely where synthetic data plugs the gap.
Research suggests that models trained on synthetic data can be more accurate than others; and of course, it eliminates some of the privacy, copyright, and ethical concerns associated with the real. Whilst derivative data allows us to repurpose data for multiple needs and enables crucial scenario planning to prepare for future issues and crises.
5. Rising penetration of AI across global industries
Many organisations have been using language AI in its basic form for some time. Think about how often you’ve interacted with a customer support chatbot to resolve issues with your bank or insurance provider. The popularity of this technology is set to grow at around 18% for the next few years; but also evolve dramatically. There are several new models in development that are significantly more powerful than anything we use today.
Where those will take us, we can only imagine but what we do know is that natural language capabilities will have huge implications for how we query information and how it’s interpreted and reported. We’ll find not only the data we’re looking for but also the data we hadn’t thought to ask about. That's why enterprises need to capitalise on this.
The good news is that after the last few years, we’re all better prepared to roll with the punches than ever before. As data and analytics professionals, we need to adjust to more fragmentation, with disparate data centers, disrupted supply chains, the consistent need for innovation, and hampered access to skilled labor. And in a world where crisis has become a constant, calibrating for it becomes a core competence – so we can react at the moment and anticipate what’s coming next.
Dan Sommer, senior director and Global Market Intelligence Lead at Qlik, wrote this article.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/AndreyPopov
