As business intelligence increasingly becomes the cornerstone of decision-making, your data team has likely already taken on a more central role within your organization. However, this isn’t an entirely comfortable transition for many teams. The rapid evolution of the technological capabilities at your fingertips has created various internal challenges for data teams.
These growing pains are difficult to combat, largely because you’re forced to adapt at a time when you’re already stretched thin by an overwhelming workload; that said, it can be done. More specifically, there are three restructured approaches to leveraging data you can implement to improve the overall efficacy of your team.
But first — let’s look into why you should consider restructuring your team now.
Why you should reform your data team now
Before you engage in any significant transformation, you first need to nail down why now is the time to make a change. After all, your data team has likely been burning the candle at both ends for quite a few years now. Well, the answer rests at the intersection of several different factors, so let’s break them down.
First and foremost, the growth of the global big data analytics market size is projected to expand from $307.52 billion in 2023 to $745.15 billion by 2030 — which suggests that your investments in the sector today will yield much greater future returns. However, that being said, only 44% of data and analytics executives reported that their team is effectively providing business value.
The fact that over half of executives question the value of such a fundamental aspect of modern business decision-making suggests that widespread data team processes are missing the mark. So, the need to reimagine your workflows to improve your team’s output and match the inherent potential business value that data analytics possesses is apparent.
On its own, this might be reason enough for you to take on the task of restructuring your team. However, this isn’t the only reason you have to act now. In fact, perhaps the most compelling and urgent reason to alter your approach is the emergence of new artificial intelligence modeling technologies that rapidly make complex analytics approachable to a wide range of professionals. The implications of this change are vast and require a completely different workflow to harness optimally.
Furthermore, as the speed of innovation begins to snowball, it becomes ever more urgent that you stay at the cutting edge of available solutions to outpace competitors — especially if you’re in an oversaturated market.
And finally, as the year ends, it’s time to set annual business goals and target metrics for 2024. So, what better time to review and remake your data team to meet the demands of the following year?
To this end, let’s go through the three strategic approaches to restructuring your modern data team.
1. Modernize your legacy team
Professionals who work in the digital world know better than anyone how inefficient it is to operate with defunct technology. You’re likely well-acquainted with the drawbacks of legacy systems. However, to truly grasp how to restructure your team according to modern standards, you first need to understand how a workforce can best leverage new technologies — and this requires taking a deep dive into the business impacts of the most important data solutions.
The business value of AI is projected to reach $810 billion in 2025 — a staggering figure considering that in 2017, it was priced at zero dollars. This shows that digital improvements are picking up speed, and because of the rapid pace of innovation, companies that don’t adopt new technologies quickly can easily fall behind.
As a result, many enterprises are already shifting their focus to filling skill gaps for emerging technologies. In fact, according to a recent survey, 25% of executives rated data scientists as their top two needed skills to fill in AI skills gaps. However, at the moment, the rampant difficulties companies are facing with talent acquisition are so widespread that an estimated 73% of leaders are feeling burnt out as a direct result.
Luckily, the pressing need for data scientists is a lingering symptom of an outdated approach to data and analytics. Of course, this might be a surprising statement to many data team leaders; however, this is largely because solutions such as Pecan AI are still new to the scene.
Pecan AI empowers non-specialists to engage with predictive analytics and machine learning models for their purposes. This means that your company’s marketing or product teams could create their own AI models without the help of a data scientist. The tool is sophisticated enough to, for example, build a customer churn prediction model for your in-house marketing data analyst. And, as a result, your team’s workload can be significantly eased.
They’ll have time to take charge and leverage their skills in uniquely valuable ways to drive success rather than endlessly generating reports. So, in this way, the adoption of cutting-edge solutions is the key to optimizing the workflows of your modern data team.
2. Develop a data-driven culture
A recent survey found that while 97% of organizations are investing in data initiatives, only 19.3% of them report having achieved a data-driven culture. Of course, it’s difficult to completely overhaul your company culture — but the growing importance of business intelligence across departments requires a well-thought-out attempt, at minimum.
Once a luxury, business intelligence has now become a necessity. But with this burgeoning need comes the challenge of fostering a genuine data-driven culture and upskilling diverse teams to meet the ever-increasing demands.
This means you’ll need to foster a focus on data that’s ubiquitous across all teams and individuals. In doing so, you’ll ensure that data’s influence is pervasive and not just limited to top-tier decisions.
Of course, this may mean dispersing the skilled workforce currently contained entirely within your centralized team. Or, in simple terms, you’ll need to move or hire skilled workers on marketing, sales, and administrative teams to address their specific data needs. Then, with the integration of accessible AI tools like Pecan, your departments can leverage more granular business intelligence to guide policies and workflows. Plus, analysts who are more involved in the specific workflows and objectives of their teams will be able to implement more creative and unique solutions. And as a result of this effort, you’ll create a data-driven culture.
Plus, decentralization will relieve the immense pressure your currently centralized data team is working under and allow for a greater volume of data to be processed. Essentially, you’ll be able to eliminate the data bottlenecks that occur when your team is attempting to grapple with managing vast information sets, extracting meaningful insights, and aligning with ever-evolving data trends.
3. Nail down a decentralized modern data team structure
While decentralizing with the help of tools such as Pecan AI is a critical step for developing a data-driven culture, it’s also essential you maintain a responsive, structured, and agile approach organization-wide. So, to this end, you’ll need to account for a few common challenges that may present themselves as the volume of data you handle grows and the processing of business logic becomes dispersed.
Data silos are one of the most pervasive challenges organizations face across sectors. Customer data remains siloed in 28% of companies, which can generate isolated insights that don’t lead to comprehensive growth. However, the key to avoiding this in a decentralized structure is to ensure that data flows seamlessly across departments. This way, you can leverage holistic business intelligence for essential functions like customer retention.
Collaboration and lines of communication also need to remain robust in a decentralized data processing structure. It’s important to be intentional about setting up and maintaining a cohesive system between distinct teams.
Although, at the same time, decentralized data processing presents some challenges to overcome, the benefits far outweigh the drawbacks. For instance, decentralized teams can promote better data governance by establishing standards that are consistent yet tailored to individual department needs. The rising emphasis on the Chief Data Officer’s role reflects the growing importance of data governance, with the focus on this practice increasing from 29.1% in 2022 to 33.3% in 2023.
Finally, the benefits of decentralization extended to the decision-making process. Decentralized teams can make quicker, autonomous decisions, given their closer alignment with department-specific goals. This accelerates the decision-making process and makes reporting more tailored and relevant.
Finding technology that generates business value
The bottom line is that the emergence of new technologies is a chance to adapt your organizational structure — first to accommodate greater technical capacity and potential but also to resolve the growing workload your data team is trying to manage now.
If you restructure now, your workforce has time to adjust to a data-driven approach across teams before competitors take on this challenge themselves. So, with this in mind, the first step is to source the technology you need.
Pecan AI enables data teams to seamlessly integrate AI modeling, shifting from reactive analytics to proactive planning and strategy formulation. It’s the sophisticated engine that makes data transformation accessible.
Reach out to learn more about Pecan AI’s advanced predictive modeling now.