Data teams are tasked with the crucial responsibility of transforming raw data into valuable insights, a process that directly influences business outcomes. However, as is the case with any complex domain, data management comes with its own set of unique challenges — which range from managing data pipelines to justifying budgets — that can limit the productivity and effectiveness of data teams.
This article aims to shed light on these challenges that data teams commonly face and offers insights into the practices of successful data teams. We will delve deep into how successful data teams manage to navigate through these challenges and unlock value from their data. The ultimate goal is to enable data teams to transform their processes, align with strategic goals, and truly become catalysts of value within their organizations.
The Challenges Data Teams Encounter
Data teams across industries and organizations are as diverse as the challenges they face. While some obstacles are specific to certain environments or business models, there are common challenges that many data teams confront. These shared hurdles provide a valuable lens through which we can analyze and propose solutions for more effective data management. The following overview of the key challenges should resonate with most data teams:
Articulating Impact and Consumer Value: Inside Out vs. Outside In
The value proposition of data projects is not always immediately apparent, especially when the projects involve complex transformations and computations. This can make it difficult for data teams to convey the importance of their work, particularly when the outcomes are not directly linked to tangible business results. A paradigm shift is needed—from an ‘inside out’ perspective, which is primarily concerned with technical outcomes, to an ‘outside in’ view that prioritizes the value perceived and appreciated by the consumers of the data.
Unable to Keep Pace with Demand: Speed of Data Product Delivery
The digital transformation wave has precipitated a surge in demand for data products. But as demand surges, data teams often struggle to accelerate their operations in parallel. This lag leads to accumulating backlogs and project delays, which can frustrate stakeholders and hamper business agility.
Unable to Justify Budget: Cost Management
Allocating budgets for data projects is no small feat. The intangible nature of data, along with the complexity of data projects, makes it challenging to quantify resources and costs accurately. When these costs balloon beyond initial estimates or when data teams cannot effectively justify their budget requirements, the resulting scenario can lead to financial stress, resource constraints, and potential cutbacks.
Data Chaos: 100’s or 1,000’s of Pipelines / Duplicate Pipelines
In many organizations, data teams manage an overwhelming number of data pipelines. This sheer volume can lead to duplication, inconsistency, and inefficiency, creating a chaotic data landscape. Not only does this chaos drain resources, but it also obscures the visibility into data flow and impacts the team’s overall efficiency.
Data Request Intake Process and Iterations: Effective Data Product Process
Striking a balance between the need for high-quality, valuable data products and efficient data request intake and iterative processes is an intricate challenge. On the one hand, data teams must maintain a high standard of data quality and relevancy. On the other, they must also streamline intake and iteration processes to ensure timely delivery. Achieving this balance requires effective process management and continuous optimization.
Multiple Data Product Point Solutions: End-to-end Data Platform
The absence of a unified, end-to-end data platform often results in fragmented data solutions. This fragmentation can hamper the overall effectiveness of data teams, create data silos, and complicate data management. To maximize efficiency and cohesion, data teams need an integrated platform that supports the entire data lifecycle—from ingestion and processing to analysis and visualization.
The Seven Traits of Successful Data Teams
Data teams that effectively navigate their challenges and consistently deliver value often exhibit certain common characteristics. These traits don’t merely address the pain points but also enable these teams to perform at their optimal best.
While every team will have a unique approach and organization-specific strategies, understanding these general principles can serve as a useful guide. The following is an in-depth look at the defining traits of successful data teams.
1. Strategic Alignment
Successful data teams do not work in isolation. They ensure their objectives and activities are closely intertwined with the broader business strategy. By aligning their work with the strategic direction of the organization, they ensure that their output – the insights gleaned from data – actively drives the achievement of organizational goals and objectives.
2. Data Knowledge
A deep and comprehensive understanding of their data landscape sets successful teams apart. They know their data’s structure, its quality, the areas of potential value extraction, and the best practices for handling it. This rich knowledge base enables them to squeeze maximum value out of their data and helps them become trusted advisors within the organization.
3. Continuous Integration & Deployment (CI/CD)
Adopting agile practices such as Continuous Integration and Continuous Deployment (CI/CD) is a key characteristic of successful data teams. These methodologies allow for rapid, reliable, and efficient delivery of data products. By maintaining a steady and consistent pace of development, they can meet growing business demands and ensure operational agility.
4. Cost Management
Effective cost management is an important aspect of successful data teams’ operations. They employ systems and mechanisms to break down costs, trace them to their origins, and understand their distribution. This granular visibility into costs enables them to justify their budgets effectively, manage resources efficiently, and optimize their return on investment.
5. Data Governance
Successful data teams invest in robust data governance, including the implementation of a comprehensive data catalog. This ensures transparency, control, and compliance across data management processes. A well-governed data environment eliminates knowledge silos, improves data discovery, streamlines change requests, and engenders a culture where data responsibility is shared across the organization.
6. Modern Efficient Data Pipelines
Efficiency is key when it comes to managing data pipelines. Successful teams leverage modern methodologies to build and maintain these pipelines. They adopt a declarative approach over an imperative one, focusing on ‘what’ needs to be achieved rather than the ‘how’. This shift results in intelligent data pipelines that reduce workload, optimize data flow, and increase overall efficiency.
7. Self-Serve Last Mile
Lastly, successful data teams democratize data access. They empower end-users – including data analysts, data scientists, and BI developers – with self-service data tools. This approach not only removes bottlenecks and accelerates data-related tasks, but it also fosters a culture of data-driven decision-making. It ensures that those who need data the most have direct access to it, enhancing the quality of insights and the speed of decision-making.
Achieving Data Excellence: A Roadmap for Success and the Ascend.io Advantage
Navigating the intricate labyrinth of data management can be an arduous journey, with hurdles and challenges at every turn. However, the road to data success is not uncharted. By studying and emulating the strategies and practices of successful data teams, organizations can rise above these challenges and fully harness the power of their data.
Now, imagine having a seasoned guide, ready to lead your organization through each milestone on your journey towards data team success. That’s where we come in. As a data pipeline automation platform, we enable data teams to build intelligent data pipelines 10x faster at 67% less cost. We consolidate the modern data stack tools — data ingestion, transformation, sharing, and orchestration — and detect and propagate changes across your data ecosystem —, boosting productivity and confidence in your pipelines.
The data landscape may be complex, but with the right guidance, determination, and tools, your organization can turn challenges into opportunities and thrive in this data-driven era. Embark on your journey to data excellence with Ascend.io, and experience the transformative power of data done right.
