Common Challenges Faced by Companies in Data Communication and Alignment

In the previous article, we explained why it is important to have effective communication and alignment between data professionals and business stakeholders. The impact of NOT having it may lead to missed opportunities, slow & inaccurate decision-making, inefficient resource allocation, lack of trust and credibility, and strained relationships & ineffective collaboration.

Now it's a good time to dive into the common challenges that often hinder this collaboration, which is much needed to cultivate a data-driven culture in any organization.

1 - Unclear or vague requirements: 

Articulating thoughts effectively is a skill that can be honed with practice. When it comes to data requests, it's important to recognize that not everyone possesses the innate ability to clearly communicate their needs. We often assume that business stakeholders, regardless of their level within the organization, will know precisely what they're looking for and can effectively convey their requirements. However, that's not always the case.

Having worked with data requests from various levels, including C-level executives, middle management, and individual contributors, across different functions within organizations for over a decade, I've observed that only a small percentage of requests are well-articulated, providing all the necessary information for data professionals to design suitable data solutions and deliver relevant insights. In most cases, data people find themselves spending a considerable amount of time and effort clarifying business requirements to gain a thorough understanding of business processes and data flows.

Without a comprehensive understanding of the business needs, there’s a high chance that the data output may not address the core business questions. Therefore, it's crucial for data professionals to proactively engage with stakeholders, ask the right questions, and ensure a clear and mutual understanding of the desired outcomes. By doing so, they can effectively bridge the gap between data and business needs, ultimately delivering valuable insights that drive informed decision-making.

2 - Misaligned expectations:

Vaguely defined projects can lead to different expectations among the involved parties. Business stakeholders may have different assumptions regarding the feasibility, timelines, or scope of data projects. It's quite common for requesters to anticipate quick turnarounds, high data accuracy, easy-to-interpret visualizations, relevant insights, and actionable plans. Each stakeholder tends to view their request as the most important, urgent, and impactful one.

However, given the increasing demand for data and insights, coupled with potential technical challenges and operational gaps, it becomes essential to assess the feasibility of requests before making any commitment. This involves determining what is realistically achievable, establishing reasonable timelines based on the company's priorities & resources, and considering how the project aligns with the responsibilities of other departments. Open communication is crucial to avoid misunderstandings and ensure that expectations are aligned before starting the project.

Failure to do so can lead to frustration and delays in delivering insights that truly meet the stakeholders' needs. By proactively engaging with stakeholders, managing expectations, and clearly communicating the possibilities and constraints, data professionals can set a solid foundation for successful projects that deliver actionable insights within a realistic timeframe.

3 - Data quality and accessibility: 

Incomplete or inconsistent data can significantly impact the accuracy and reliability of insights, undermining trust with business stakeholders. It's important to recognize that data quality issues are often symptomatic of underlying operational gaps. When numbers from different reports fail to align, business users tend to assume that the issues were caused by data teams. However, by diving deeper into the discrepancies, we often uncover a multitude of issues and gaps within the business itself. While these insights may not be glamorous, they hold the potential to be game-changers in improving operational efficiency.

Even after addressing data quality concerns by closing operational and business gaps, data accessibility can still be a challenge. Here are a couple of key obstacles:

Getting everyone on the same page:

• Data Silos: When each department keeps its data on separate servers or platforms, it becomes troublesome to access and share information across the organization. This lack of cohesion makes it difficult to gain a comprehensive understanding of the overall picture.
• Technical Complexity: Dealing with diverse data formats, structures, and storage locations can be a major headache. Navigating this technological maze successfully requires the right skills and tools to integrate the data effectively.
• Scalability and Performance: As organizations grow, so does the volume and variety of data being generated and received. To keep up with this influx, we also need robust systems that are capable of handling the load without compromising performance.

Navigating the rules:

• Data Governance and Security: Striking the right balance between data security and providing appropriate to employees is a delicate task. While safeguarding data from unauthorized access or breaches is essential, employees must still be able to obtain the necessary information when needed so that it doesn’t become a blocker for crucial decision-making.
• Regulatory Compliance: Different industries and regions have their own set of rules and regulations. Ensuring compliance while maintaining data accessibility often feels like maneuvering through a regulatory minefield.

Creating a more cohesive and accessible data environment requires a combination of technical expertise, effective data governance practices, and a commitment to keeping up to date with the changing regulations.

4 - Data literacy and interpretation: 

To fully leverage the insights provided by data professionals, businesses must enhance their understanding of data concepts and analytics techniques. Without this comprehension, there is a risk of misinterpreting the insights, ultimately limiting the impact and value derived from data analysis.

Even if an organization has access to a wealth of data, it doesn't guarantee that they know how to effectively utilize it. Generating relevant insights requires data professionals to possess a solid understanding of the business context. On the other side, it’s also necessary for business units to have a decent level of comprehension regarding how data is captured, measured, and analyzed to extract meaningful insights instead of overanalyzing or underanalyzing data.

Unfortunately, the investment in data literacy training is often overlooked. As "being data-driven" is assumed to be a given, this dangerous assumption can widen the skill gap over time and create more friction between data professionals and business stakeholders. Therefore, let’s recognize the importance of data literacy and proactively invest in training so we can foster more effective collaboration and maximize the value derived from data.

More to come in future chapters:

1. The Role of Data Professionals & Business Units in Bridging the Gap
2. How to understand business stakeholders, align data priorities with business objectives, and build relationships & trust
3. 8 Effective Communication Strategies
4. Overcoming obstacles & resistance and the power of mentorship & coaching