From Roadblocks to Breakthroughs: A Guide for Data Leader Challenges

By Susannah Barnes

Published on May 8, 2024

From Roadblocks to Breakthroughs: A Guide for Data Leader Challenges

As a data leader, your ultimate goal is to use the data you have to drive strategic results for your organization. But despite the growing importance of using data to make informed business decisions, many data leaders struggle to deliver strategic outcomes.

Why is that the case?

This article explores the key reasons for inefficient data leadership and offers strategic solutions for overcoming them. Let’s get started!

Top obstacles to data success

Data stands out as a key tool for impactful decision-making. According to Salesforce’s global survey of almost 10,000 business leaders, 73% of the respondents agree that data accelerates decision-making.

Poll asking "To what extent do you agree that date does the following?"

Image via Salesforce

Besides accelerating decision-making, most respondents agreed that data builds trust, minimizes the influence of personal opinions on business decisions, and keeps people focused on the things that matter.

Although business leaders agree that data is important, there’s a noticeable gap in how they are leveraging it in their businesses. For example, 67% are not using data to price their products effectively and build a customer centric business.

Poll asking "Which of the following does/would your organization use data for?"

Image via Salesforce

Furthermore, 71% of business leaders don’t use data when expanding into new markets. Small wonder, then, that less than half (44%) of data and analytics leaders reported that their team effectively provides value to their organization, according to Gartner research.

If so many companies aren’t using data for strategic decisions, it stands to reason that their data initiatives are failing. Understanding the reasons for failure can help you avoid such pitfalls and ensure that data initiatives are successful.

Below are the top reasons data leaders struggle to deliver strategic outcomes and the possible solutions to overcome these challenges.

Skill and staff shortages

According to Gartner research, skill and staff shortages are key reasons data leaders struggle to deliver strategic outcomes; 17% of chief data and analytics officers said the lack of quality talent is the top impediment to data and analytics success, while 39% ranked it among the top three.

Figure1: Top Roadblocks to the Success of D&A Initiatives

Image via Gartner

Given the complexity of demands placed on data and analytics teams, it's understandable why talent shortage is the main obstacle to success. The lack of qualified talent makes it difficult for data leaders to harness the full potential of data to drive strategic results. The issue is further aggravated by a tight talent pool that increases competition for skilled data professionals.

That’s why your talent strategy shouldn’t just focus on hiring ready-made talent. Instead, focus on education and training to enhance your staff's data literacy.

When you hire staff, invest in their training and development to help them excel in data roles in your organization. This training need not be technical upskilling alone. Rather, leaders should take care to instill a data and analytic mindset as the standard way to approach problem solving. You should also prioritize continuous learning to upskill your staff and empower them to take responsibility for delivering data-informed insights and identifying data opportunities. This is a worthy investment, as it will help you ensure the data professionals in your team have the necessary skills to manage your data effectively and deliver strategic outcomes.

Lack of resources

Resource constraints can significantly limit the ability of data leaders to deliver strategic outcomes with their data initiatives.

According to the Gartner study mentioned above, 29% of data leaders rank the lack of resources (including talent) among their top three challenges with 13% citing it as their top hurdle.

Inadequate resources often force data teams to use outdated or inadequate tools, hindering their ability to analyze data effectively to generate meaningful insights. This can lead to manual processes that delay delivery timelines and frustrate stakeholders, damaging trust between data teams and business data users.

Resource limitations also affect the recruitment and retention of skilled data professionals. As mentioned earlier, the lack of skilled talent makes it difficult for data teams to harness the full potential of their data. This affects the quality of the data analysis and strategic decisions informed by this data.

The solution to this challenge is to identify and prioritize the areas of investment. This will help ensure your resources are allocated to the most impactful data areas to drive strategic outcomes for your company.

You should also strive to gain maximum value from your existing resources. Train your staff to identify ways to operate more efficiently and derive maximum value from your current tools, ensuring the full capability of your current tech stack is used. This may require initial investments in process improvement training and continued investments in technology training, but the return on the investment is worth it. You gain operational efficiency and ensure consistent delivery practices amongst your data teams, and you also demonstrate a commitment to providing your teams with continuous learning opportunities. Both are important to maintain an engaged workforce and to attract new talent.

Poor analytics strategy

You can only make sense of your data if you have a robust analytics strategy. Many data leaders have prioritized the data collection part of delivery when defining a data strategy, leaving the more critical data analysis strategy to be defined later.

Without a clearly defined analytics strategy as the foundation for your data initiatives, you might struggle to deliver strategic outcomes since it leaves you without a clear definition of success.

Data initiative challenges arise from a few key issues that, once solved, can help you uncover the true value of your data. They are:

Too much data

With the advent of big data, organizations can be overwhelmed by the amount of data they collect.

While it’s important to collect data about every incident and interaction within your organization, be careful not to overwhelm your analysts with unnecessary information. Make sure you only select the most relevant, high-quality data that can answer the kind of questions outlined in your analytics strategy.

Data from various sources

The other challenge with data analytics is dealing with data from the many sources in a complex data ecosystem. Analysts may not know which data sources are appropriate to use for their analysis. This could lead to inaccurate or incomplete analysis and it could lead to inconsistency in the findings of teams and individual analysts.

Inaccurate data

Nothing is more detrimental to data analytics than inaccurate data. Accurate and reliable analytics require good data quality. Manual data entry and collection processes are some of the primary causes of inaccurate data. This can have significant consequences for your business when the data is analyzed for decision-making. Implementing automated data collection and data validation processes within your organization can minimize the impact of human error and will also improve operational efficiency.

Weak data literacy

If your workers are uncomfortable working with data or do not have a data mindset, it will be difficult to deliver on your analytics strategy. Data analytics tools can simplify your data management efforts. However, your staff still needs the skills and training to use the tools effectively and to identify opportunities for using data for decision-making.

Adequate training will help your staff interpret and handle your data effectively. When you acquire a new system, be sure to offer comprehensive onboarding so your staff has the knowledge they need to use the new systems effectively to deliver your data and analytics outcomes.

This proactive approach will support your analytics strategy and ensure that data becomes a valuable asset for your business.

Poor data governance

Data governance creates a system for trusted data; it can be defined as the actions you take to ensure your data is accurate, accessible, private, secure, and usable. Proper data governance will give firms a competitive advantage by empowering data users to find, understand, and leverage data ethically and efficiently.

When data leaders struggle to deliver strategic outcomes, it's often because of poor data governance. Poorly implemented data governance can lead to negative outcomes like:

  • Decreased data quality: When data governance fails, the result is often poor quality data that is incomplete, inaccurate, or inconsistent. Such data can lead to faulty analyses and misguided decision-making. 

  • Compliance failures: Poor data governance practices can result in non-compliance with existing data regulations. This can lead to severe penalties and legal complications that damage your organization’s reputation. 

  • Security breaches: Failure to uphold the highest levels of data governance can lead to security breaches that put your customer data at risk. These can have serious financial implications for your organization through lawsuits and significant reputational damage.

  • Misuse of resources: Without proper data governance, you can easily misuse or overuse your data, which can result in unnecessary costs, such as maintaining redundant data in your systems.

Lack of support from senior leadership

Data management success requires strong support from the top leadership. Without buy-in from executives, data leaders may struggle to deliver strategic outcomes from their data initiatives.

This is because the executive leadership sets the direction for the organization. When they don’t see the need to prioritize data, it can lead to misalignments between data initiatives and the organization's business objectives. The lack of support could also lead to budget constraints that may hinder a data leader’s ability to invest in their team’s development and the right data tools and infrastructure.

Furthermore, the absence of executive support can result in understaffed data management teams, limiting their ability to handle all the data they collect.

To generate strong support from executives for a data and analytics program, you must directly tie the strategic outcomes of the data initiative to the strategic business outcomes of the broader organization. Buy-in for the program will directly result from how well you can create a compelling narrative that underscores the critical role data plays in your organization’s success.

By engaging in transparent conversations with the executives about the current state of data in your organization and the direct correlation between data initiatives and the company’s strategic vision, you can win their support for your program.

Final thoughts

Effective data management is crucial for achieving strategic results that propel businesses forward and ensure long-term success. However, many data leaders struggle to deliver strategic outcomes for their organizations.

By understanding the reasons for their failure, you can overcome the challenges that prevent your business from delivering strategic results with your data initiatives.

Take initiative and implement the right strategy to transform your data initiatives into a powerful tool for driving business success. Get started with Alation today.

  • Top obstacles to data success
  • Skill and staff shortages
  • Lack of resources
  • Poor analytics strategy
  • Lack of support from senior leadership
  • Final thoughts
Tagged with