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Survey: You're Not Getting the Most Out of Your Data Analysts  
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Survey: You're Not Getting the Most Out of Your Data Analysts  

Survey: You're Not Getting the Most Out of Your Data Analysts  

Data analysts are a critical resource, but often face challenges and blockers to timelines.

By Natalie Waller, August 5, 2021

Data analysts are stretched thin and businesses are not getting the most out of them. 

That’s the key takeaway from a recent Fivetran survey targeting analyst and BI job titles. 71 percent of analysts surveyed are working on four or more different types of projects, and often across multiple use cases such as marketing, product, customer success, sales and finance analytics. Only 20 percent of survey respondents reported being able to focus on one or two analytics use cases.

Analysts play an integral role in the success and decision-making of most departments. They provide dashboards and reports that enable managers to make data-led decisions. Without reporting from data teams, managers are left to make decisions based on instinct instead of seeing the whole picture. 

 Data cleanup was the top-ranked project data analysts said they would like to take on if they had more time. This indicates a lack of confidence in their current processes, because surveyed analysts estimate their data is 79 percent up to date — but only 71 percent accurate. Other priorities include showcasing the impact of data analytics and building more sophisticated reports. 

 Conducted in May, the Fivetran survey explored the working lives of data analysts and was sent to data professionals across the globe. We received responses from ~300 analysts across seniority levels up to VP/department lead. 

Responses were split across different company sizes, with 36 percent coming from smaller companies (1-199 employees), 43 percent from medium-sized companies (200-1999 employees), and 21 percent from larger enterprise companies (2,000+ employees). In addition, responses came from analysts working in many different industries, including SaaS, Manufacturing, Transportation, Construction, Healthcare, ecommerce and Retail. 

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Data Teams Run Lean

The majority of survey respondents, across all different company sizes and industries, say their teams operate with five or fewer full-time employees. However, looking only at the larger enterprise companies, only 27 percent of respondents reported having groups of more than ten employees. 

As companies expand, analysts are managing more data and supporting the data needs of larger departments with a greater volume of projects. 

Data Analysts Face Challenges and Blockers to Timelines

The surveyed analysts estimate they spend more than half of their time on tasks outside of analyzing data and uncovering insights. As data analysts work hard to support the success of their organizations, they face challenges and blockers to their timelines. 

The top three are:

  1. Inadequate information to fulfill requests. Most analysts are fielding requests that require information that doesn’t exist or is not accessible.

  2. Last-minute or ad hoc requests. Rush projects have a negative effect on the output from data teams.

  3. Poorly defined requests or requirements. Analysts must spend additional time to define what is needed from the requestor. 

 Other challenges to analysts and their reporting are:

  • Data illiteracy. Data analysts are experiencing a dissonance between their understanding and their stakeholders’ understanding of the data and metrics they report on. Forty-nine percent of the analysts surveyed said their key metrics are identified by their stakeholders, where 24 percent said they’re defined based on industry standards and best practices. 

  • Unclear requests. Analysts receive unclear requests, resulting in additional time needed for coordination or time wasted on a project that is not valuable to the requestor. 

  • Too many requests. Analysts receive more requests than they have the bandwidth to complete. 

 Possible solutions include:

  • Data workshops. These can facilitate improved literacy, so stakeholders can understand the capabilities and limitations of the available data.

  • A standardized request process. Standardization can ensure all the pertinent information is included in the request the first time.

  • Self-service options. It’s possible to empower non-analyst employees to generate and analyze some data on their own. Self-service can lower the volume and frequency of the requests flowing to analysts and maximize analysts’ time spent on more meaningful and impactful projects.

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With Automation, Analysts Could Add New Data Sources Faster  

35 percent of the analysts surveyed say it takes eight or more hours for them to add a new data source. Of this group, nearly 80 percent say that they rely on internal or contracted engineering resources — as opposed to automated tools.

Data teams relying more heavily on automated resources — data extraction and consolidation software, for example — say it takes them far less time to add data sources. Software and tools for automation are vital to data teams as they scale to manage increasingly large volumes of data.

Databases Top the List of Most Important Data Sources

When asked to rank their most important data sources, analysts responded that database sources are the most important, regardless of their top analytics priorities or goals. 

Customer relationship management (CRM) platforms ranked number two in importance, except for analysts with a marketing analytics focus, who ranked ad platforms number two. 

Customer support platforms and enterprise resource planning (ERP) platforms ranked high in importance, especially for sales and product analytics. 

Analysts Are Stretched Thin — but Solutions Are Available

The survey data confirm that data analysts are valued and appreciated within their organizations — but they could use some help. Data professionals empower multiple departments at almost every organization to make data-led decisions, measure their success, and be data-driven. 

If data professionals were afforded more time, they could perform valuable operations like improving data quality and showcase the impact of data analytics. They are stretched thin — but we have solutions that are available to help them.  

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