What are the common hurdles encountered when putting analytics to work in a business, both in developing analytical models and applications and in building enterprise analytical capability?

This question is central to IIA’s mission of helping organizations navigate the many challenges to achieving analytics maturity.

Succeeding with analytics, and sustaining that success, is complex. It requires both a multi-pronged approach and an awareness of the pitfalls that analytics leaders and teams often face along the path toward increased analytics maturity.

In the final article of this series, let’s explore overcoming people obstacles for data and analytics success.   

1. Staffing: Still Not a New Normal

The question of whether organizations possess the necessary analytics professionals to leverage business opportunities and remain competitive persists, given the longstanding shortage in this field. With fierce competition for talent, attracting staff becomes challenging, although the shift to remote or hybrid work allows for recruitment across less desirable geographies. Despite the proliferation of university and non-university training programs producing analysts and data scientists, many roles within data and analytics remain ill-defined. While the role of the chief data officer is gaining clarity, tenure remains short, prompting consideration for expanded responsibilities encompassing analytics. The emergence of chief AI officers, often recruited externally, reflects evolving organizational needs and aspirations within the data and analytics landscape.

Warning Signs:

Some of the common warning signs in this area include:

• Resistance from HR in increasing the velocity of the hiring process, especially for entry level roles
• Inability to rotate staff through different roles to provide opportunities for new learning
• Talk within the company of fully returning to “how things used to be”
• Becoming too aggressive with hiring and staffing ahead of demand

Overcoming Talent Obstacles:

Consider some ways to navigate the new normal:

• Broaden candidate pool: Expand recruitment efforts beyond traditional areas to counter recruitment by organizations outside your region.
• Communicate lifestyle benefits: Highlight lifestyle advantages of locations beyond major tech hubs to attract talent to non-coastal areas.
• Flexible work policies: Emphasize organizational flexibility in work arrangements to accommodate diverse employee needs and optimize productivity.

2. Business Talent: Lack of Data Literacy

The symbiotic relationship between technical analytics development and business utilization underscores the heightened emphasis on data literacy. Recognizing that literacy demands effort from both technical and business realms, analytics teams must simplify and convey insights while business teams must effectively apply them. Amidst discussions of talent shortages, the demand for business-side proficiency in analytics consumption is equally pressing. Bridging these gaps in literacy becomes paramount, especially with the proliferation of AI tools. Mature analytics organizations adopt self-service models, empowering business units with analytics proficiency to drive sustained growth and self-sufficiency.

Analytics organizations must remember that businesspeople may need help with:

• Data awareness: paying attention to available data, including its quality and sufficiency and fit-for-purpose, and exploring what it contains.
• Decision awareness: paying attention to one’s decision-making methods and the extent to which decisions can be helped with analytics.
• Self-sufficiency: being able to use business intelligence resources and visualization tools to explore data and perform basic analysis via self-service.

Warning Signs:

Some of the common warning signs in this area include:

• Analytical teams commonly complain about the business team’s lack of readiness to make use of analytical output
• Business teams commonly complain about the analytics team’s lack of ability to explain what they’ve found and how to make use of it
• There is no formal data literacy program in place to address the prior two concerns, and also to assuage risk from upcoming regulations

Overcoming Data Literacy Obstacles:

Achieving a high level of corporate data literacy requires commitment and effort from everyone.

• Be specific about what businesspeople in specific roles need to understand and can do with analytics.
• Set development goals, provide training, and offer ongoing support. Businesspeople should effectively “minor” in analytics.
• Encourage analytics professionals working “in the field” to embrace the roles of teacher, advisor, coach or “personal analytics trainer” for business colleagues.
• Set development goals and provide training to the analytical team regarding how to best distill and communicate the details of the technical work they do and the results they find.

Having one side of the equation handled isn’t enough. Ensure that both the analytics and business teams are aware of their responsibilities as it relates to data literacy. Also ensure that they are aware of the efforts being made by their counterparts so that they understand that it is truly a two-way street.

3. Human Network: Difficulty for Analytics Professionals to Build Connections

With many organizations operating in a hybrid environment, many professionals face feelings of disconnection and dissatisfaction. For analytics practitioners, immersion in the business is crucial, yet challenging without informal networks. Business leaders often fail to promote analytical management enterprise-wide or provide adequate support for analytics processes. Additionally, remote early-career employees receive less feedback, hindering their growth. To address these issues, professionals must actively engage in business immersion, leaders must promote analytical practices, and ongoing feedback mechanisms must be established to support remote employees' development.

Warning Signs:

Some of the common warning signs in this area include:

• People are unable to recall the last time they met other critical team members or partners face-to-face
• There are multiple stakeholders across the business and analytics teams who are new and have never met other stakeholders at all
• There are concerns raised about not truly knowing, or having a connection with, others involved with corporate analytical processes

Overcoming Networking Obstacles:

Due to the lengthy gap in interaction due to COVID, it will require some explicit focus and encouragement to nudge employees to interact and form personal relationships again.

• Reestablish the corporate analytics community that encompasses stakeholders from all aspects of analytical planning, development, and deployment. This includes business, analytics, and IT at a minimum.
• Encourage people to meet for lunch, have coffee, or get together for a happy hour after work. Enabling people to get to know one another will make working together on analytics initiatives easier due to the level of comfort people will have with each other.
• Be clear about expectations and intentionally offer feedback: A remote/hybrid workforce places a greater need on management to set expectations and provide information feedback so that junior employees can grow.

Be especially mindful to focus on new or young employees who have little historical knowledge of the corporate culture or the individual personalities of those within the analytics community. These employees have a distinct disadvantage that needs to be addressed proactively to bring them more fully into the fold.