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Snow Fox DataJuly 21, 202313 min read

Getting Budget and Support for Your Data Analytics Transformation

A Swing and a Miss

Armed with a well-crafted presentation about the current state of data science and the latest trends in AI-powered analytics, Nick entered the conference room eager to get budget approval and support for his analytics initiative from the leadership team at AnyCorp.

Less than an hour later, Nick heard the words he hadn’t prepared himself for. “I’m sorry, Nick – we’re just not ready for this. There are too many unknowns and too much uncertainty in your ROI projections for such an ambitious effort. Maybe next year,” said the CEO of AnyCorp, as the leadership team gathered their things on their way out of the conference room.

Nick, a newly hired Data Analytics Leader at AnyCorp, couldn’t believe it. His presentation to the leadership team was so well-prepared.  Hadn’t he made a compelling case to get the budget approval he needed to build his organization’s advanced analytics capabilities?

He was convinced that the latest developments in AI would show his peers how advanced analytics would revolutionize their organization. Inspired by stories of other medium-sized companies using advanced analytics, Nick’s vision of AnyCorp using its data to drive innovation did not seem that far-fetched. AI integration into everyday living is a reality and no longer limited to science fiction fantasies or $100 Billion Enterprises. Nick was excited to show his peers that companies like AnyCorp are using machine learning, cloud computing, and MLOps to streamline operations. In fact, if a competitor like OtherCorp could use modern technology to improve decision-making, why couldn’t AnyCorp?

Data-Driven Initiatives Require Focused Change

Despite Nick’s best efforts to showcase the potential of AI-powered analytics, the leadership team remained skeptical.  Though they were intrigued by his demonstration of ChatGPT and the latest technology trends in AI, there was something else missing. Nick needed to approach his leadership team as a trusted advisor. Instead of showing a highlight reel of all the modern tools and technology available to perform advanced analytics, Nick’s leadership team needed him to explain the unique opportunities and challenges of data-driven initiatives that were relevant to AnyCorp.

To become data-driven and take advantage of AI technology, there’s no question that companies need modern tools to collect, store, manage, and analyze data. It’s also important for data leaders to remember that implementing new tools without evaluating existing processes and employee interactions with current technology will often result in inefficient workflows and poor adoption rates.

Experienced leadership teams – like the ones at AnyCorp – have learned over the years that technology alone cannot be a panacea for missed business opportunities. Data leaders – like Nick – can inadvertently lose sight of their primary purpose of helping the employees of their organizations understand the unique role of data in driving business success. By fixating on acquiring the latest technologies, data leaders can overlook the need for active involvement and engagement of people at all levels of the organization. Data leaders who drive sustainable business success are the ones who are able to shift their focus from technology alone to a more holistic approach that considers the people and process aspects of building a data-driven culture.

The Data Analytics Journey Starts With Identifying Business Value

For many data leaders today, a version of Nick’s story is playing out in their own organizations. It’s easy to find areas where AI-powered analytics can be leveraged to improve business outcomes. Enhance efficiency? Check. Boost revenue? Check. Improve customer satisfaction, predict potential equipment failures, or optimize supply chain operations? Check. Check. Check. So, if other companies are using advanced analytics to impact the bottom line, why are so many data leaders running into the same resistance like Nick did with AnyCorp?

If organizational leadership hasn’t been exposed to the unique role of data in driving business success, data leaders will need to bridge the gap between the technical jargon in the AI hype cycle and the language of business. For example, data leaders will garner more support if they’re able to articulate the value of data in terms that resonate with organizational leaders, such as Return On Investment (ROI), risk mitigation, and Key Performance Indicators (KPIs).

What could someone like Nick do differently next time to get more support from the AnyCorp leadership team? Rather than highlighting the latest developments of AI solutions in the marketplace, data leaders like Nick can break down the complexity of implementing advanced analytics initiatives into four areas of discussion:

  • Business Value
  • People
  • Process
  • Technology

Business Value

Harvard Scientist E.O. Wilson famously said that “We are drowning in information while starving for wisdom.” This statement perfectly encapsulates the challenges in data analytics today - the daunting task is not gathering the data, but extracting meaningful insights from it. This is why a well-defined use case that is tightly aligned with a known strategic business goal is a golden opportunity for data leaders to encourage their organizations to venture deeper into advanced analytics. Focusing on achieving specific, measurable outcomes will help Nick’s peers understand the direct impact of using data to drive business success.

It’s important for data leaders to communicate in business terms rather than using industry buzzwords and technical jargon. The language of organizational leadership focuses on revenue growth, cost savings, and customer satisfaction – not automated data pipelines, ETL processes, or elastic cloud services. By helping the other organizational leaders understand the relationship between data and business success, data leaders position themselves as trusted advisors who can shape strategic decisions. This cannot be overstated.

For example, let’s say that the sales leaders AnyCorp are not only looking for more information about their customers, they also want quick access to the most recent data from customer interactions, sales transactions, and website visits. If the current technology infrastructure is built on batch processing, requires manual reporting, and has limited integration between systems, data analysis for these sales leaders will most likely be time-consuming and prone to errors. If, however, modernizing the technology allows the AnyCorp sales team to make a data-driven decision on offering certain customers credit within minutes of application rather than within days, Nick is going to have an easier time getting support for his data initiatives.

Leadership teams unfamiliar with data-driven initiatives will eventually recognize the untapped potential of advanced analytics when they’re able to observe tangible business results. Creating a culture that values data-driven decision-making takes time. Whenever an organization is at the beginning of its advanced analytics journey, it’s essential for data leaders like Nick to celebrate early wins with clear metrics and goals to build credibility and momentum for further funding and support.


The most successful organizations are armed with teams or individuals who know how to understand, interpret, and ask impactful questions about their data. Even though members of the analytics team may have well-defined roles and responsibilities that align with their skills, advanced analytics often require a level of expertise that many organizations don't have in-house. Hiring talented data professionals makes sense, but there's a significant demand for data scientists and data analysts who can conduct complex analyses as well as data engineers who understand how to operate in cloud environments (like Azure or AWS). Organizations may struggle to find, hire, and retain these professionals.

Alternatively, many organizations invest in upskilling their existing talent to become more data proficient. Even though the time required for comprehensive training can be a barrier for employees with limited capacity, being able to leverage the wealth of internal knowledge and domain expertise in data-driven initiatives should be obvious to any organization looking for a competitive advantage. Providing opportunities for upskilling demonstrates an investment in employee growth and development, but where do you start? How do organizations identify individuals with the right skills, mindset, and ability to conduct complex data analyses and become more data-curious? What skills are needed and what roles are necessary to guide capacity-building efforts when it comes to data proficiency?

To help organizations answer these questions and understand the strengths and skills gap in their workforce, they turn to companies like Snow Fox Data to perform an analysis of their current analytics state and future state desires. By conducting interviews with key stakeholders and reviewing individual responsibilities, we can provide detailed recommendations on areas of improvement for increasing the company’s analytics capacity and throughput.

As a leader, Nick knows his AnyCorp analytics team the best. Ultimately, the decision between hiring data professionals or upskilling existing employees depends on specific circumstances, timelines, and the availability of talent. With a proper skills assessment, Nick could leverage the experience within his internal team while strategically bringing in external expertise as advisors and hands-on mentors for fresh perspectives and industry best practices. By using a combination of both approaches, he could have given the AnyCorp leaders the reasons they needed to support his team’s capacity-building efforts.


As a company like AnyCorp becomes more data-driven, it also becomes necessary to change data processes to accommodate and leverage the increasing volume, variety, and velocity of data. A common theme in analytics is around “democratizing your data” to empower a wider range of employees with access to relevant and timely data. To accomplish this, data processes need to be adjusted to enable self-service analytics, data visualization, and user-friendly data access tools. This shift allows employees across the organization to explore data, generate insights, and make data-informed decisions. However, before creating or updating policies and standards for handling data, organizations must conduct a high-level data inventory and classification exercise. The outcome provides data leaders with an understanding of what data is available, where it's stored, who has access to it, and how it's currently used. By providing AnyCorp leadership team with an overview of their data assets, Nick would have helped them understand their data within the specific business context and laid the foundation for more data-driven initiatives within the organization.

Another theme you’ll find in analytics is that “data science and advanced analytics is a team sport.” If data leaders like Nick do not address the common barriers of cross-functional collaboration - like conflicting department goals, ineffective communication channels, or differences in work styles - their analytics teams will have a hard time coordinating activities that are often distributed across different departments or teams. Collaboration and communication among professionals with different backgrounds and expertise are essential. To meet timelines and achieve meaningful outcomes, analytics teams need to follow project methodologies that call for regular meetings, brainstorming sessions, and knowledge sharing to ensure everyone on the team - including the business stakeholders - is aligned with the overall objectives and priorities. Organizations like AnyCorp not only need the technology to support the demands of AI processing, their data leaders also need the right tools to share insights, collaborate on projects, and track progress across teams.


Even though we already addressed how important it is for data leaders to communicate in business terms, it bears repeating. Assuming that the AnyCorp leadership team is driving toward specific goals and business outcomes, it’s Nick’s responsibility as the data leader to demonstrate how investing in new technology enhances those outcomes with better and faster access to data. In fact, when business leaders begin seeing better outcomes and realizing the potential of data-driven initiatives, it’s common for successful analytics programs to receive even more requests from these leaders who are actively using data analytics in their day-to-day operations.

In order to meet this demand for better and faster access to data, organizations will need to invest in a modern analytics architecture. To help our clients understand what constitutes a modern analytics architecture, we typically break it down into four components:

  • Compute/Store.  A flexible and scalable environment to warehouse data and support analytics operations and workflows.
  • Extract/Load.  Data migration tools responsible for moving data between systems, often with automation/monitoring features.
  • Analytics Platform.  Centralized and democratized location to prep and transform data, collaborate, and govern all maturity levels of analytical projects.
  • Consumption.  Tools for end-users to interact with data and analytic products, best if widely adopted and supported with training.

We typically recommend choosing a single tool - a best-of-breed approach - as a primary technology solution for each component. Determining the particular features and functionality within each component is typically a collaborative decision among the various roles or “personas” in data analytics - data engineers, data scientists, data analysts, and data consumers. Each of these personas require specific software tools to effectively perform their responsibilities. Most importantly, these tools need to work together to support best practices and seamless workflows. Communication and collaboration are key. If there are too many tools that aren’t integrated and disparate data flows that require workarounds, data leaders will not be able to support the speed and volume requirements of data-driven decision-making.

The Importance of Highly-Tailored Data Analytics Assessments

 When we look at people, processes, and technology as critical components, we find that data leaders also need to consider how each of those influence a culture where insights and decisions are based on evidence and data rather than personal opinions or assumptions. Fostering a data-driven culture is an ongoing journey of refinement and learning as the organization gets better at using data and collaborating on more complex analytics projects.

Understanding the pieces necessary to support these data analytics initiatives, what could a data leader like Nick do differently next time to get more support from the AnyCorp leadership team? To build a compelling business case that quantifies the impact of advanced analytics, data leaders need a comprehensive understanding of their organization’s strategic goals and priorities. Additionally, they need to know how their team’s capabilities, data infrastructure, and analytics practices compare against best-in-class organizations. Even if a data leader proactively engages with stakeholders at all levels of the organization, it can still be incredibly difficult to identify internal gaps and optimal improvements that align with industry-leading practices and trends. Oftentimes, data leaders will tell us that “they don’t know what they don’t know.”

What if Nick didn’t have to do all of this alone? What if an objective third party could help data leaders identify blind spots, uncover hidden opportunities, and challenge existing assumptions while also offering insights and best practices based on experiences working with different organizations and industries? What if a third party could collaborate with a data leader to get a quick win that helps reinforce the value and benefits of additional investments in advanced analytics?

At Snow Fox Data, we built our Data Advisory Services model to answer these questions. Our holistic assessment provides a thorough understanding of the current state, gaps, and areas for improvement within the organization's data analytics capabilities. To help data leaders identify clear actionable steps to make their data vision a reality, we focus on business value, people, process, and technology. We interview key stakeholders, document your current state, help set goals toward a desired future state, and consult on the activities that fit your organization's culture and structure. Ultimately, we leave with you a road map and recommendations for the entire data lifecycle - from data collection and storage to analysis and reporting.

If you’d like to learn how a Data Analytics Strategy Assessment could kickstart your data journey, contact us. We meet you where you are on your data analytics journey and get you where you need to go. 



Snow Fox Data

Snow Fox Data is a premier data strategy, data science and analytics solutions provider. Our team of data architects, data scientists, data engineers, and data analysts are passionate about helping businesses make a difference with data.