Mastering Data Transformation: A Guide for Growth-Stage Companies Eyeing a Future IPO 

Finance and technology leaders at growth-stage companies face a pivotal challenge: navigating expansion while laying the groundwork for a potential IPO or transaction down the road. In this turbulent environment, data and analytics emerge as primary focus areas every company must enhance. Why?

Because intuition-driven decisions don’t suffice when greater financial, operational, and regulatory complexity enters the picture.  

Common Data and Analytics Challenges

Transforming data and analytics capabilities often presents growth-stage companies with a unique set of challenges they can no longer postpone or hope to bypass. At the forefront is the issue of data quality. Without high-quality, reliable data, resulting financial reports can be misleading, a risk growth-stage companies can ill-afford, especially if contemplating a transaction.  

Challenges often include: 

• Data integration from disparate sources. A lack of consistency in data can generate obstacles in financial reporting, potentially leading to inaccuracies. 
• Scalable data infrastructure. As companies evolve and expand, reporting and data manipulation in Excel is no longer realistic. 
• Skilled, efficient data analysis. Limited expertise conducting analysis or ingesting data using Excel means outcomes are highly variable and may not be actionable. 
• Technology optimization: Companies currently using or exploring use cases for tools like Alteryx, Tableau, or MS Power Platform Apps often aren’t doing so in a standardized way, such as a Transformation Management Office (TMO) or Center of Excellence (CoE), which creates more IT bloat, technical debt, and process redundancies. 

In conversation with thousands of organizations seeking to address data and analytics gaps, these are some of the pain points that are often expressed: 

• “My team spends weeks manually wrangling data in Excel. It takes us so long to pull together data for our reports that we don’t even have time to analyze and act on our findings.” 
• “My teams and business are growing so fast. We cannot keep up with the amount of additional work. How can we use data or automation to scale our processes?” 
• “I don’t have a complete and accurate view of the key data needed, nor do I know which definitions to use to make decisions.” 
• “Individual team members know how to build and develop specific reports, but when they’re out, no one else knows how to create the reports.” 

Sound familiar? 

A Path Forward: Data and Analytics Solutions for Scale  

Fortunately, with the support of transformation advisors and implementation partners, leaders can lay the data foundation for scalable, sustainable success. To get started, focus on these solutions: 

• Develop a data strategy and data governance framework. This will guide all future decision-making, systems selections, corporate policy, and more. It will also be immensely helpful to have a fully established and documented approach to data when the time comes for a complex transaction like IPO or M&A. 
• Review and optimize data processes. Assess which processes in the data and analytics architecture can be automated, streamlined, eliminated, or further invested in. This might mean developing reusable assets for common use cases, reducing the time spent gathering information, and leveraging artificial intelligence (AI) or machine learning (ML) platforms to make analytics more predictive and prescriptive. 
• Implement automation software. Manual processes will not scale as the company evolves, and bad processes should never be automated. Instead, create an “ideal” process workflow that requires limited human intervention leveraging stakeholder inputs from a TMO. Then implement an automation tool like Alteryx to bring the new process to reality. By reducing manual effort, organizations can free up time, improve productivity, and generate cost savings. 
• Implement data management software. Cloud data warehousing, data lakes, and other data management tools like Snowflake help centralize and rationalize the storage, collection, lineage, and structure of organizational data flows. 
• Standardize reports and dashboards. Employee churn, continual ad-hoc requests, and inflexible reporting tools make visualizing and transmitting key data incredibly difficult and time-consuming. Reports often must be built from scratch each time new data is needed, and not everyone has the skills or tool permissions to make their own reports. Through Power BI and Tableau, reports, dashboards, and other visualizations can be standardized, regularly refreshed, and easily sent to stakeholders as needed. This frees up and converts analysts’ time from simply gathering information and building one-off reports to instead performing more meaningful tasks and finding more actionable insights. 
• Define and document roles and responsibilities. In parallel with new technologies, staff must understand roles and responsibilities within their team, department, and organizational structures. Based on the operating model, staff might be working laterally with other functions, in agile sprints, or hierarchically – whatever the case, these rules of engagement must be documented to ensure seamless handoffs, clear lines of communication, and efficient workflows when generating, curating, and delivering key data. 
• Establish and strengthen a data-literate corporate culture: It’s imperative to improve every employee’s level of comfort with generating, storing, handling, and disseminating data from the organization’s data ecosystem. Data literacy can enhance risk awareness, remove informational silos, and organically lead to faster delivery in day-to-day workflows. 
• Create a framework for refining and evolving the strategy for the future: There are several necessary steps required to set a strong data foundation to achieve predictive, real-time insights. A crawl-walk-run framework recommends focusing on establishing a data governance framework before building out a BI and analytics platform. And, if you can’t produce BI, don’t focus on AI. 

Why Invest Now? The Data-Driven Advantage 

By investing in data and analytics capabilities now, companies can optimize the present and prepare for the future. This means: 

• Smarter decisions, faster growth: Data-driven insights help optimize growth campaigns, identify customer needs, and improve operational efficiency. 
• Scalability and agility: Building a robust data infrastructure now ensures seamless integration of new systems and data sources as companies scale. This agility is crucial for adapting to market changes and seizing new opportunities. 
• Exit readiness: Investors demand transparency and data-backed forecasts. By showcasing data and analytics capabilities, leaders demonstrate a commitment to sound decision-making and future growth potential. 
• Demonstrated financial maturity: Better forecasting, projections, scenario planning, modeling, and reporting via enhanced data and analytics show investors a clear understanding of financial health and growth opportunities. 
• Reduced risk and enhanced transparency: Data-driven risk management tools provide a deeper understanding of the expanding risk universe and the mitigation strategies to address employee, Board, regulatory, and investor concerns. 
• Change readiness: Continuously innovate and adapt to market changes with data-driven decision-making, securing a competitive advantage against key market players. 

To thrive, leaders need a scalable technology and process architecture capable of meeting current and future finance, operations, and data management needs.  With this foundation in place, leaders can successfully solve comprehensive enterprise challenges by converging the efforts and talent from technology and business teams on an actionable transformation roadmap.  

Ready to take the next leap forward in your data and analytics journey? Contact Biovell to get started.