When implementing a value creation plan following a buyout, many private equity (PE) firms encounter challenges with the management information produced by newly acquired portfolio companies. The task of effectively managing data on business metrics and performance is often hindered by fragmented system technology landscapes, legacy systems and a lack of robust data management. The complexity is greater when the business is a bolt-on acquisition, creating a patchwork of systems that can stifle performance and growth.

The challenge of a fragmented systems landscape

Medium-size newly acquired businesses typically bring a disparate array of legacy systems. While functional in their original contexts, these systems may not align with the strategic objectives of the newly formed entity, making their integration into a cohesive IT landscape a major challenge.

Legacy platforms can also pose risks, inflate IT operating costs, and limit business transformation and value creation initiatives. Additionally, underinvestment in reporting and analytics can deprive the organization of critical insights needed to drive business performance.

Value creation cannot wait

Operational improvements and digitally-driven growth opportunities at portfolio companies, key components of value creation plans, often demand technology investments. These may include automation, AI and the implementation of new enterprise systems supporting streamlined business processes.

Optimizing the IT function itself – by streamlining and right-shoring operations to support a modernized IT landscape – is also a contributor to value creation. Delivering on all these levers requires system transformations with timelines ranging from a few months to several years.

Because data insights are crucial for driving performance – whether through managing working capital or focusing on higher-margin products – management and investors should not wait for the wider strategic technology transformation to be completed. Instead, these insights should be realized early through tactical initiatives. Such initiatives should be designed as “no-regrets” actions that will lay the groundwork for lasting solutions, rather than temporary fixes.

Key considerations

To navigate these challenges, a strategic approach to system transformation is essential. Here are key recommendations for organizations seeking to optimize their IT landscapes and drive value creation while delivering early value opportunities:

1. Implement a data layer over existing IT Applications: While multi-year, comprehensive IT transformations such as ERP migration and consolidation are underway, businesses can implement a data layer over the existing business applications. This approach allows organizations to harness the data in their current systems, even legacy ones, while system transformation and modernization takes place.

2. Develop scalable solutions with a solid foundation: Avoid the pitfall of creating tactical solutions that are merely stopgaps. Instead, focus on developing analytics and reporting solutions with a robust foundation that can be incrementally improved and scaled.

3. Engage an effective and efficient delivery partner: Partner with a delivery partner that emphasizes pragmatic and incremental value, as opposed to long data warehousing projects which lack absolute focus on business outcomes.

4. Leverage accelerators for rapid deployment: Utilize accelerators, such as A&M’s Data Toolkit, to swiftly deliver key priority use cases. These accelerators can facilitate critical analyses, including procurement spend, working capital, P&L analysis and SKU profitability. Through these tools, organizations can gain valuable insights quickly, supporting informed decision-making and strategic planning, which are essential to drive value creation opportunities.

5. Deliver a pragmatic and fit-for-purpose IT transformation strategy: Create a technology transformation plan that aligns with the organization's current and future growth objectives by balancing costs, impact of the transformation, the total cost of platforms and the supporting IT organisation.

Conclusion

Value creation plans can face obstacles when dealing with IT transformation and management information. Organizations can overcome these obstacles by adopting a strategic and joint approach to system transformation and data. 

Implementing a data layer, developing scalable solutions, engaging the right delivery partner, and leveraging accelerators are all critical steps in delivering early value creation while delivering IT transformation plans to deliver sustainable performance improvement and strategic growth.

How A&M can help

At Alvarez & Marsal, we seamlessly integrate our expertise in data analytics, performance improvement-focused accelerators, and financial acumen with our technology advisory capabilities. This approach ensures the delivery of data-driven value creation early in the investment life cycle, while defining and driving the technology transformation roadmap.

1. Business and Financial Knowledge Driven: A&M's deep expertise in business and financial matters ensures that data solutions are not only technically sound but also aligned with the client's strategic and financial goals. This knowledge allows A&M to tailor data initiatives that directly support business objectives, enhancing decision-making and operational efficiency.
2. Razor focus on Value Creation: A&M is committed to creating tangible value for clients. Our approach to data work is centered around identifying and unlocking opportunities that drive measurable business outcomes. This focus ensures that every data project contributes to the client's bottom line, maximizing return on investment.
3. Incremental Value: A&M emphasizes delivering incremental value throughout the data project lifecycle. By breaking down projects into manageable phases, we ensure continuous delivery of benefits, allowing clients to see early results and adjust strategies as needed. This approach reduces risk and enhances the overall impact of data initiatives.
4. Pragmatic Approach: A&M's pragmatic approach to data work means we prioritize practical, actionable solutions over theoretical models. We focus on what works in the real world, ensuring that data strategies are feasible, scalable, and sustainable. This approach helps our clients implement data solutions that are both effective and efficient, leading to long-term success.