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Mitigating Risk for MA and Post Merger Integration

Wed, 05 Mar 2025 11:34:39 GMT

Mitigating Risk for MA and Post Merger Integration

For any company, supporting M&A and Post Merger Integration (PMI) is daunting. The numbers are stacked against you, failure rates are high, timelines are aggressive, and synergies are obfuscated by cultural fog. With expectations high and speed to value a priority it’s critical to prepare for M&A and PMI and engage in a disciplined and deliberate way. As a former software executive, I was often chosen as a Field Stakeholder supporting both major M&A activities and “tuck in” acquisitions to bolster a capability in a tactical way, or to support the strategic GTM direction of the company. As I looked around the Enterprise Software landscape it also seemed to be the standard operating model for many others. One thing was clear, the sooner we knew about our adjoined customers, the better we could serve them. The advantages were plentiful: new markets, new ARR, new offerings, new customers; but so were the risks; nearly all of them affect growth, valuation, and market perceptions. Specifically, if you cannot integrate post-merger, your competition will use that against you.

“Normally, data management in some form comes up in the separation/integration phase and not that often in the preparation phase. I’d argue that’s at an organization’s own peril.”

Forbes Technology Council.

After leaving the software industry and turning my efforts toward consulting, a few root causes tied to PMI risk consistently appear. Interestingly they are less about tech integration and more about people and process: Deal Alignment, Culture, Employee Retention, Communication of strategy, change management, amongst others.

To mitigate these known risks, at amino Data we focus on accelerating PMI via data integration and integrity . Trust is everything and seeing the world through a single pain of glass is the best way to bring merged companies together. Systems & data integration often involve data synchronisation, deduplication, and migration. You’ll need a single view of the truth , especially when each company may have their own definitions. What’s critical is that the newly merged company must move forward as one and share a common vision to execute in the marketplace. That’s next to impossible if you can’t come to terms for things like, “what is a customer?”

While not comprehensive as each client has different needs, at the core foundation we address the data to answer:

Who is my customer – define a customer under the merged company?
Which products do they own?
Upcoming renewals, churn / risk?
How can we improve retention?
How can we bring more value by cross selling and bundling solutions?
Do we have supply chain risks?
What’s our combined inventory?
How can we close our financial statements in an integrated and timely way?

When teaming with PE / VC Firms, if each PortCo delivers different interpretations for sales, inventory, FP&A, etc.… the burden to make sense of it all is on the PE Firm and it is daunting. We’ve found that applying a consistent set of standards and data integrity provides both the portfolio company and the PE/VC with a greater appreciation of what’s needed to ensure reporting integrity and unlock market insights. Most directly, the merged entities have a greater opportunity to take informed action against any number of data signals. By serving the portfolio company and the PE Firm we focus on strategic outcomes via aligned data. This addresses “XLS spreadsheet” wars, gets everyone together for a single view of the truth, and offers unrealised insights which may have been historically unseen.

When it comes to effective M&A and PMI, data isn’t the only thing, but it is the foundation for valuation realisation .

To learn more about our approach and how we can share some ideas, visit us at www.amino-data.com/PEandVC

#Private Equity #data #M&A

The Two Circles: How AI is Reshaping Modern Data Governance

Sun, 23 Feb 2025 15:12:38 GMT

How AI is Reshaping Modern Data Governance

We find ourselves at an intriguing juncture in the world of data governance. On one hand, there’s the growing need to govern Artificial Intelligence (AI) systems, ensuring they’re used ethically, responsibly, and in compliance with regulations. On the other, AI is being seamlessly integrated into our data governance tools, helping us solve some of the most complex challenges we face in managing and securing data. This raises an interesting question: where do these two types of governance meet, and where do they diverge?

Data governance, while a critical function for any organisation dealing with vast amounts of data, has often struggled to live up to expectations. Many businesses are still grappling with building a strong business case for data governance, and even when implemented, the outcomes are often less than stellar. Furthermore, the sheer volume of data companies must manage, alongside the need for high-quality, well-structured data to train AI systems and support technologies such as Generative AI and Retrieval Augmented Generation (RAG) systems, presents an entirely new set of challenges. In this context, traditional approaches to data governance seem increasingly outdated, highlighting the need for innovation.

Enter AI. As data governance becomes more complicated, AI has emerged as a powerful tool capable of transforming the way organisations manage their data assets. By enhancing efficiency, improving accuracy, and streamlining operations, AI-powered solutions are rapidly reshaping the landscape of data governance. Let’s explore some of the real-world applications of AI in this domain and understand how it is making a tangible difference.

AI Use Cases in Data Governance

Automated Data Discovery and Classification

One of the most remarkable applications of AI in data governance is automated data discovery and classification. In the past, organisations relied on manual processes to scan through their data and classify sensitive information—an approach that was not only time-consuming but also prone to human error. Now, AI systems equipped with machine learning algorithms can automatically detect and classify data across diverse systems, identifying sensitive data such as personal information, financial records, and intellectual property.

A prime example of this in action is Grundfos’s use of Microsoft Purview. Grundfos are a pump manufacturer that gathers a lot of data from IoT devices, as well as organisational data. This has been deployed to scan the organisations hybrid estate to help the scan and classify data. This has significantly reduced the need for manual data classification, improving both efficiency and accuracy.

Data Quality Management

Data quality management is another area where AI excels. AI’s ability to analyse vast amounts of data and detect patterns makes it an ideal solution for identifying inconsistencies, anomalies, and potential issues in data sets. Finance organisations have been using AI-driven platforms such as Precisely’s Data360 to automatically monitor data quality across their operations. The AI system flags issues such as duplicate records and missing values, while also predicting potential data quality problems before they escalate.

The result is a more proactive approach to data management, reducing the risk of errors and improving the overall integrity of data. AI, therefore, plays a critical role in ensuring that organisations maintain high standards of data quality in real-time.

Security and Privacy

As privacy regulations continue to tighten globally, AI is emerging as a vital tool for helping organisations comply with these increasingly complex requirements. AI is instrumental in areas like intelligent access control, where it analyses user behaviour to detect any unusual or potentially suspicious activity.

For instance, Partners Healthcare, a leading healthcare provider, uses AI to monitor access to sensitive medical records. The AI system flags any unauthorised attempts to access data, ensuring that patient information remains secure and compliant with privacy regulations. Additionally, AI-powered systems are invaluable in helping organisations identify and classify personal data in accordance with data protection laws such as the GDPR.

A notable example comes from Migros-Genossenschafts-Bund (Migros), Switzerland’s largest retailer and supermarket chain. Migros uses OneTrust’s AI capabilities to identify and classify personal data across its operations. This automated system ensures the company adheres to GDPR requirements, reducing the risk of non-compliance and associated penalties.

Data Lifecycle Management

AI is also transforming how organisations manage data throughout its lifecycle. From the moment data is created or collected to when it is archived or deleted, AI is enhancing the efficiency and cost-effectiveness of data management processes.

AI is also improving data lineage tracking, which is essential for maintaining transparency and ensuring compliance. Pfizer, for example, uses Alation’s AI-powered data catalogue to map data lineage automatically. This enables the company to track where data comes from and where it goes, making it easier to ensure that data is handled correctly and in line with regulatory requirements.

Governing AI within Data Governance Tools

While AI is becoming a key component of modern data governance tools, it is important to recognise that AI itself must also be governed. In essence, organisations must govern the very systems that are helping them govern their data. This presents unique challenges, as the governance of AI involves several key considerations.

Performance Monitoring: Just as you would monitor the performance of any tool or process, it is crucial to assess how AI models are performing and ensure that they are delivering accurate results. This includes ensuring that there is a human in the loop for monitoring.
Bias Detection and Mitigation: AI systems can inadvertently introduce biases in the way they classify or process data. Therefore, it is vital to regularly check AI models for biases and take corrective action when necessary.
Version Control: Much like software updates, AI models need to be versioned and tracked to ensure that any changes or improvements are well-documented and do not inadvertently impact governance processes.
Compliance Assurance: Organisations must ensure that AI-powered data governance tools comply with all relevant regulations, including the GDPR and the California Consumer Privacy Act (CCPA).
Continuous Monitoring: AI systems should not be set and forgotten. Ongoing monitoring is required to ensure that the models remain accurate and relevant over time.
Training and Education: For organisations to successfully implement AI-powered data governance tools, staff must be adequately trained. This includes not just technical teams but also non-technical departments, as AI will have an impact on many facets of the organisation.

The Regulatory Landscape

The regulation of AI is still a work in progress, with different regions taking different approaches. The European Union is at the forefront with its AI Act, which establishes strict guidelines for high-risk AI applications. The Act aims to ensure that AI is used safely and responsibly, with particular emphasis on transparency, accountability, and fairness.

In contrast, the UK has adopted a more flexible approach to AI regulation. The UK AI Bill, which is currently making its way through Parliament, focuses more on guiding principles and is less prescriptive than the EU’s approach. This allows for greater innovation but also requires organisations to take a more proactive stance in ensuring that AI is used responsibly.

The regulatory landscape needs to be reviewed to determine whether an organisation is either a developer or deployer of AI products, as there are different obligations for each.

Impacts on Organisations

As organisations integrate AI into their data governance processes, several considerations must be kept in mind:

AI Literacy: Organisations need to ensure that their teams, from IT to procurement, have a basic understanding of AI and its implications. This is no longer just a concern for data scientists—it’s something that affects the entire organisation. For example, procurement needs to be more aware of software procurements with embedded AI and where the accountability would lie if something went wrong.
Consistency Across Tools: Organisations should strive for consistency in how AI behaves across different governance tools. This ensures that AI models and algorithms can be trusted to work seamlessly across the organisation’s data landscape.
Increased Technical Expertise: The procurement of AI-powered data governance tools is no longer a simple purchasing decision. It requires a higher level of technical expertise to evaluate the capabilities and risks associated with the AI models being used.

Conclusions

AI-powered tools are undeniably transformative, helping organisations streamline their data governance processes, improve efficiency, and ensure compliance. However, they are not ‘set and forget’ solutions. Just as with any tool, AI requires ongoing management, monitoring, and oversight to ensure that it continues to perform as expected and remains in line with evolving regulations and organisational needs. Think of AI as a highly skilled assistant—it can do incredible work, but it still requires guidance, oversight, and care.

In the fast-evolving landscape of data governance, organisations must not only embrace AI but also recognise the responsibility that comes with it. By doing so, they can ensure that AI remains a force for good, improving both their governance practices and their overall data strategy.

For further information, please contact amino Data to discuss your AI and Data Governance requirements.

The Post Merger Integration Achilles

Tue, 04 Feb 2025 17:24:12 GMT

Your Stability is Tied to Your Foundation

Data can either cripple or accelerate Post Merger Integration for a Private Equity portfolio company. In the Invest – Grow – Strategic Exit cycle, “Growth and Innovation” were the prime directives for this enterprise software company. While new cloud applications were on the roadmap supporting their new Go-To-Market (GTM) and all the right players for CRM, ERP, Marketing Automation, eCommerce, etc. were identified… lurking in the shadows beneath the business processes were issues that were lingering in the data.

ARR growth was critical, but just as a calculator is dependent on the accuracy of the digits’ input, these digital transformation investments were dependent on the core data being consumed. While systems are temporary, the data lasts forever.

At the foundation, data can be the Achilles or the catalyst to growth.
Synergies (Yes, I said it!) and Outcomes tied to PMI relied heavily on making the right decisions based on exact answers. Our client was looking for insights such as:

Ø ARR and Bookings by Market?
Ø Customer Profitability Analysis?
Ø What is the fully loaded cost of my ARR, by market?
Ø Which campaigns were most effective, and why?
Ø What content resonated most for conversion?
Ø What product features are most sought after?
Ø How do I cluster customers to cross-sell better?
And more.

Our approach was to start with a data foundation anchored in discipline: policies , rules , and standards . Boring stuff unless you’re a data geek; but when mapped to a strong ROI and business outcomes like: lead conversion, ARR growth, solution bundling, and improved customer insights we could navigate and engage more effectively with the business stakeholders. It was designed for their business needs not simply an IT data project.
Understanding the Go To Market was critical to provide the right data in context to be analyzed. Part of the Post Merger Integration effort was to bridge teams, build on a common set of goals, and show with a fact-based data driven approach of how insights would be gained and then applied within the merged entity. This led to trust , collaboration , and engagement as part of the PMI process. In essence, PMI and growth were inextricably linked to the data; just as your stability is tied to a solid foundation.

To learn more about our approach and how we can share some ideas, visit us at www.amino-data.com/PEandVC

AI Ready Data

Mon, 03 Feb 2025 17:26:15 GMT

Is your data AI-Ready?

Introduction

During a recent industry conference, a survey of around 250 attendees revealed an interesting statistic: only one participant tentatively claimed that their organisation’s data was AI-ready. This finding opens up a bigger conversation about what being AI-ready even means. It seems like there’s no common understanding among professionals about it.

Many organisations hesitate to declare their data as AI-ready. But what does that really mean? It’s clear that data readiness isn’t a one-size-fits-all situation. Instead, it heavily depends on specific use cases and applications. The idea of universally AI-ready data sounds nice, but it might not be realistic or necessary. Rather than striving for some unattainable ideal, organisations should focus on making sure their data meets the specific needs of their intended AI projects.

Definition

So, what exactly is AI-ready data? At its core, it refers to information that is known, understood, available, fit for purpose, and secure enough to serve as reliable input for artificial intelligence and machine learning systems. To put it simply, your data needs to be high-quality, easy to explain, and well-governed. This brings everything back to how the data was captured, how it has been managed, and how it's being used.

Gartner has an interesting take on this too. They suggest that “AI-ready data” should be representative of the use case, capturing every relevant pattern, error, outlier, and emergence needed for training or executing the AI model for its specific application (Gartner, 2024). This helps clarify why we can’t confidently say that data is universally AI-ready—because we really need to consider each use case individually. This aligns well with the Unified Governance Framework by Amino, which provides a structured way to implement AI while considering specific use case requirements.

How to ensure AI-Ready Data

To truly make the most of AI capabilities, organisations should adopt a use case-driven approach when it comes to both AI initiatives and the data requirements that come with them. Instead of trying to make all your data AI-ready at once (which can feel overwhelming), focus on gradually improving data quality and accessibility to align with specific AI initiatives.

By identifying and preparing the necessary data for each use case, you can build a solid foundation that grows naturally alongside your AI projects. This incremental strategy helps tackle two major hurdles in adopting AI. First, it prevents organisational paralysis—the tendency to keep delaying AI initiatives while waiting for “perfect” data readiness. Second, it guards against rushing into AI deployments without fully understanding the quality and lineage of your data.

Focusing on specific use cases ensures that each data domain meets the quality standards required for its intended function. Plus, you can implement the necessary governance at any given moment.

This approach fits perfectly with the Hybrid methodology outlined in the Unified Governance Framework. Each use case might need different levels of data governance and preparation, which allows organisations to tailor their efforts

accordingly.

Speaking of tailored efforts, organisations can utilise AI Backlogs—dynamic collections of potential AI projects that highlight opportunities across the enterprise. Each proposed use case goes through an evaluation process using a prioritisation matrix where data requirements play a crucial role. This assessment looks at both the strategic value of the use case and the quality and readiness of the necessary data.

The prioritisation process helps organisations make informed decisions about resource allocation and project sequencing. By evaluating data requirements early in the process, organisations can identify and address potential data gaps before committing significant resources to implementation. This proactive approach to data readiness ensures that AI initiatives have the highest probability of success while maintaining efficient use of organisational resources.

When planning your AI projects, it's important to think beyond just technical specifications. A strong data foundation is key! Here are a few areas to pay attention to:

Relevant Data

Is the right data available for your use case? You'll want to ensure its accurate and transparent. Some things to consider:

• Data Profiling: Check if the data fits your needs.

• Unstructured Data Labelling: Particularly important for large language models.

• Data Lineage: Know where your data comes from!

Responsible Data

Is your data governed, accessible, and secure? It should also comply with standards for AI usage. Key considerations include:

• Data Access: Who can access it?

• Anonymisation/Pseudonymisation: Protect personal information.

• Data Bias Metrics: Measure and mitigate bias.

• Drift Monitoring: Keep an eye on any changes in your model's performance.

• Consent: Ensure proper usage consent is in place.

Robust Data

Make sure your data is complete, resilient, and consistent. This involves:

• Data Quality Management: Regular checks on your data quality.

• Versioning: Keep track of changes.

• Automation: Streamline processes where possible.

• Licensing: Ensure you have the right permissions.

• AI-Ready Information Architecture: Set up a strong structure for your data.

The Underpinning of Data Governance

Over the last decade or so, organisations have changed how they view and manage their data. With business intelligence and analytics becoming essential for decision-making, the demand for high-quality data has skyrocketed. As AI technologies rise in prominence, this need has only intensified, pushing for robust data governance frameworks that cater to both traditional analytics and modern AI initiatives.

If your organisation has established data governance programs, you’re already ahead of the game! These frameworks serve as a strong foundation for implementing AI solutions and help you leverage quality data sooner in development. However, with advantages come responsibilities—balancing governance with innovation is crucial.

Best Practices for AI-Ready Data Governance

To effectively prepare your data for AI applications, consider these best practices:

Leverage Existing Frameworks: Instead of starting from scratch, consider extending your existing governance frameworks to accommodate AI-specific needs. This way, you build upon what's already working while adding new elements necessary for AI implementation.

Embrace Modern Tooling: Utilising advanced data quality tools and catalogues has become essential for managing both structured and unstructured datasets. Tools like BigID can help handle diverse data types while maintaining governance standards—providing necessary infrastructure for ensuring quality across your AI initiatives.

Foster Organisational Collaboration: Success in implementing AI requires coordination across different roles within your organisation. Creating an AI framework that brings together various stakeholders leads to better outcomes and higher success rates. This collaborative effort aligns governance practices with AI goals while effectively using institutional knowledge.

Implement Robust Monitoring: Data governance today needs to go beyond just initial preparations; it requires continuous monitoring and adjustment. This is especially important for systems utilising advanced techniques like retrieval-augmented generation. Establish comprehensive protocols that track both data quality and model performance after deployment. And don't forget about having clear incident response processes in place.

Conclusion

Recognising that getting to AI-ready data is more of a journey than a destination marks a significant shift in how organisations approach governance. The real success lies in putting robust mechanisms, toolsets, and processes in place to support ongoing preparation and management of data tailored to specific AI use cases.

By building your data capabilities incrementally alongside these initiatives, you can gain a thorough understanding of your organisation’s data landscape. This approach allows you to create well-tagged datasets that can support multiple AI projects while still adhering to good governance practices.

As we continue exploring the potential of artificial intelligence in our organisations, our focus should remain on developing governance frameworks that are not only strong but also flexible. These frameworks should promote innovation while ensuring the underlying data meets essential requirements for relevance, resilience, and robustness specific to each use case.

In short, achieving AI readiness isn’t just about having perfect datasets; it’s about understanding your organisation’s unique needs and preparing your data accordingly. Taking actionable steps today towards better governance and strategic planning around our datasets sets organisations up for success with artificial intelligence tomorrow!

For further information, please contact Amino Data to discuss your AI and Data Governance requirements.

AI is the New Panacea with the Old Challenges

Tue, 28 Jan 2025 16:44:43 GMT

AI is the New Panacea with the Old Challenges

AI is everywhere these days - it's the hot topic at most conferences and seems impossible to avoid. While it's seen as transformational, there are significant concerns about potential harm at societal, organisational, and personal levels. Despite these concerns, AI adoption continues to grow in organisations, with many comparing its impact to that of the Industrial Revolutions. The Life Sciences industry is a perfect example, where AI is already demonstrating real value.

But here's the interesting part - despite all this progress, data remains the primary barrier to entry for AI initiatives. So, what's the solution? Many suggest tying Data Governance and AI Governance more closely together. While this might seem obvious, the real challenge isn't in identifying what to do, but in figuring out how to do it effectively .

Let's take a step back and consider what needs to be in place before we can even think about governing AI. Quality data is absolutely crucial - it's the lifeblood of AI. Yet it's fascinating how often this gets reduced to a throwaway line like "of course, you need to make sure that you have good quality data." It's so obvious that it often goes unnoticed .

But here's where it gets more complex - AI is broader than just data. We need to consider multiple factors, including Ethics, Transparency, Explainability, and Responsibility. Data is just one piece of this larger puzzle. This raises an important question: how can we implement governance for such a rapidly evolving technology?

Unified Governance Framework

This is where the Unified Governance Framework by amino comes into play. It's an innovative approach that takes existing data governance as the foundation of AI Governance while recognizing that AI Governance extends beyond just data. The framework builds upon existing structures to cover both Data AND AI, creating a unified governance mechanism. It provides processes, templates, and accelerators that organisations can tailor to their needs, taking into account regulations, geographies, model governance, and expected outcomes - all aligned with business strategy.

What makes this framework particularly valuable is its flexibility. Whether an organization already has robust Data Governance in place or is starting from scratch, the framework can adapt. For those with existing Data Governance, it offers a baseline process to identify what evolution is needed for AI Governance. For organisations with minimal formal data governance, it enables specific governance initiatives with AI as the end goal. The framework can even be retrofitted onto existing AI initiatives.

One crucial point to understand is that data governance is a component of AI Governance, not the other way around.

Principles

The framework is built on four key principles:

Data – without quality data, there simply is no AI. This makes data governance essential for ensuring data quality meets requirements.

Leverage investments and evolve – this means not just evolving data governance but also other governance mechanisms, like risk management frameworks. It's about examining existing processes and identifying necessary changes for AI implementation.

Innovation – since AI is inherently innovative, governance must drive innovation rather than impede it. While this can be challenging, taking time at the start to consider requirements enables a "fail fast but safely" approach.

AI without IA is futile – technical and business aspects must be integrated, with Information Architecture providing the foundation for AI.

Evolution

When organisations already have data governance in place, they've developed specific structures, processes, and clearly defined roles and responsibilities. Instead of dismantling this existing structure, these are used as the baseline and then expanded to accommodate the new requirements that AI brings to the table.

This evolution involves bringing more people into the conversation than might have been originally considered. With AI governance, we need to expand our circle of stakeholders and be very explicit about who's responsible for what decisions and why they need to be included in the process.

One of the most crucial aspects of this evolution is ensuring that business teams and data science teams work hand in hand. These groups sometimes speak different languages, but the framework helps bridge this gap by creating structured ways for them to collaborate effectively.

But here's where it gets really interesting - this evolution isn't limited to just data governance. Any governance process that touches AI can and should evolve. Take risk management, GDPR compliance, or privacy governance, for example. These all need to work together in harmony within the unified governance structure, like different systems in a smart home that need to communicate with each other.

However - and this is important - not everything needs to come under one umbrella. Some processes might work better remaining separate but connected. The key is understanding how these different elements link together and influence each other.

Approaches

When it comes to implementation, there's often tension between governance and innovation speed. Some argue there isn't time to implement Enterprise AI Governance first, fearing loss of competitive advantage. However, rushing into initiatives without considering business strategy alignment can lead to pilots never scaling to production.

The solution? A hybrid approach using "Just in Time Governance." This ensures enough overarching principles and guidance are in place before considering the specific governance needs of each AI use case. It's about asking the right questions: for example, who's developing these use cases? Who's assessing risk? Who needs to approve pilot entry and exit? Answering these questions will inform who needs to be part of the governance structure.

Conclusion

When implementing AI into an organisation, consider the following areas:

Review existing governance structures – don’t start from scratch and look how to leverage existing investments to meet AI requirements.
Data Governance is not AI Governance – but it does form the basis for AI Governance. There are other areas that need to be considered as part of AI Governance, including whether this should be deployed.
360-degree Risk Review – AI governance will only work if all the relevant groups are brought together. This includes both technical resources, business resources and areas such as legal and commercial to understand the risks associated with each initiative.
Use ‘JIT’ Governance – establish the overarching principles and processes, just enough to get going. As part of each initiative prioritisation, review the stakeholders needed through the development and deployment process, and bring these in at the appropriate time.

For further information, please contact Amino Data to discuss your AI and Data Governance requirements. Download our Unified Governance brochure here

M&A and Post Merger Integration

Tue, 28 Jan 2025 16:44:43 GMT

Has much changed?

Famously in 2011 Harvard Business Review stated, “The M&A failure rate is between 70% and 90%." A critical element was "whether and how to integrate them.” Has much changed? Looking through a #data lens, when it comes to operationalizing an #M&A strategy, a single view of the truth is paramount to align stakeholders and eliminate culture siloes furthered by spreadsheet wars. At one PE owned company we found: Growth by acquisition was the goal but they experienced impediments due to siloed data. Data was difficult to access, find, and lacking integrity. Duplicate records prevented a 360-degree view of the customer’s history, market value, contacts, and buying centers. Key data elements such as physical and billing address, tax IDs, industry / sector, and even email or phone numbers were often incomplete, inconsistent, or simply missing from the records. As a result, critical insights tied to marketing campaigns, lead management, cross-selling and upselling, and customer profitability analysis were compromised. In a world of personalization, data integrity was corrupting marketing, sales, subscriptions, renewals, and other key financial analysis.

Bringing together disparate organizations to generate greater value is the promise of M&A and Post Merger Integration. With unrealized expectations there’s greater pressure on #PrivateEquity Firms to bring management discipline, improving operations, and financial performance.
In working with PE Firms to support Post Merger Integration , data can be either a godsend or a pitfall.

To mitigate risk , we recommend:
1) Having the required data & information centralised, secure, accessible, and easily consumable.
2) Align data efforts to strategic growth and valuation improvement goals
a. Valuation Realisation is an OUTCOMEof ongoing data discipline.
3) Become data-drivenn by demonstrating and visualising the link between data integrity and business process outcomes, decision making, and insights.
4) Gain Trust for the newly merged organization so that every employee has a single view of the truth. By doing this you will eliminate the “spreadsheet wars” and bring the new teams together.
a. Cultural alignment can be accelerated when all are on the same page.
5) Integration of systems depends on trusted and managed data
6) To exploit Advanced Analytics and AI for the new merged entity, systems must consume from a foundation of trusted and current data. In short, AI without an Information Architecture is futile .

Additional ideas and recommendations can be found here: www.amino-data.com/PEandVC