Tag: Document AI

Value-Focused Due Diligence with Data Analytics

Private equity funds are shifting away from asset due diligence toward value-focused due diligence. Historically, the due diligence (DD) process centered around an audit of a portfolio company’s assets. Now, private equity (PE) firms are adopting value-focused DD strategies that are more comprehensive in scope and focus on revealing the potential of an asset.

Data analytics are key in support of private equity groups conducting value-focused due diligence. Investors realize the power of data analytics technologies to accelerate deal throughput, reduce portfolio risk, and streamline the whole process. Data and analytics are essential enablers for any kind of value creation, and with them, PE firms can precisely quantify the opportunities and risks of an asset.

The Importance of Taking a Value-Focused Approach to Due Diligence

Due diligence is an integral phase in the merger and acquisition (M&A) lifecycle. It is the critical stage that grants prospective investors a view of everything happening under the hood of the target business. What is discovered during DD will ultimately impact the deal negotiation phase and inform how the sale and purchase agreement is drafted.

The traditional due diligence approach inspects the state of assets, and it is comparable to a home inspection before the house is sold. There is a checklist to tick off: someone evaluates the plumbing, another looks at the foundation, and another person checks out the electrical. In this analogy, the portfolio company is the house, and the inspectors are the DD team.

Asset-focused due diligence has long been the preferred method because it simply has worked. However, we are now contending with an ever-changing, unpredictable economic climate. As a result, investors and funds are forced to embrace a DD strategy that adapts to the changing macroeconomic environment.

With value-focused DD, partners at PE firms are not only using the time to discover cracks in the foundation, but they are also using it as an opportunity to identify and quantify huge opportunities that can be realized during the ownership period. Returning to the house analogy: during DD, partners can find the leaky plumbing and also scope out the investment opportunities (and costs) of converting the property into a short-term rental.

The shift from traditional asset due diligence to value-focused due diligence largely comes from external pressures, like an uncertain macroeconomic environment and stiffening competition. These challenges place PE firms in a race to find ways to maximize their upside to execute their ideal investment thesis. The more opportunities a PE firm can identify, the more competitive it can be for assets and the more aggressive it can be in its bids.

Value-Focused Due Diligence Requires Data and Analytics

As private equity firms increasingly adopt value-focused due diligence, they are crafting a more complete picture using data they are collecting from technology partners, financial and operational teams, and more. Data is the only way partners and investors can quantify and back their value-creation plans.

During the DD process, there will be mountains of data to sift through. Partners at PE firms must analyze it, discover insights, and draw conclusions from it. From there, they can execute specific value-creation strategies that are tracked with real operating metrics, rooted in technological realities, and modeled accurately to the profit and loss statements.

This makes data analytics an important and powerful tool during the due diligence process. Data analytics can come in different forms:

  • Data Scientists: PE firms can hire data science specialists to work with the DD team. Data specialists can process and present data in a digestible format for the DD team to extract key insights while remaining focused on key deal responsibilities.
  • Data Models: PE firms can use a robustly built data model to create a single source of truth. The data model can combine a variety of key data sources into one central hub. This enables the DD team to easily access the information they need for analysis directly from the data model.
  • Data Visuals: Data visualization can aid DD members in creating more succinct and powerful reports that highlight key deal issues.
  • Document AI: Harnessing the power of document AI, DD teams can glean insights from a portfolio company’s unstructured data to create an ever more well-rounded picture of a potential acquisition.

Data Analytics Technology Powers Value

Value-focused due diligence requires digital transformation. Digital technology is the primary differentiating factor that can streamline operations and power performance during the due diligence stage. Moreover, the right technology can increase or decrease the value of a company.

Data analytics ultimately allows PE partners to find operationally relevant data and KPIs needed to determine the value of a portfolio company. There will be enormous amounts of data for teams to wade through as they embark on the DD process. However, savvy investors only need the right pieces of information to accomplish their investment thesis and achieve value creation. Investing in robust data infrastructure and technologies is necessary to implement the automated analytics needed to more easily discover value, risk, and opportunities. Data and analytics solutions include:

  • Financial Analytics: Financial dashboards can provide a holistic view of portfolio companies. DD members can access on-demand insights into key areas, like operating expenses, cash flow, sales pipeline, and more.
  • Operational Metrics: Operational data analytics can highlight opportunities and issues across all departments.
  • Executive Dashboards: Leaders can access the data they need in one place. This dashboard is highly tailored to present hyper-relevant information to executives involved with the deal.

Conducting value-focused due diligence requires timely and accurate financial and operating information available on demand. 2nd Watch partners with private equity firms to develop and execute the data, analytics, and data science solutions PE firms need to drive these results in their portfolio companies. Schedule a no-cost, no-obligation private equity whiteboarding session with one of our private equity analytics consultants.


Data & AI Predictions in 2023

As we reveal our data and AI predictions for 2023, join us at 2nd Watch to stay ahead of the curve and propel your business towards innovation and success. How do we know that artificial intelligence (AI) and large language models (LLMs) have reached a tipping point? It was the hot topic at most families’ dinner tables during the 2022 holiday break.

AI has become mainstream and accessible. Most notably, OpenAI’s ChatGPT took the internet by storm, so much so that even our parents (and grandparents!) are talking about it. Since AI is here to stay beyond the Christmas Eve dinner discussion, we put together a list of 2023 predictions we expect to see regarding AI and data.

#1. Proactively handling data privacy regulations will become a top priority.

Regulatory changes can have a significant impact on how organizations handle data privacy: businesses must adapt to new policies to ensure their data is secure. Modifications to regulatory policies require governance and compliance teams to understand data within their company and the ways in which it is being accessed. 

To stay ahead of regulatory changes, organizations will need to prioritize their data governance strategies. This will mitigate the risks surrounding data privacy and potential regulations. As a part of their data governance strategy, data privacy and compliance teams must increase their usage of privacy, security, and compliance analytics to proactively understand how data is being accessed within the company and how it’s being classified. 

#2. AI and LLMs will require organizations to consider their AI strategy.

The rise of AI and LLM technologies will require businesses to adopt a broad AI strategy. AI and LLMs will open opportunities in automation, efficiency, and knowledge distillation. But, as the saying goes, “With great power comes great responsibility.” 

There is disruption and risk that comes with implementing AI and LLMs, and organizations must respond with a people- and process-oriented AI strategy. As more AI tools and start-ups crop up, companies should consider how to thoughtfully approach the disruptions that will be felt in almost every industry. Rather than being reactive to new and foreign territory, businesses should aim to educate, create guidelines, and identify ways to leverage the technology. 

Moreover, without a well-thought-out AI roadmap, enterprises will find themselves technologically plateauing, teams unable to adapt to a new landscape, and lacking a return on investment: they won’t be able to scale or support the initiatives that they put in place. Poor road mapping will lead to siloed and fragmented projects that don’t contribute to a cohesive AI ecosystem.

#3. AI technologies, like Document AI (or information extraction), will be crucial to tap into unstructured data.

According to IDC, 80% of the world’s data will be unstructured by 2025, and 90% of this unstructured data is never analyzed. Integrating unstructured and structured data opens up new use cases for organizational insights and knowledge mining.

Massive amounts of unstructured data – such as Word and PDF documents – have historically been a largely untapped data source for data warehouses and downstream analytics. New deep learning technologies, like Document AI, have addressed this issue and are more widely accessible. Document AI can extract previously unused data from PDF and Word documents, ranging from insurance policies to legal contracts to clinical research to financial statements. Additionally, vision and audio AI unlocks real-time video transcription insights and search, image classification, and call center insights.

Organizations can unlock brand-new use cases by integrating with existing data warehouses. Finetuning these models on domain data enables general-purpose models across a wide variety of use cases. 

#4. “Data is the new oil.” Data will become the fuel for turning general-purpose AI models into domain-specific, task-specific engines for automation, information extraction, and information generation.

Snorkel AI coined the term “data-centric AI,” which is an accurate paradigm to describe our current AI lifecycle. The last time AI received this much hype, the focus was on building new models. Now, very few businesses need to develop novel models and algorithms. What will set their AI technologies apart is the data strategy.

Data-centric AI enables us to leverage existing models that have already been calibrated to an organization’s data. Applying an enterprise’s data to this new paradigm will accelerate a company’s time to market, especially those who have modernized their data and analytics platforms and data warehouses. 

#5. The popularity of data-driven apps will increase.

Snowflake recently acquired Streamlit, which makes application development more accessible to data engineers. Additionally, Snowflake introduced Unistore and hybrid tables (OLTP) to allow data science and app teams to work together and jointly off of a single source of truth in Snowflake, eliminating silos and data replication.

Snowflake’s big moves demonstrate that companies are looking to fill gaps that traditional business intelligence (BI) tools leave behind. With tools like Streamlit, teams can harness tools to automate data sharing and deployment, which is traditionally manual and Excel-driven. Most importantly, Streamlit can become the conduit that allows business users to work directly with the AI-native and data-driven applications across the enterprise.

#6. AI-native and cloud-native applications will win.

Customers will start expecting AI capabilities to be embedded into cloud-native applications. Harnessing domain-specific data, companies should prioritize building upon module data-driven application blocks with AI and machine learning. AI-native applications will win over AI-retrofitted applications. 

When applications are custom-built for AI, analytics, and data, they are more accessible to data and AI teams, enabling business users to interact with models and data warehouses in a new way. Teams can begin classifying and labeling data in a centralized, data-driven way, rather than manually and often-repeated in Excel, and can feed into a human-in-the-loop system for review and to improve the overall accuracy and quality of models. Traditional BI tools like dashboards, on the other hand, often limit business users to consume and view data in a “what happened?” manner, rather than in a more interactive, often more targeted manner.

#7. There will be technology disruption and market consolidation.

The AI race has begun. Microsoft’s strategic partnership with OpenAI and integration into “everything,” Google’s introduction of Bard and funding into foundational model startup Anthropic, AWS with their own native models and partnership with Stability AI, and new AI-related startups are just a few of the major signals that the market is changing. The emerging AI technologies are driving market consolidation: smaller companies are being acquired by incumbent companies to take advantage of the developing technologies. 

Mergers and acquisitions are key growth drivers, with larger enterprises leveraging their existing resources to acquire smaller, nimbler players to expand their reach in the market. This emphasizes the importance of data, AI, and application strategy. Organizations must stay agile and quickly consolidate data across new portfolios of companies. 

Conclusion

The AI ball is rolling. At this point, you’ve probably dabbled with AI or engaged in high-level conversations about its implications. The next step in the AI adoption process is to actually integrate AI into your work and understand the changes (and challenges) it will bring. We hope that our data and AI predictions for 2023 prime you for the ways it can have an impact on your processes and people.

Think you’re ready to get started? Find out with 2nd Watch’s data science readiness assessment.