The important steps after a Merger and Acquisition: Streamlining Data Access with AI

Make Your AI Reliable and Find Relevant Data Fast

A merger or acquisition can trigger a data scavenger hunt, leaving everyone frustrated and exhausted. Mergers and acquisitions in the 21st century include a great deal of data. People in the combined company suddenly have access to terabytes or petabytes of data they didn’t previously have access to. They can use that data to make better decisions than in the past. At least, that’s the theory.

The Data Dilemma in M&A

Data is the lifeblood of modern finance, fueling insights and actions. But post-merger, the sheer volume and variety of data can be overwhelming.

How rapidly the journey from data to insight to action plays out—and hence how quickly the company can respond to changing conditions—depends on how easy it is for analysts to find the data they need. After an acquisition, merely figuring out the new data assets can be challenging: databases may not be well-documented, data may not be well-organized, or the data may be in an unfamiliar technology. For any given employee, finding out which databases they now have access to can be an odyssey!

Challenges include:

• Unfamiliar Systems: Databases may be poorly documented or utilize unfamiliar technologies.
• Disorganized Information: Data may lack structure and standardized formats.
• Access Issues: Determining which databases are accessible can be an odyssey.

The Pitfalls of Traditional Data Integration Approaches

The standard approach to accessing new data involves laboriously identifying its meaning (and metadata), loading it into a data lake, and making it available.

This process is time-consuming and error-prone, and if the conversion takes too long, it will degrade the timeliness of the data. It also means that irrelevant or outdated information is included along with useful information.

The challenges with traditional ETL are:

• Time-consuming
• Error-prone
• Security issues may exist if the data is moved outside the corporate firewall into a public or vendor cloud.

The traditional ETL approach may also bring in irrelevant or outdated information along with useful data.

Leveraging AI for Efficient Data Management

Fortunately, we have AI to help with the task of understanding data. The trick is to benefit from AI without going through the entire ETL process to move the data into a data lake or a vector database and without moving the data outside the firewall. In Star Trek and other science fiction shows, autonomous AI systems would simply analyze all the data, figure out what is useful and what isn’t, and automatically make the right decisions based on the right data.

Those futuristic AI systems don’t exist, but SWIRL gets you close.

Introducing SWIRL

SWIRL AI Connect brings an innovative combination of AI, metasearch, and vectorless RAG capabilities that dramatically shorten the time it takes to find relevant data. SWIRL will automatically search all the data repositories to which you have access. You don’t have to remember which one was added recently or which ones you used six months ago.

SWIRL AI Connect supports multiple LLMs and AI systems, allowing you to use LLMs inside the corporate firewall. You have total control over which AI you use. SWIRL brings AI to the data so it can remain where it is. No need for laborious and time-consuming ETL processes to move data around before it can be used—SWIRL queries data where it sits, uses AI to analyze the results, and quickly sorts the results by relevancy to the original query.

Key Features of SWIRL AI Connect

SWIRL is format agnostic and can read structured and unstructured data—including useful data in email, team collaboration apps, office documents, and more. SWIRL doesn’t need the data to be a vector database to use RAG, accelerating data access, increasing the amount of data SWIRL can search, and significantly improving the relevancy of the results compared to other systems.

SWIRL integrates with existing security protocols, ensuring your data stays safely inside your firewall.

SWIRL Gets You More

AI and Banking Industry Trends: The banking industry increasingly uses AI to enhance efficiency, decision-making, and customer experience. AI can help banks integrate and analyze vast amounts of data swiftly, which is crucial during and after mergers. Read about how SWIRL can enhance your banking experience.

Regulatory and Compliance Challenges: Integrating data post-merger also involves navigating complex regulatory landscapes. Ensuring compliance with regulations like GDPR and maintaining robust data governance frameworks are critical.

Get in touch with the team and book your FREE SWIRL DEMO.