Merger? Make Your AI Reliable and Find Relevant Data Fast
A merger or acquisition can trigger a data scavenger hunt that leaves everyone frustrated and exhausted.
Mergers and acquisitions in the 21st century include data, a great deal of data. People in the combined company suddenly have access to terabytes or petabytes of data that they didn’t previously have access to and can use that data to make better decisions than in the past. At least that’s the theory.
The reality is more complicated.
Data is the lifeblood of financial institutions, so being able to find the right data quickly and easily is vital. Data leads to insights which lead to actions. How rapidly that process 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 what the new data assets are can be a challenge: 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, just finding out which databases, Confluence pages, Slack channels, and so on, they now have access to can be an odyssey in itself!
A standard approach to accessing the new data is to use ETL processes where we laboriously identify the meaning of the data (and metadata), load it into a data lake, and then make it available. This process is time-consuming and error prone, degrading the timeliness of the data should the conversion take too long. It also means that irrelevant or outdated information is brought in along with useful information. There may be security issues if the data is moved outside the corporate firewall into a public or vendor cloud. And this only works for data in known databases, not everything else.
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 (no doubt delivering the results at a suitably dramatic moment).
Those futuristic AI systems don’t actually may exist just yet, but SWIRL can get gets you close.
SWIRL brings an innovative combination of AI, metasearch, and vectorless RAG capabilities that dramatically shorten the amount of time it takes to find relevant data.
SWIRL will automatically search all the data repositories that you have access to. You don’t have to remember which one was added recently or which ones you used six months ago.
SWIRL supports multiple LLMs and AI systems, allowing you to use LLMs located inside the corporate firewall. You have total control over which AI you use and what data it has access to.
SWIRL is zero-ETL. It brings AI to the data so the data 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.
SWIRL is format agnostic and is capable of reading both structured and unstructured data—including useful data in email, team collaboration apps, office documents, and more (data that would never make it into a data lake). SWIRL doesn’t need the data to be in 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, and your data stays safely inside your firewall.
With SWIRL, you can say farewell to laborious ETL processes, expensive data conversion, and long waits before data becomes available. You can end the data scavenger hunt, benefit from new data as soon as it becomes part of your technological ecosystem, and dramatically accelerate responses to market conditions and customer behavior. SWIRL makes AI reliable so you can find the data you need when you need it.