Finance operations and organizations have the exciting opportunity to use machine learning and advanced algorithms to improve forecasting supply chain and operating efficiencies. Watch our quick 3-minute video to hear our Director of Advanced Analytics, Kyle Clubb, explain how it works.
Businesses have realized that data is important to all parts of their operations and strategic planning. Issues that they have, typically, it's just the timeliness of the data, the ability to have the data they need to make the best decisions possible in a timely manner.
So a lot of businesses get the data they need, but sometimes it's a month too late, a week too late, it's not fast enough or it's not reliable enough. With finance, especially, it's essential to have the right data at the right time to make short-term and long-term strategic decisions.
There's a lot of really intelligent people in finance that we talk to every day and executives and they feel uncomfortable sometimes, they're making a lot of subjective decisions, they don't have all the facts. But they do have to make decisions, non the less, quickly.
A lot of the times, they're really handicapped with the software package or the financial package that they have. A lot of times they do have parts of the organization that can transform, manipulate data that's unstructured or structured and presented, but sometimes it takes a long time, or it's not efficient.
Unstructured data usually means that it's not in a fully organized format. So it may be something just like streaming ones and zeros for IOT devices, binary, it could be PDF files, it could be maintenance records that were handwritten from 25 years ago.
The cost of not having the right data or untrustworthy data, it could cost millions of dollars. There may be external macro indications like what's my competitive landscape? What's the current world conditions? It could be something as simple as not forecasting that a supply chain vendor was going to have issues that's disrupting manufacturing.
Finance organizations are struggling with antiquated algorithms for forecasting and models that are, frankly, hundreds of years old. And a lot of times, they just don't know that there's better algorithms out there. They can take advantage of more sophisticated neural processing. There's an emergence of quantum that's coming on the scene once that hardware becomes cost effective.
Well, we feel the main need is firms that really understand big data, can understand the technologies behind it, and also have the acumen to do the analytics on that data that's can be quickly customized for their unique situation.
We're filling in the gaps were a lot of pre-packaged software can't solve for. So it may be as simple as just understanding, at a more granular level, certain parts of the business. And it could be macro data analysis for what's my cash flow analysis and forecasting for the future and how would I mitigate risk?
But a lot of times, as we go through and we look at operations, we find potential where we can bring machine learning and advanced algorithms to help a lot with forecasting supply chain and operating efficiencies can get improved.
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