Data stack

The faster your sales decide to grow, the harder the business prediction becomes. This blog is about how a data tech stack can help teams to make better decisions faster, when the business scales up, at the same time when cash burn and customer behaviour are hard to predict.

When data stack is scalable?

When you start sending and receiving payments, you start building business-critical data. All relevant business data is on ERPs, invoicing or payment systems, and bank accounts. Data flow starts from invoicing or payment software and ends up on the cash, sales, or purchase predictions. Data stack extract data from your ERP, banking, and maybe CRM software and predicts insights about your Cash burn, Runway, Sales, Purchases, and so on. That's why it's important to choose data sources that enables extracting data. The decisions about choosing the data sources. like ERP, invoicing, and banking are important, as it enables that data stack scales when your business scales, and can predict future spurring risk-taking.

Data stack can connect data sources into prediction models which can predict for example future Cash burn without a second of manual work.

Rule number one, choose an ERP that has open APIs

An enterprise resource planning (ERP) system, the central repository for financial data, is designed for accounting and it only gives a backward-facing view. With that lag, you have difficulty assessing cash burn, insights about sales growth, customer behavior, or runway in real-time.

If you are scaling, your focus is on sales and your team needs to get data insights and predictions without a second of manual work. Rule number one is to make sure, that your ERP has open APIs. As your business grows and becomes global, you need the financial data to predict cash position or integrate with the other modern data layers.

Do business with a bank that is PSD2 compliant

The same goes for your banking. If you operate in Europe, make sure that your bank is PSD2 compliant, which enables you to adapt new technologies to better manage your banking operations. With software that connects to your bank accounts, you can predict the cash position, FX risk, debt, and money movements in real-time.

Turn precious data into predictions and insights using machine learning

Sign up in 8 minutes → here and instantly see the insights. Outgoing cash, like purchase invoices to suppliers, salaries, taxes, AWS monthly billing, or Hubspot fees can be done on spreadsheets but it’s slow and sensitive for human errors. But incoming cash, like Customer purchase and payment behaviour, can’t be done on a spreadsheet because Customer purchase and payment behaviour are sporadic. At the same time, there is a lot of precious data on invoices, that can be turned into predictions about customer's future behavior or business-critical KPIs about purchases.

peace of mind using prediction you can trust

Cash prediction can be a painful process, if we do invoices and payments using different software, like Stripe for customer payments, accounting software for salaries and taxes, credit cards for AWS, Hubspot, Slack, Google, and so on. Or we use local ERP in the home country, but Netsuite or Xero in subsidiaries. Having several bank accounts increases the difficulty level. This is where data stack and machine learning can help you.

Predict future with superior accuracy

Using open APIs you can connect an unlimited number of companies, billing software, or bank accounts to your cash prediction that updates automatically without a second of manual work. Data fusion from your ERP, CRM, and banking software combined with machine learning works your business weather forecast. It can predict your customer purchase and payment behavior and even invoices that have not been sent, more than 90 days earlier. Effortless, yet with super accuracy. Turn precious data into confidence. Have confidence about the future without a second of manual work. Make better decisions ten times faster predicting customer behavior. sign up in 8 minutes → here instantly get the insights