3 Ways Disconnected Data Can Lead to Financial Loss

When starting a new business, disconnected data is not something most people consider. Oftentimes, ensuring you have systems that integrate with each other is simply not the priority. Many companies will choose software for basic accounting, like QuickBooks to get started. They then use spreadsheets, such as Excel for much of their data. As the company grows, they continue to add systems to assist with various parts of their business. Eventually, they’ll realize they have data in numerous siloed systems. Suddenly, they don’t know how to connect these systems to get holistic insights. This can lead to financial loss and stunted growth of the company. 

The Cost of Disconnected Systems

When data disconnects are present, companies will have difficulty scaling and growing. This can inhibit their flexibility, productivity, budgeting, and planning and can slow the company’s overall growth.

Major Costs

  • Lack of Employee Productivity
    When data is in various, disconnected systems, it becomes difficult to keep track of it all. Oftentimes employees must manually enter data, which can lead to data loss, an increase in errors, and precious time taken away from more important, core tasks.
  • No Real-Time Data Leading to Lack of Meaningful Insights
    When financial systems are disconnected, it is impossible to come up with meaningful insights in a reasonable amount of time. For example, if you have your accounting and bookkeeping in one system, track transactions in another, and use yet another for budgeting and forecasting, it becomes impossible to see the big picture in real-time. It can take too long to source, extract, and analyze the data leaving important financial insights up to chance. In fact, 46% of business leaders say disconnected document processes impair their ability to plan, forecast, and budget because of lack of visibility. This can lead to major missed growth opportunities and put a big dent in a company’s bottom line.
  • Integration Complexity & Cost
    Eventually, to make sense of the siloed data, integration becomes a topic of conversation. At this point, there are multiple systems that don’t “talk to each other” and the company needs a solution so they can continue to grow. The next step is to ask IT to find a solution to connect all of these systems. This wastes valuable time and money where IT could be working on more important tasks. Furthermore, it isn’t a one-time setup and done. It takes time to maintain and anytime a new version of any of the software system releases, IT would have to create new connections. This leads to both wasted time and skyrocketing maintenance costs.

Why Integration is Important to Avoid Disconnected Data

I know what you’re thinking. I’ve just told you data integration can be expensive and time-consuming. While this is true, we have also seen why having connected data is incredibly important. However, we don’t want you to reinvent the wheel and try to transfer all of your company’s financial data to an entirely new system. The key is finding a solution that frees up your IT team to do what they do best, while also having real-time, meaningful insights. That is where DataBlend comes in. We can quickly and easily set up connections between all of your financial systems so you always have up-to-date information at your fingertips. This means you can choose the best individual solutions for each aspect of your company while not having to worry about how they can integrate.

Data Lake

Data Lake vs Data Warehouse: What You Need to Know

Data Lake vs Data Warehouse is a conversation many companies are having and if they’re not, they should be. However, more often than not, those who are deciding between them don’t fully understand what they are. For this reason, I will be breaking down the details of why one would choose a data lake vs data warehouse in the simplest terms possible. So, let’s get to it and learn the difference, without all the unnecessary technical jargon.

5 Ways To Utilize DataBlend’s Adaptive Insights Export

One of DataBlend’s most powerful capabilities is the Adaptive Insights export. The ability to export data out of Adaptive allows the user not only to integrate with other systems, but integrate with Adaptive itself. In addition, data is exported in a very clean format using DataBlend’s intuitive Query Builder, eliminating the need for any complex transformations. Below are 5 examples of how the Adaptive Insights data source can be utilized.

1. Exporting data from one version and importing to another

One of the most useful capabilities is moving data from one version to another with the click of a button. Common workflows for this include static data imports, moving actuals to plan versions for scenario planning, integration between different sheet types (i.e. modeled to cube), or even instance to instance integration (production to sandbox). All of these examples will drastically save time, as this is typically all done with manual export/import processes and Excel manipulation.

2. Zero out data from planning versions

A great feature of Adaptive is the ability to erase actuals for fresh data imports. However, the ability to erase data for particular accounts or levels in planning versions is not available, unless you create a new blank version. With DataBlend, you can create a workflow that will export the data with the elements you choose, and then you can easily convert all of that data to zeros and import back to Adaptive. This gives you a clean slate to either import a new set of data or enter in new budget or forecast data.

3. Push data to a flat file

Full data extracts out of Adaptive can be useful, whether it’s for a reporting baseline or using the data for an import to another system. With DataBlend’s clean Adaptive Export, you can get an Excel or CSV output in perfect format. Using DataBlend’s ETL capabilities, you can even make the most complex transformations simple.

4. Push data to another system

DataBlend has bi-directional integration capabilities with Adaptive. Not only can ERP and CRM data be imported to drive forecasts and budgets, but data can be pushed back to those same ERP and CRM systems as well. This is important for stakeholders who may not have access to Adaptive, since it enables them to view budget, forecast and metric data in the systems they do have access to. Furthermore, Account and Department structures along with other metadata can all be aligned from system to system.

5. Export prior month balances to create current month balances

A unique but fairly common usecase is the ability to export prior month balance data in order to create current month balances. This comes in handy if your ERP or GL system can only export periodic change, or “delta”. Combining deltas with prior month balances in DataBlend generates current month balances, which is useful if your historical Actuals have already been set as “Balance” without having to change the account type and re-import history.


Take a look at how easy it is to build an Adaptive Export in the video below, and request a demo today!