Growing Your Multi-Entity Business with Sage Intacct
Are you adding new locations? Opening another office? Congratulations! Your business is on a growth path. While it’s exciting to cut the ribbon or open the bottle of the bubbly to celebrate, challenges await. New entities add complexities to your organization that your QuickBooks financial software isn’t built to handle.
Some of these include: decentralized payables, inter-entity transactions, multiple currencies, and global consolidations—all of which require a purpose-built financial management system such as Sage Intacct. Whether your business is global or domestic, simple or complex, now you can close the books faster and gain the visibility you need even as your operations grow and evolve.
Get quick and accurate closes with automated consolidations
Finance teams in distributed organizations struggle with QuickBooks’ inability to quickly consolidate information at period close or when it’s needed for decision making. Sage Intacct’s multi-entity structure allows you to consolidate multiple entities with just a couple of clicks. Now you get accelerated closes, improved accuracy, and better visibility. Plus, every global consolidation includes a detailed journal entry report for easy audibility.
Easily manage multiple currencies
Sage Intacct automates the management of multiple currencies, saving you time and increasing accuracy. You’ll use up-to-date exchange rates for currency conversions and revaluations, and instantly access information on currency gains and losses. You can also produce reports in your headquarters’ currency or the entity’s local currency.
Save time and effort with simple setup and maintenance
With Sage Intacct, all your business entities reside in one system. You can quickly set up policies, procedures, workflows, and reporting for new entities regardless of the complexity of your multi-entity structure. For example, you can easily configure each new entity with unique definitions. Or automatically “inherit” centralized definitions—workflows, charts of accounts, period definitions, and lists—across entities for a standard, organization-wide configuration. You can also opt for automated inter-entity transactions with manageable rules, making it easier to centralize payables and receivables.
Give the right people the right access with permissions
Sage Intacct provides permissions that can be scaled to just the location the user works in and to just the area that person needs to see. For example, you may want a person to be able to enter sales orders for their entity, but they don’t need to see everything that’s going on in purchasing. Also, you may want a manager to be able to pull a report just for their department without being able to delve into the financial information of the whole company.
Take advantage of fast, flexible reporting
Giving people self-service access lets them get to the information they need—when they need it—to help their part of the company grow, and you don’t become a single source for constant requests. And Sage Intacct makes it easy to quickly produce accurate financial reports, regardless of your organization’s complexity. You can get insights from real-time visibility with instant roll-up reporting (single currency) or push-button consolidations (multiple currencies). What’s more, you can easily switch between consolidated and local views for further insights into the figures.
Growing businesses trust Leaf Software Solutions
QuickBooks, while excellent for new businesses, wasn’t designed for the complexities of multi-entity organizations. Whether you’re expanding geographically or diversifying your business, rely on Sage Intacct to keep you in control across your company. The professionals at Leaf Software Solutions can create and implement a technology solution that meets your unique business requirements—now and as you grow. Contact us by phone or email and let us know how we can help.
An example of using real data from SupplyKick as an ML experiment
So, what exactly is Machine Learning?
Machine learning is a growing concept in computer science, where scientists attempt to teach computers to learn abstract concepts just as a human would. This is a major paradigm shift from traditional programming, in which programs operate using a set of logical rules that are explicitly stated by programmers. In machine learning, the programmers instead develop a “model,” a structure that is able to learn the data that the user feeds into it.
The first thing we need to do was get an actual dataset to manipulate. We did so using Amazon’s Marketplace Web Services API. We pulled in the responses from this API and stored them as CSV files that are easy for our program to read. We then clean the data by filtering out repeat entries or records that don’t contain any data at all. Because the goal is to predict future sales, we ordered the rows by date and totaled all orders on the same day. Finally, through experimentation, we found that the model fits best to data for individual products; rather than for all products, so we filtered down to only rows containing a specific product. The result of all of this cleaning is plotted in blue.
Let’s try getting some predictions! After no training, here’s how well the model fits to the training data.
It’s pretty terrible, but this is what we expect without any training. This would be like walking into a calculus test without even having heard what “math” is. After training on the data 100 times we are given the following result.
It seems to be picking up the general trends in the data but doesn’t quite hit the mark. Lets try doing another 900 iterations through the data, for a total of 1000 training runs.
Now the prediction is clearly fitting to the actual sales trend. Also, keep in mind that although 1000 iterations sound like a lot, the entire training was completed in about 15 seconds. However, the real challenge is fitting the model to data that the model has never seen before. Below are the predictions on the “test” dataset.
Though these predictions aren’t quite as good as on the training set, this is expected, and they are still very accurate overall and likely more precise than a human’s predictions.
What this could look like for you
- 3Blue1Brown- YouTube channel with amazing visual representations of abstract mathematical concepts
- Two Minute Papers- YouTube channel that distills recent machine learning research papers down to 2-minute videos
- Siraj Rival- YouTube channel with tutorials on lots of machine learning concepts
- Google Machine Learning Crash Course- Web tutorial series on machine learning (has more of a business/real-world focus)
- Machine Learning Mastery- Website with tons of machine learning tutorials and resources
- Colah's Blog- In depth explanation of ML concepts by Google Brain Scientist
- Distill- Explains abstract concepts through interactive machine learning widgets
How to Enable ‘Doc Date Verify’ using Professional Services Tools Library
By Samantha Higdon, Microsoft Dynamics GP Consultant
Once PSTL is installed, configuring Doc Date Verify is a simple process
Entering a valid date does not prevent a user from keying a document date as 8/1/2018 instead of 7/1/2018 if both periods exist and are open in GP, but it is a great tool to capture those transpositions of numbers that put dates in nonexistent fiscal periods which are much harder to correct!
How are Microsoft 365 ENTERPRISE and Microsoft 365 BUSINESS, Different?
By Justin Kruse, Microsoft Dynamics 365 Senior Consultant
Microsoft 365 Enterprise:
Microsoft 365 Business:
Amazon Web Services
Best Companies Group
Best Places To Work
Chamber Of Commerce
Chief Financial Officer
Cloud Financial Software
Doc Date Verify
Dynamics 365 Apps
Indiana State Museum
Internet Of Things
Joseph Graves Associates
LCD Welcome Screen
Microsoft Dyanmics 365
Microsoft Dynamics 365
Microsoft Dynamics CRM
Microsoft Dynamics GP
Microsoft Visual Basic
Migrating Cloud Applications
Mobility & Security
Not For Profit
Off The Circle
Professional Services Tools Library
Project Service Automation
Python Tornado Web Framework
Repetitive Stress Injuries
Single-Page App Framework
Traditional Vs Machine Learning
Unit Test Fixtures