7 Reasons You Should Consider Outsourcing Your Finance and Accounting

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Successful businesses, no matter how small or large, are able to focus, like a laser beam, on what’s important: innovation, customer service, growth, company culture and winning against their competition.

Finance and accounting, while critical to business operations, is often urgent, but not important. Yet so many executives like you allow themselves to get distracted from these critical success factors by responding to inquiries, tracking down missing checks, making sure the books get closed accurately and reconciling accounts.

If you are looking for better ways to focus on growing your business you should consider outsourcing your finance and accounting operations. Here are seven key benefits of outsourcing accounting and finance.

  1. Be more efficient– For one week, keep track of how you spend your day. (You can easily accomplish this with a free tool called toggl. http://toggl.com ) How much time do you spend each week on finance and accounting? If it is more than 1 – 2 hours a week…it’s too much. You should be spending your time improving operations, better serving customers and growing your business. Removing the daily distractions of accounting will help you do this.
  2. Reduce costs – Outsourcing your accounting eliminates all of the costly taxes and fringe benefits associated with full and part time employees. You pay one fixed monthly fee for everything. Research has shown that outsourcing accounting can save up to 40% in monthly costs, when you consider the salary plus taxes, supervision, vacation and health insurance.
  3. Eliminate fraud –Most small businesses have one accounting person that does everything….sends out the bills, collects and deposit checks and reconciled the bank account. When these duties are not separated, you increase your risk of fraud. A recent Association of Certified Fraud Examiner’s study showed that the most common victims of fraud are privately owned small businesses with less than 100 employees with an average fraud amount of $147,000. Outsourced accounting provides you with the checks and balances, as well as the oversight that you need to prevent fraud.
  4. Highly qualified and experienced staff – By having a team of accountants and CPAs work together to take care of your books, you can take advantage of their significant accounting, tax and compliance expertise which is all included in the monthly cost. By outsourcing you will automatically stay ahead of and comply with the myriad changes in income and sales tax and reporting laws.
  5. Ability to scale – By outsourcing finance and accounting, scaling your business becomes easier. Rather than distract yourself by hiring additional finance staff, outsourcing grows automatically with your business. You can focus on hiring the best people to sell your products and service your customers…which goes right to the bottom line.
  6. Improve cash flow – Outsourcing provides you with access to cloud based tools and technologies that will help you get paid faster and manage payments more effectively. At the simple click of a mouse, you can see an up to minute analysis of your cash.
  7. Better Manage Your Business –What type of information are you receiving today from your finance system? Most importantly, how timely is it? When you get last month’s financial on the 20th of the following month, how do you support decisions in the beginning of the month? Outsourcing provides you with real time information on all aspects of your business, not just financials, with the click of a mouse.
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Automating VM Startup and Shutdown in Azure Resource Manager

This article summarizes the differences in automating starting up and shutting down virtual machines (VMs) between Azure Resource Manager and Classic mode.

Microsoft introduced Azure Resource Manager to facilitate management of your cloud assets in Azure, but given how new it is, it is often difficult to find exactly how to do that.

Earlier this year I created a new Azure VM for a client for use during business hours. The client requested the ability to automate the startup and shutdown of the VM when not in use, in order to save on costly compute time. Implementing a scheduled startup and shutdown of classic mode VMs has been documented and posted to various blogs. It was hard to find how to schedule startup and shutdown when the VMs are managed in the new Azure Resource Manager (ARM).

Classic method

The classic method of setting up auto-start up and shut down involves three steps: create an automation account and configure automation credentials, create runbooks that contain the PowerShell workflow scripts you want to execute, and attach one or more schedules to those runbooks. With Azure Resource Manager, the PowerShell workflow script you use in the second step is different.

In the classic method, you would use code similar to the following to set up your automation.

Startup script

workflow Start_MyClassicVM

{

$cred = Get-AutomationPSCredential -Name “MyAutomationCredential”

Add-AzureAccount -Credential $cred

Select-AzureSubscription “MySubscription”

$myVM = “MyVMName”

$serviceName = “MyService”

Start-AzureVM -ServiceName $serviceName -Name $myVM

}

First you fetch your credentials for your automation account and authenticate using the Add-AzureAccount method.

Once authenticated you designate which subscription context you are using. Often this might be called “Pay-As-You-Go” or “MSDN Subscription”.

You would then specify your service and run the Start-AzureVM command against your VM in that service.

Azure Resource Manager method

When using Azure Resource Manager you would adapt the following code.

Startup script

workflow Start_MyRmVM

{

$cred = Get-AutomationPSCredential -Name “AzureServiceAccount”

Add-AzureRMAccount -Credential $cred

Get-AzureRmSubscription -SubscriptionName “MySubscription” -TenantId “GUID-goes-here” | Set-AzureRmContext

$resourceGroup = “MyResourceGroup”

$myVM = “MyRmVm”

Start-AzureRmVM -ResourceGroupName $resourceGroup -Name $myVM

}

While configuring the automation account, runbooks and schedules remain the same, there are several key differences between automating startup of an ARM VM and a classic VM. Almost all of the methods you use with the Azure Resource Manager will contain “Rm” after the word “Azure” in their name. Also, the statement starting with “Get-AzureRmSubscription” is a bit more complex than in the classic version. Note that now, you need to know not just your subscription name, but also your Azure Active Directory Tenant ID. (Since I did not have access to the Azure Active Directory for my client’s subscription, I retrieved this GUID from the URL in the address bar after navigating to the Azure Active Directory in the classic portal. It’s possible, however, that this will change in the near future.) You then pipe the results of this new command, complete with the Tenant ID, to Set-AzureRmContext.

After that, rather than supplying a Service Name, you supply the name of your Azure Resource Group to the Start-AzureRmVM cmdlet.

Azure menu

Active directory link in Azure Portal menu

Shutdown script

To shut the VM down, the code is near-identical except for the last line:

Stop-AzureRmVM -ResourceGroupName $resourceGroup -Name $myVM -Force

Here is the full workflow script for shutdown.

workflow Stop_MyRmVM

{

$cred = Get-AutomationPSCredential -Name “AzureServiceAccount”

Add-AzureRMAccount -Credential $cred

Get-AzureRmSubscription -SubscriptionName ‘Pay-As-You-Go’ -TenantId “GUID”| Set-AzureRmContext

$resourceGroup = “MyResourceGroup”

$myVM = “MyRmVM”

Stop-AzureRmVM -ResourceGroupName $resourceGroup -Name $myVM -Force

}

KPI’s in Power BI: Not as hard as you think

KPI compare

Power BI just keeps getting better. The addition of the KPI visual to the standard palette is just another example in a long line of improvements, and the subject of this quick article.

Key Performance Indicators have been around long before the computer age. Show of hands: Who has ever browsed the new car showroom and NOT looked at the window stickers listing the vehicles’ MPG ratings? I thought not. While maybe not a true KPI as explained by Gerke & Associates, Inc here, Miles per Gallon is something we all understand when talking about the performance metric of a car. Most cars now come with computerized displays that will show instantaneous MPG, or an average over time. Keep these in mind when we transition this discussion over to Power BI.

In SQL Server Analysis Server cubes, you had the ability to create KPI’s inside the cube. They could then be browsed by the tool to which it was connected, something such as an Excel Pivot Table. Thought slightly different, KPI’s were also available in Analysis Server Tabular Models and Excel PowerPivot, both precursors to the Power BI Desktop.

But data analysts and modelers may experience premature disappointment to find that there is no way to create a KPI when using the Power BI Desktop designer. Knowing it was there in Excel PowerPivot models doesn’t help. It was there before, why did they take it out? Enter the KPI VISUAL.

KPI on Palette

By selecting this visual, you can create a KPI out of any metric in your model. It has three simple fields in the designer to define its appearance: Indicator, Trend axis, and Target goals. Let’s take a look at each of these in turn.

  • Indicator: This is the aggregated column or measure being considered. It could be as simple as the Sum of Sales. You do NOT need to slice this metric down to the latest value, such as [Sum of Sales for the last full month] or anything crazy like that. The base metric will suffice. The reason is explained below.
  • Trend axis: Grab a date type field for this (obviously) and (not so obviously) select the granularity: year, month, etc. Doing so will tell the KPI visual how you want to aggregate the Indicator metric over time.

At this point, your KPI should display two things: a black number and a grey shaded area in the background. We’ll explain all this at the end.

KPI 2

  • Target goal: This can also be an aggregated column or measure similar to the Indicator. If you don’t have one, it’s easy to create a ‘static’ goal by adding a new Measure with a simple static value such as: “KPI Goal = 100″, which is what I did for this demo.

Now, there’s only two options when you get all those things set: either it looks right, or it doesn’t. Consider the following two KPI Visuals from Power BI, both created from the same set of data, both using the same field settings.

KPI compare

The green one on the left I can tell you is how it is supposed to look based on the data I entered. It’s easy to see in the 6 rows that there has been a steady increase over the last 4 years.

KPI Data

So why did my initial attempt at a KPI result in the red one on the right? Apparently there is a dependency on the order in which the KPI is designed. If you selected a metric to add to your canvas first, and then decided to switch it to a KPI, you may get erroneous results. If, however, you start by adding an empty KPI to the canvas and then populating the three fields, you will probably see what you expect. If it doesn’t look right, the fix is quite simple really: remove the Indicator field and re-add it! It may be quirky, but it works.

Now let’s talk about all the pieces of information contained in this one (now correctly formatted) KPI visual. Referring to the green version on the left above, the bold number 120 corresponds to the value calculated (probably summed) for the latest point of the Trend Axis. Based on my data, that is the point for 1/1/1015. Typically, data would be spread over may dates over the years, but the aggregation of the Indicator, and the granularity of the Trend axis will determine the latest point to be displayed. The shaded green area is the trend plot for the Indicator. This shows a decrease at the very beginning, but steady increase after that. Next we see the green check-mark and green shade, indicating that this point is above the goal. (Using the format menu for the KPI you can reverse this if a lower number is better.) Finally, the small black text below the Indicator shows us the Goal, and the distance from that goal.

With this type of control in your hands, you can easily create KPI’s that display the same Indicator, but for different time slices such as by year, quarter, month, week, or day, depending on your needs.

Applied Machine Learning: Optimizing Patient Care in Hospitals, Profitably

Health Care

Why are so many industries exploring Machine Learning as a means of delivering innovation and value?  In my view, the technology speaks to a primitive urge – machine learning is like having a crystal ball, telling you what will happen next.  For a business, it can convey information about a customer before they introduce themselves.  On a personal level, when I consider what I would like to have information about in advance, the first thing that springs to mind is obvious: my health.  Am I about to get sick?  How can I improve my wellness and overall health?  If you are wearing a Fit-Bit right now, then you probably agree with me.

In my last blog, I shared some real-world examples for how the Hospitality Industry applies Machine Learning.  What about Health Care and Hospitals?  While hospitals have similar challenges, in that they accommodate guests who stay overnight, the objectives in health care are quite different, and changing rapidly.   The Affordable Care Act is driving new business models, incentivizing outcome based reimbursement as opposed to volume based reimbursement.  Unlike hotels, today’s hospitals are interested in ensuring their guests do not have to return, at least not in the short term.   They also need to manage costs in a way they have not been incentivized to do in the past.  Hospitals across the country are considering how predictive analytics can have a  meaningful impact on operations, leading to improved patient health and improving the bottom line.

The cost savings opportunity for health care providers is startling. Here are just three examples:

Reducing Hospital Readmissions – in 2014, Medicare fined 2,610 hospitals $428 million for having high hospital readmission rates. Leaving actual fines aside, industry analysts estimate that the overall cost of preventable readmissions approaches $25 Billion annually. As a result, hospital systems all over the nation are mobilizing to intervene, using ML to identify risk factors which are highly predictive of readmission. Carolinas Healthcare System, partnering with Microsoft, did just that. Using data from 200,000 patient-discharge records, they created a predictive model deliver customized discharge planning, saving the hospital system hundreds of thousands of dollars annually. Read the article in Healthcare IT News.

Clinical Variation Management – Mercy Hospital is partnering with Ayasdi to find the optimal care path for common surgical procedures. Using knee replacement as an example, the Clinical Variation Management software helps hospital administrators find clusters of patient outcomes, then enables the exploration of those clusters in order to correlate a metric (i.e. Length of Stay) with a certain regiment or activity. Watch this video to learn how Mercy Hospital saved $50 million by applying Machine Learning to an extremely common procedure.

Improving Population Health – Dartmouth Hitchcock, a healthcare network affiliated with Dartmouth University, is piloting a remote monitoring system for patients requiring chronic care. 6,000+ patients are permitting the hospital to collect biometric data (i.e. blood pressure, temperature, etc.) in order that nurses and health coaches can monitor their vital signs, and machines can predict good days and bad days. Quite the opposite of hospitality: Dartmouth Hitchcock is trying to keep the guests from needing to checking in! Read more about the Case Study from Microsoft.

Are machines taking over for physicians? No. The Patient – Physician relationship remains (and I think will always remain) central to the delivery of personal health care. However, it seems clear that ACA is providing significant rewards to health care providers who manage population risk better. Machines can help here: through data, machine learning can find risk “hiding in the data”.

Contact Blum Shapiro Consulting to learn more about how Azure Machine Learning can curtail hospital readmissions, identify variations in common clinical procedures and improve patient population health.