Five Steps to Reducing Your Paper Processing Costs
Are your document processing activities efficient?
Intelligent data capture can save valuable time in end-to-end document processing to drive lower overall costs. Integrating useful information earlier and more reliably can provide an improved customer experience, less manual data entry that in turn improves data quality, and greater security controls.
Increase Efficiency and Reduce Document Processing Costs with Intelligent Data Capture
Effectively managing document processing activities is critical to an efficiently run department and your business success. It can help raise employee productivity; reduce the cost associated with processing documents such as applications, forms and invoices; and strengthen security and improve cycle times in customer service. Intelligent data capture is an approach that can help you streamline document processing activities and realize these and other goals.
Defining Intelligent Data Capture
Before looking in more detail at the business benefits and implementation strategies connected with intelligent data capture, let’s start with how Canon Business Process Services defines the term.
Intelligent data capture is the process of automatically scanning traditional long-form documents or electronic pages, extracting specific data (regardless of its structure) and integrating information with downstream workflow processes and systems.
When implemented correctly, this approach can save valuable time in end-to-end document processing, which in turn can drive lower overall costs. Other benefits of integrating useful information earlier and more reliably in the document management process include, as spotlighted earlier, an improved customer experience, less manual data entry that in turn improves data quality, and greater security controls enabling an enterprise to better meet today’s compliance requirements.
Capture the Right Information to Improve and Optimize Your Team’s Workflow
With this definition in mind, let’s begin our closer look at intelligent data capture with why we at Canon believe it matters. According to our own industry research and discussions with clients and subject matter experts, approximately 80 percent of an organization’s information assets exist as unstructured content. In a sense this means that the vast majority of your company’s “intelligence” is trapped inside unstructured, unmanaged documents. In this scenario, a tremendous amount of useful information is not maximized via processes and technologies that can efficiently move it into workflow systems that support and help grow the business.
Trapped does not necessarily mean stuck forever. In reality, the data is caught in a bottleneck that hinders its flow to downstream systems, some of which can include:
- Enterprise risk management systems
- Client management software
- Business intelligence dashboards
- Private or public cloud repositories that are used for collaboration
Your Structured vs. Unstructured Data Impacts Downstream Processes
The key to unlocking this trapped data so it can more effectively support downstream processes is to extract it. We’ll look at this process in more detail shortly. First, it’s important to understand two concepts. The first is illustrated by the infographic below, which contains a line graph representing the level of technology required to extract data from a document.
The left side of the illustration indicates that less advanced technology is required to extract data while, as we move further toward the right, the technology becomes increasingly more robust to accommodate the need to make more complex decisions. The reason for this is that within structured documents (lower left), the essential information is consistently located in the same place. Examples of these documents include government I-9 and medical HCFA (Health Care Financing Administration) 1500 forms.
Within semi-structured documents (middle of the line), information generally is found in the same location with some variation in how data is displayed, depending on the individual company. Documents such as invoices and purchase orders fall into this category. Unstructured documents (upper right) display information with the most variation. Data could be located anywhere within these documents, which typically include contracts, correspondence, reports and business proposals. Therefore, extracting data from these documents requires more complex decisions, driving the need for more sophisticated technology.
Besides document structure, the second concept that companies need to be clear about is that data enters an enterprise in many different formats. Ironically, technology has actually increased the number of these formats, not reduced them as was anticipated a decade ago. Data is received in the form of paper documents as well as faxes, and emails with attachments. Beyond that, information is transmitted as Microsoft Office files (Word, Excel and PowerPoint), PDF documents and more. This is compounded by the fact that data can enter your company from different devices (desktop, laptop or mobile) and multiple locations within and external to your organization.
The Level of Technology Required To Extract Data From A Document
The Importance of Data Extraction
As pointed out earlier, businesses extract data in order to make downstream systems and workflow processes more efficient and cost effective. This in turn enables an organization to grow, maintain a competitive edge and realize other significant benefits. One example of the value of data extraction concerns an accounts payable solution.
Canon teamed with an investment bank that wanted to improve its extraction process and technology. The company had moved its approval process offshore, but wanted to reduce the cycle time for getting invoices into its payment system. Additionally, the bank saw an opportunity to reduce the staff required to manage this process so it could redeploy resources to more strategic areas of the operation.
While our project included using OCR technology to extract and index a variety of key data points such as vendor name, invoice number and line item detail, the purchase order number was especially important. Once validated, purchase order invoices could be paid without a departmental approver, which could reduce processing cycle time by as much as a week.
Also, the information for all invoices, including those that required approval, would flow more quickly into our client’s ERP system, which triggered the approval workflow. Due to the enhanced extraction process and technology we put into place, the bank reduced processing cycle time from seven days to one and reassigned 10 staff members to areas that more directly supported its core business. These results indicate the potential business value of effective data extraction.
Five Steps to Implementing the Intelligent Data Capture Solutions
Now that we’ve established its potential, let’s examine intelligent data capture at work, including five key steps to implementing a solution. The basic flow of the capture process, begins with how data typically enters an organization in many different formats that are sent from multiple locations, as noted earlier.
The information is captured using scanning technology, comprising hardware and software. OCR is applied, and at this point the real power of the approach begins to be harnessed. Data is extracted using business rules, which specify such details as how a document is recognized: what information is needed from a particular document type as well as how this information is identified, where it is sent and in what format.
This is a broad view of how the process works, but how can an organization plan to implement an intelligent data capture solution? At Canon we have determined five essential steps that can support a successful implementation.
Step 1: Centralize Intake
Step one is to centralize the intake of information. This is perhaps the most important step in the process of implementing a data capture solution. It revolves around understanding how and where useful data is being received, and in what formats.
Another key to this step is for the organization to identify the type of business model it wants to use for the capture process: internally managed or outsourced. We’ll examine some pros and cons of each model later in this paper. The point for now is that, depending on the model chosen, the enterprise will want to designate a point of data capture and begin to consolidate information streams through that point. The reason for this is to help ensure that all useful data will be handled by the new workflow and that duplication of efforts will be avoided as much as possible.
It is important to note that centralizing intake does not have to disrupt ongoing processes that the company wants to retain. For example, perhaps the established practice is to send invoices to different locations. The organization might want to continue this approach so that the locations can efficiently maintain current vendor relationships. In this case, the invoices could be scanned locally at multifunctional devices, and then the electronic images could be automatically routed to a central point for processing, keeping the local workflow intact.
Step 2: Automate Capture
Step two involves automating the document capture process using OCR and business rules. Implementing new capture systems of this type can require a substantial investment. Some of the more complex software systems are not easy to deploy and many IT departments lack the expertise to install these solutions. Consequently, service costs from vendors can accumulate.
One way to contain these expenses is to use a phased approach. This includes identifying the important channels and/or locations and bringing them online one at a time. In this manner a company can purchase fewer licenses and reduce installation time and associated costs.
Another possibility is to leverage the expertise and technology that the right managed services provider can deliver. This approach can work well regardless of the centralization model, but is especially helpful for organizations deploying an offsite solution because they can take advantage of the provider’s technology and infrastructure.
Step 3: Classify Documents
The goal of step three is to virtually eliminate manual document sorting by classifying document types, beginning with the most important. One challenge in this step is to ensure that explicit rules exist to identify or classify the document types. In our experience working with clients, we often find that manual processes, especially those in place for a long time, may not have specific written rules for document classification. For example, how does the company identify purchase order and non-purchase-order invoices? Is the purchase order number required to be on each invoice? Another challenge is that it may be a regular practice for processors to read some unstructured documents in order determine their type, such as a contract or proposal.
Companies can meet these challenges by, first, writing rules for document classification, starting with documents that are either most important or easiest to classify. Second, the implementation team can undertake an analysis designed to help gain better control of unstructured documents. This could involve talking to processors and determining what key words or phrases they look for when classifying documents. Third, the team can investigate the possibility of leveraging a solution that is commonly referred to as clustering. This is a machine learning technology that involves scanning and automatically grouping documents together that have similar characteristics. The document groups can be reviewed and corrected, and the clustering system continually adjusted, so that over time it can identify document types without the need to code a series of complex rules.
Step 4: Extract Data
Step four revolves around cost-effectively extracting data. This means reducing labor costs associated with data entry. The key point is that in its current document processing, an organization may use manual data entry which, being labor intensive, causes the overall document management process to be more costly and error prone than it needs to be. Additionally, manual data entry keeps the document processing system from being scalable because there is a limit on how many characters an individual can key in a given period of time. Finally, if more processors are required, the learning curve for training them is going to differ for each person.
To resolve this situation, an enterprise can take a few initiatives. One is to determine critical data points and the rules for finding them. Then, leverage software to automatically extract those data points based on clear rules. Finally, assign manual steps only to exception processing.
Step 5: Export Data
The objective of step five is to distribute—or export—the extracted data to areas within the organization where it will be used. As with the previous steps, there is at least one key challenge involved. In this case, creating custom links between capture systems to downstream platforms, such as an ERP system, can be expensive and usually require professional service support. There are a few ways to help alleviate this pitfall:
- Document the requirements for the downstream systems. These systems may be able to accept information in more formats than expected, enabling an “out of the box” export from the capture system to work.
- Use a phased approach, connecting to one system at a time. This potentially reduces the amount of professional services needed at once.
- Leverage a managed services provider with the right experience and technology. The latter might include a connector that works “as is” or could work with a few changes.
Managing Documents Internally vs. Outsourcing
The earlier section highlighting Step 1 (Centralize Intake) noted that a key to this step is for an enterprise to clarify the business model it wants to use for managing the capture process: internally or outsourced. Let’s briefly examine some pros and cons of each model.
On the plus side, the organization maintains control over implementing and maintaining the capture process. Further, the company’s IT department controls the installation, which occurs on an internal network.
There are several issues on the minus side. First and foremost, the company has to bear the entire cost for the implementation, including hardware, software, training and project management. Another challenge related to implementation is that many IT departments do not have the requisite expertise with capture systems to ensure a smooth install process. Additionally, implementation might require transitioning from legacy systems. Depending on the age of the systems, such a transition could be costly. And looking ahead, as the need to scale up arises due to changes in the business, the company may need to purchase additional equipment and software licenses, and incur other related expenses.
The “pros” of the outsourcing model include the fact that, because an organization is leveraging the expertise, processes, technology and economies of scale connected with a service provider, there is the potential to realize a greater return on investment over time. Additionally, a service provider often provides the latest technology upgrades and security updates as part of an outsourcing agreement, freeing the organization from these concerns. Another significant benefit is access to a service provider’s pool of technology and processing experts, who can help ensure the success of future projects.
The main potential drawback to the outsourcing model is that—due to security concerns for documents containing sensitive data such as employee benefits and compensation—some document management activities may need to occur offsite, away from where employees are located. However, there is a “political” issue connected with offsite document management. Some executives are simply not comfortable with an offsite, outsourced model because they believe that this generally poses a higher security risk. However, in our experience offsite operations managed by a services provider are often protected with greater security than a company’s internal, onsite systems because the provider’s offsite facilities have to meet a much broader range of industry regulations and guidelines.
Whether your company is looking to reduce document processing costs, boost employee productivity, safeguard against business interruption or respond to internal and external customers faster, intelligent data capture can help achieve these and other critical business objectives.
Developing a document management strategy that will help you achieve these goals requires expertise and best-in-class technology. Canon Business Process Services has over 20 years of experience providing end-to-end imaging, document conversion and indexing services, and we are here to help you. You will find that intelligent data capture is among the best investments you can make for your business.