Through the rapid adoption of wearables, the proliferation of data-driven product recommendation systems and the advent of the smart car, data has infiltrated everyday life. Everything, it seems, has the capacity to be quantified and analyzed.
It’s not just our personal lives that can be measured, tracked and optimized. When it comes to the workday, enterprises are realizing that data is the key to increasing productivity and gaining a competitive edge. Analytics has found a place in nearly every function of every industry, making it an essential component of next-generation business intelligence.
Using data analytics, information workers can quickly monitor, report and analyze business processes to make informed decisions. From solving complex business problems to gaining deeper insight into business activities, data makes employees more efficient and even predicts bottlenecks in operations.
The demand for data analysts, tools and resources is at an all-time high, with advanced data analytics presenting exciting opportunities for business process optimization. Gartner predicts that the revenue in the business intelligence and analytics market will grow 5.2 percent in 2016 and, in a recent study, International Data Corporation (IDC) forecasts that worldwide business analytics services spending will surpass $100 billion in 2019.
Even organizations that do not have a large staff dedicated to data science can thrive in today’s data-driven market with the proper framework and tools in place. Yet as newer and more sophisticated data and analytics tools become available, organizations that have not assessed the quality and accuracy of both structured and unstructured data must start now in order to keep pace with a rapidly changing landscape.
Laying the Groundwork
According to a study by the University of Texas, Fortune 1000 companies could gain $2 billion a year in employee productivity by increasing usability of their data by 10 percent.
Nearly 80 percent of enterprise data — including emails, photographs, video, social media content and text documents — is unstructured. However, most organizations remain unable to leverage this data.
In order to increase the usability, volume and quality of data, an organization must first and foremost harness and manage its unstructured information. This is where a solid foundation in enterprise content management (ECM) becomes a necessity. ECM systems facilitate unstructured data capture and management by:
• Centralizing all data types — including emails, photographs, video, social media content and electronic and scanned paper documents — in one repository
• Running optical character recognition (OCR) on document text so it is accessible, searchable and easier to categorize
• Enabling the automated and accurate capture of metadata that would otherwise need to be manually entered
• Automating manual process steps with workflow automation to reduce errors and delays
• Digitally capturing, routing and approving forms to accelerate and optimize forms-based business processes
By using ECM to re-engineer business processes, organizations are making information workers’ jobs much easier while simultaneously making them far more productive. For example, an ECM system automatically reconciles invoices — which are often received in various forms and templates—by recognizing vendor information and matching invoice line items to corresponding purchase order line items, even when data does not exactly match a company’s existing data or when words are misspelled. Invoices are automatically routed to multiple employees for review, approval and check processing. Ultimately, automation drives more timely payments, improves accuracy and efficiency, and lowers costs — while freeing employees to focus on other tasks.
Yet when it comes to workflow, it’s not just about providing tools for IT — it’s about democratizing business process automation tools and making them available to and usable by business leaders. With traditional business process automation tools, managers must wait for available IT resources before process automation can begin. Using flexible ECM automation tools such as workflow and electronic forms, business leaders are empowered to remove bottlenecks. They can use their process knowledge to drive improvement, working with IT to accelerate digitization and automation. This accelerates the transformation cycle so organizations can begin to optimize processes using data-driven insights.
Leveraging Analytics for Process Optimization
Like many organizations, Texas A&M AgriLife, an education and research institution that is a part of the Texas A&M University System, implemented an ECM system out of necessity. After natural disasters threatened its physical document storage facilities, AgriLife needed a way to safely store documents.
After solving the immediate need of digitizing documents, AgriLife began to investigate process automation. In short order, staff used the ECM system to implement workflows including accounts payable, purchase orders and payroll changes. This automation provided immediate results of time and cost savings, but also enabled AgriLife to begin building a structured system to collect and grow data for analysis.
The Texas A&M University System now offers Laserfiche as a shared service that is supported through its central IT office, meaning all of its colleges and departments can leverage the software to automate processes. This strategic centralization lessens the burden on IT workers as departments and colleges share critical data and best practices for business process automation and optimization.
Enterprise-wide adoption of a common ECM system simplifies data-sharing, key to ultimately reaching a point where an organization can benefit from advanced analytics. ECM enables organizations to simplify and consolidate processes that can be leveraged by multiple departments. Although organizations may start by automating just one business process or department, ECM can scale rapidly throughout the enterprise.
As the number of automated processes grows within the enterprise, so does the data, giving institutional leadership greater insight into processes and granular actions based on actual business execution across its departments and organizations. This process-based analysis provides the groundwork for the organization to systematically improve operations and build to increasingly advanced levels of analytics.
The Journey to Predictive Analytics
Automation provides a consistent framework with which to store accurate and complete process information. In the past, organizations have relied heavily on anecdotal information when improving processes, but the data gathered from automated workflows enables an organization to accurately and granularly examine how people are using or interacting with documents.
Analysis of this data is called descriptive analytics, as it describes what has occurred in the past, facilitating an informed response. Descriptive analytics can reveal unnecessary activities, uncover bottlenecks and help managers identify dominant activities that take the most time and effort in a process — all information that managers can leverage to reform and improve business processes.
While descriptive analytics provides information workers with valuable insight, advanced analytics can go even further. Building on descriptive data analytics, predictive analytics examines processes at a macro level, detecting healthy or unhealthy patterns in workflow based on what has occurred in the past. From these data patterns, advanced analytics helps in forecasting normal timeframes for processes and activities.
Information workers have interactivity with live data — both structured and unstructured — which provides a comprehensive, real-time view of operations. Using structured data, advanced analytics can predict probable occurrences, and with unstructured data, advanced analytics also provides insight into causation.
An organization that is analytically savvy can plan and build processes more proactively, creating an agile environment in which processes can be adjusted and resources can be shifted to quickly respond to trends or changes in the market. Business leaders can ask complex questions about processes, create what-if scenarios and evaluate possible outcomes without affecting live processes and waiting for the results of their changes.
Tying Data to Strategy
Connecting analytics with an enterprise’s overall strategies and goals has powerful implications across multitudes of industries. Many organizations have already begun transforming their operations with newfound insight:
• Financial service institutions can analyze account activities to mitigate risk
• Government agencies can geographically pinpoint usage of benefits to determine where to launch strategic programs
• Colleges and universities can track student performance and activities to implement interventions and prevent dropout
• Manufacturers can anticipate machine failures and perform the preventative maintenance to increase production equipment uptime
Data scientists and business leaders continue to uncover more possibilities for analytics applications. Even organizations that are early in their analytics journey can reap the benefits from working toward a goal of becoming analytically savvy, including:
• Accelerated productivity as a result of identifying and investigating bottlenecks
• Improved customer experience from deeper insight into customer feedback
• Reduced response times to customer, client or employee queries due to automated processes
• An environment that fosters innovation by empowering employees to create and automate workflows
• Enhanced transparency through more accurate up-to-date reporting
• Easier compliance with secure, accessible and organized data
Achieving business maturity and leveraging advanced analytics requires planning, commitment and collaboration from all of the organization’s content users, as well as IT and business leaders. Organizations must first approach their data by digitizing paper and processes, enabling process improvement and building a solid foundation of data through an enterprise-wide adoption of ECM. If ECM is already in place, an organization must assess the maturity of its ECM system — whether it needs to digitize processes, can optimize processes based on descriptive data or is ready to embed its advanced analytics into process strategy — and achieve consistent results before building to the next step.
This method will result in a robust, scalable and impactful analytics program that will ultimately enable users to engage effectively with their organization’s data in real-time and use this information to drive business initiatives.
This article originally appeared in the May 2016 issue of Workflow.