man sitting around with papers flying around him
PHOTO: Dmitry Ratushny

Bad management will impact productivity in one way or another, and it can come in a number of forms — poor management of human resources, poor financial management and poor customer management. Poor information management, however, makes work harder for everyone and affects the entire enterprise. Recent research from M-Files has quantified just how bad it can get.

Information Management Impacts Productivity

The M-Files 2019 Global Intelligent Information Management Benchmark Report (registration required) surveyed 1,500 office workers across multiple global regions to establish an organizational benchmark on how business information is organized and accessed. Among the top challenges is document management.

Globally, the findings of the research showed that:

  • 82 percent of respondents stated navigating different systems and locations to find the correct version of a file they are looking for negatively affects their productivity.
  • 91 percent of respondents reported that their job would be easier if they could quickly and easily access the most current version of a document.

Other less widespread problems included:

  • 42 percent cited challenges with improper or incorrect labeling of documents.
  • 41 percent reported that information was frequently stored in the incorrect folder or system — with 29 percent asserting information was misplaced or lost.
  • 26 percent cited challenges determining which system or repository to search, while 26 percent were unsure whether they found the current version of a document.

Related Article: Content Services Threaten to Repeat the Mistakes of Our ECM Past

Many Enterprise Technologies Are Outdated

There is clearly a problem, research shows that many businesses manually store information using outdated hierarchical folder structures across a variety of disparate and often unintegrated systems. Ajeet Dhaliwal, lead developer and founder at Tesults, pointed out that the problem goes much deeper than issues about finding business documents or other content.

Poor information management has been a major problem for software development teams in startups, small and midsized businesses and enterprises for years. Productivity suffers when information around activities involved in production are not managed efficiently, in particular for requirements gathering, design documentation, source-code management, code reviews, bug tracking, testing, release and general project management.

The situation can be improved by using a range of tools that help tackle specific areas, such as Slack for general communication, Asana and Trello for handling sprints, GitHub and Bitbucket for SCM, JIRA for bug tracking, but many gaps remain.

The existing tooling has to always had a problem catching up to the increasing complexity in systems being designed and the growing amount of data being generated and collected. "I discovered that despite considerable investment in automation for conducting the testing itself, there was lack of information management when it came to handling the huge amount of test results data being generated when teams have continuous integration, delivery and testing being undertaken with automated build and test systems," Dhaliwal said.

In response, he designed and created custom solutions across three teams and two companies to manage. He found that the return on investment of automation was multiplied by actually being able to understand what the data was showing us with solid analysis. The right decisions could be made faster, and the team could understand where problems were introduced, what needed to be fixed first, and we were able to improve quality and release faster.

Related Article: Intelligent Information Management: What's in a Name?

Technology Must Be Accessible

Managing information technologies is key to productivity, according to Matt Matsui, chief product officer at Calabrio. With the influx of information available at employees’ fingertips, you need to tap into “behind the scene technology” to unlock full human potential. By blending both artificial intelligence (AI) and business intelligence (BI), companies enable their employees to be more productive as well as proactive.

Companies who blend both AI and BI unleash customer service agents to be productive and do their best job because they are utilizing the real-time insights and data about the customer that they have from multiple channels.

Organizations Are Resisting Change

This is a problem that effects everyone. Grant van der Harst, managing director at Anglo Liners, pointed out that all organizations are structured by information and knowledge, and employees being able to use this is what drives the business forward.

Poor information management has many negative effects within a company and could be affecting other parts of the business without leaders even realizing it. If, for example, it is difficult for an employee to access information quickly, time gets wasted unnecessarily and serving the customer takes longer, which leads to bad online reviews. After time this becomes stressful for employees and employee retention suffers. Many employees are aware of how inefficient information management is within their company and may even have great solutions, but for whatever reason this isn’t communicated to management, or it’s ignored.

“Organizations need to address their resistance to change and critically analyze how their organization functions. Although this may take up time and resources in the short term, it can only have positive outcomes for the future,” he said. “As technology advances and competition becomes even fiercer, unproductive organizations with ineffective information management will fall behind the rest.”

Related Article: Change the Way You Look at Content Management: Muhi S. Majzoub of OpenText

Data Capture Is Key

As technology develops the number of solutions to the problem grows. There have been significant advancements in machine learning technologies, commonly referred to as robotic process automation, said Andrew Pery, a marketing executive at ABBYY.

Even still, only 20 percent of organizations surveyed employ effective metadata and classification of their data. With the proliferation of incoming information from multiple channels, it is imperative for organizations to invest in capabilities to capture information at the source, digitize it as soon as it enters the organization and transform that information into actionable business processes.

Document understanding of incoming information based on content and metadata is key to automating the document capture and classification process. That way, organizations can reduce error-prone and labor-intensive tasks associated with the capture, extraction and classification of large volumes of information and accelerate the process of compliance with data classification, retention and compliance policies and regulations.

“If your system doesn’t have a highly accurate, scalable data processing and document capture solution that intelligently extracts, classifies and serves critical data from incoming image, email and document streams, it’s time to find a new system,” he said. Yet even the most accurate systems are never 100 percent accurate. Thus, once captured, business-critical data must be automatically validated for accuracy and compliance.

Finally, for fundamental capabilities, any solution introduced into the enterprise, must integrate into corporate information systems such as CLM, ECM, ERP and EDMS — having to export or migrate data from a siloed system simply makes no sense in this day and age. Pery pointed out that a typical Fortune 1000 company manages anywhere between 20,000 to 40,000 active contracts at any given time, 10 percent of which are misplaced, difficult to find, still in paper form or on a file share somewhere, or buried in an email attachment.

Organizations continue to struggle to do something that, in the abstract, seems simple — connect business documents to business value, to mission-critical processes that drive revenue or mitigate risk.