Don't Let Bad Enterprise Search Drag Down Employee Experience
Employees waste hours searching for data siloed across various departments, leaving them feeling frustrated, unengaged and unproductive.
The ability to locate and retrieve information is a straightforward process when all you have to do is look in one or two places. But when it is spread out across the enterprise, it becomes a challenge to locate, let alone effectively evaluate and use.
Here are some ways that intelligent enterprise search can enhance and improve the employee experience.
Focus on Findability
Employees are experiencing high levels of difficulty locating information, according to a 2019 IntraTeam benchmark survey, and 40% were either dissatisfied or very dissatisfied with their current search application. The inability to locate needed information leads to frustration, a drop in productivity, wasted time and a lower level of job satisfaction.
According to digital literacy expert Elizabeth Marsh’s Digital Workplace Skills Framework report, 21% of employee productivity is lost through the process of locating and managing information, and managers themselves spend two hours per day searching for information. Despite improving technology, enterprise search and navigation tools are often seen as the enemy of productivity in the digital workplace, Marsh reported, because there is a critical skills gap in accessing people and resources, evaluating information that has been retrieved, and assimilating and applying it.
As Chris Tubb, digital workplace and employee experience consultant at SparkTrajectory, said in a discussion about enterprise search: “Users are not good at searching and neither are they trained to do so. Google does not train them to do so because they are usually searching in non-domain topics and are easily satisfied."
The widespread use of Google search has defined expectations and employees have come to expect the same search experience at work. Unfortunately, that is an unreasonable expectation. Google web search uses programmatic analysis to facilitate a query, while enterprise search uses more personal analysis because workers are searching for something specific to the tasks they need to perform. In most cases, enterprise search is not effective and findability is more vital than search, Tubb said.
“My current feeling towards enterprise search is that most organizations are not able to implement it in an economically viable way as employees aren't able to describe their contributed content to the extent to make it findable,” he said. “The majority of larger organizations should focus on findability (rather than just search) of important non-domain (such as HR, IT support, employee processes) information and tools and leave personal and group domain needs to the default search tools provided by Microsoft, ensuring that group information basically becomes restricted.”
Related Article: Why Is Enterprise Search So Difficult?
Train Employees to Effectively Evaluate Data
Once an enterprise search solution is in place and findability has been addressed, employees need to be able to effectively evaluate the data that is retrieved. According to Marsh's research, the key employee skills to focus on are ability to evaluate information in terms of validity, usefulness, relevance and timeliness.
The ability to search through mountains of data is a process that begins with the need to acquire specific data, perform a search, and effectively retrieve said data. At that point, it’s up to employees to evaluate the data and apply it to the task at hand.
That’s where the problem lies because quite often the data retrieved are prolific and unorganized. It's at this point that AI and machine learning are changing the game for enterprise search.
Related Article: Reading Between the Lines of Enterprise Search
AI-based Enterprise Search Provides Real Answers
Enterprise data comes from many sources, including databases, email, social media and web analytics services. Much of it is largely unstructured and therefore enterprise search platforms must be able to analyze unstructured data and make it available to be searched. The reason many enterprise search solutions tend to fail is that they are keyword-based and return overwhelming lists of documents rather than providing an answer to a question.
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New enterprise search platforms use AI and machine learning to drive natural language search, allowing employees to search the same way they do on Google, by asking specific questions rather than using keywords or phrases. Amazon Web Service’s Kendra, released last year, uses machine learning to search data that exists in S3, SharePoint, Salesforce, ServiceNow, RDS databases and One Drive, among others, and returns an answer to an employee question rather than point to documents.
AWS Kendra is industry-specific and is able to understand language specific to an industry, with the goal of providing search results more like what users typically expect from a web search engine. For example, an HR employee might ask "what is the deadline for filing HSA form" and Kendra would also automatically search for "what is the deadline for filing health savings account form" to obtain the most accurate response.
Similarly, IBM’s Watson Discovery uses AI, machine learning and natural language processing to create industry-specific enterprise search functionality. Coveo’s Workplace is yet another enterprise search platform that uses AI and machine learning to provide relevant, industry-specific responses to employee queries, and integrates with enterprise software platforms, including Salesforce Sales Cloud, Salesforce Service Cloud, ServiceNow ITSM & HRSM, Sitecore and others.
Related Article: When Personalized Enterprise Search Results Are Hidden in a Black Box
Effective Enterprise Search Boosts Productivity and Engagement
An intelligent enterprise search such as AWS Kendra enhances employee productivity and engagement by unifying content platforms and providing custom data source connectors. This allows the search platform to return relevant, actionable and contextual results to queries, enabling employees to make more informed decisions. This, in turn, decreases frustration, improves job satisfaction, enhances engagement and increases productivity.
Enabling employees to effectively search for data through a centralized source provides several benefits to both employees and companies, said Irina Soriano, head of enablement at Seismic, a sales support platform provider. In today’s often remote workplace, the distractions of over-communication are a particular concern.
“Overall employee productivity and new hire ramp can be significantly improved by decreasing the time to locate content across multiple data sources,” she said. “An effective enterprise search tool presenting critical information in a structured way will also support in limiting direct employee outreach through email and other channels."
An effective enterprise search platform also improves the level of job satisfaction for employees because it allows them to do their jobs more effectively. “Analytics and user access data enables companies to ensure the right information finds employees at the right point in time, helping them to feel equipped to perform their roles successfully," Soriano said.
Enterprise search platforms have continued to evolve and many now include AI and machine learning to return relevant results that answer specific questions, rather than just returning lists of documents. When employees are trained to use enterprise search and evaluate the results returned, enterprise search becomes an effective tool that increases productivity, employee engagement, job satisfaction and improves the overall employee experience.
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