No matter what search technology an organization uses, replacing it can disrupt normal operations. Even if employees are dissatisfied—complaining every other day to IT about how slow it is or user unfriendly—the old system is familiar.
In 2015, technology consultant Tim Powell blogged that in the early 2000s many organizations were very disappointed in their knowledge management (KM) efforts—some of which were multimillion dollar undertakings. The main complaints centered on integrating KM into organizational workflows and KM’s failure to produce a substantial ROI.
If you’re a millennial, you don’t remember the bad old days of enterprise search. It was high on the scale of frustration. You often couldn’t find what you were looking for. And, if you did, it probably took a long time. But then, thankfully, Google happened.
Users want relevant results from their search queries. But, in addition, they want their search tool to “understand” what their queries mean based on context. In other words, know the difference between what was expressed in the query and what was intended.
Remember the frustrations of key word search? If you need a refresher, just check out If Google Was a Guy. It’s funny—in a way that makes you kind of squirm in your seat. Of course, in the early days of search engines, power searchers used Boolean query operators. We have George Boole, 18th-century English mathematician, to thank for that.
Search engines index millions of pieces of information, structured and unstructured. But simply indexing information isn’t enough to give a user the results they need when they perform a search.
The Need for Relevancy
The goal of relevancy tuning is to help a user get the best results for a given query they are trying to run. Relevance is telling the search engine how to best sort the information in its index to ensure search results match search queries as closely as possible. It’s the process of bringing the most relevant information to the top of the result list.
Good question. What is the point? The point is to create measurable business value from enterprise data. Of course, before measurable business value comes insight. The Modern Data Architecture (MDA) recognizes that insight can lie hidden in data of all types—structured or unstructured, messy or modeled, historical or realtime.