Google has announced that it’s sunsetting the Google Search Appliance. Are you using it? Microsoft is sunsetting FAST. Are you using it?
Here’s the thing. The search market is changing, and the vendors know it. That’s why you are seeing major changes in the traditional search market. More importantly, though, you are noticing that traditional search simply isn’t giving you the information you need.
Traditional search was good, it indexed content and allowed you to perform quick searches. But it’s not that straightforward today. The amount of information you collect and create in your company is growing. It’s stored in multiple repositories and business systems. Some of it is secure and can only be seen by certain people.
Benedict Cumberbatch, star of the BBC series “Sherlock,” has a problem. Sherlock’s stream-of-consciousness deductive speeches must be delivered at warp speed— “100 miles an hour”—and that’s hard to pull off without mistakes.
But, of course, all that speed makes sense. Holmes observed, processed, and bang! Insight. That’s rapid time to value.
Time to Value: The Missing Ingredient
Organizations deploy cognitive search platforms to boost employee productivity, foster innovation, and gain greater insight from their data. But to achieve those goals, they often take on huge professional services from “mega vendors” that don’t deliver an effective cognitive solution.
Transforms sensory/data inputs into understandable formats
Reduces massive amounts of data to a concise, usable summary
Elaborates, extends, and enrichs the information processed
Stores and recovers information efficiently
Uses information to solve a meaningful problem
Of course, most of us don’t think about this when we use a cognitive search engine, but, “behind the veil,” natural language processing, machine learning, and text analytics function to execute those five tasks as quickly as possible.
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.
Of course, sometimes a search solution must be replaced because the vendor stops supporting it. A good example would be HP selling off its intelligent data operating layer (IDOL) product, a.k.a. Autonomy, to Micro Focus. Who knows what they’ll do with it?
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.
Googol—1 Followed by 100 Zeroes
Larry Page and Sergey Brin launched Google in 1997. Hey, Google will be 20 in September. It’s no longer a surly teenager! Speaking of, where did they come up with the name Google anyway?
When you think search, you usually think of asking a question and getting an answer. This is usually correct, but sometimes you need more than that.
Consider search in a more specific context:
Finding the expert in a large company with many locations and remote workers,
Gathering information on a prospective customer from across multiple sources,
For a pharmaceutical company, you may need to research drugs with similar chemical structures.
You need a search engine that supports your exploration process, whatever that happens to be. You need a search engine that knows your data, can recommend results, and can help you find what you’re looking for.
Some days, it seems that new machine learningapplications are popping up everywhere you look in the news. In this author’s opinion, the search market seems to have anointed machine learning as the new hotness. This is a fascinating realization, because anyone who has spent more than a couple years deploying search in the enterprise knows that machine learning has been used and applied in exciting and unique ways for years and years.
In my experience, clients tend to conflate what machine learning means when it comes to Enterprise Search. Of course, it’s not their fault – machine learning is everywhere you look! But when Attivio says machine learning, we mean two things:
Attivio, a cognitive search platform provider, is pleased to have been recognized recently by Gartner as a “Visionary” in their inaugural report on “Insight Engines.” The report offers this definition for what an Insight Engine actually does:
"Insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments."
One of the foundational technology differentiators of the Attivio Platform is the ability to perform Query Time Joins of data across both structured and unstructured data. Last year we received our latest patent on an extension of that technology called a “Composite Join” and it has enabled us to deliver some awesome solutions for our customers.
The Query Time Join
Before we get into composite join, let’s take a step back. The concept of a join between two tables is well understood in the realm of databases. For example:
Policies are rarely something that get people excited, but when it comes to the enterprise, they are the foundation for every risk and compliance solution. More importantly, regulators around the world and in every industry, rely on, and in many cases, require corporations to maintain and enforce policies. Policies are what keep your data private, ensure a fair playing field, and generally keep the world a safe place.