10 Key Requirements for an Effective Enterprise Search Solution
Enterprise search increasingly poses greater challenges for organizations of all sizes. Many organizations have unique use-cases when it comes to pulling disparate internal and external data, and this data cannot be indexed in the same it is for web search.
What is more difficult is the challenge of finding the right data, and only the right data, out of the huge piles of data within the database.
While there are a host of solutions out there that are geared towards solving the enterprise search problem, it is important that an organization’s decision on which enterprise search solution to choose is based on specific, critical criteria.
Here are 10 requirements an organization must look for in an enterprise search solution:
1. AI Powered Enterprise Search
Among the many buzzwords being thrown around these days Artificial Intelligence, or AI, ranks high among them.
However, beyond the hype, AI has the power to change nearly every aspect of our lives – and our work.
One such area is enterprise search, where it is already becoming a fundamental requirement for any useful enterprise search solution. The reason AI is so necessary for enterprise search lies in the fact that data is disparate, structured differently, and is often in varying formats.
Big data is, well, big.
As such, we need an intelligent technology to parse through it and find the right content within a reasonable amount of time.
2. Queries Both Structured and Unstructured Data
One of the biggest drivers in the explosion of big data over the last few years is its incorporation of unstructured data. It’s important to highlight the major and fundamental differences between structured and unstructured data:
- Structured data is generally created by computers and its formatting is consistent
- Unstructured data, however, is usually created by humans. It is often inconsistent and relies on “imperfect” human traits including emotion, carelessness, opinions, anger, stress etc…
Most organizations need to reference both structured and unstructured data, which is why an enterprise search solution that doesn’t recognize unstructured data should be avoided.
3. Understands Natural Language
While traditional AI is generally geared towards structured data, in order to get the best results when parsing unstructured data an organization will need an enterprise search solution that marries AI with Natural Language Processing.
Natural Language Processing, when applied to enterprise search, enables an organization to use search queries that are natural in form and get actionable search responses.
Natural Language powered enterprise search does not rely on keywords, semantics, or structures. Rather, it looks at the underlying knowledge rather than documents.
4. Moderate Cost
For any director of VP that reports to a P&L head, cost will always be a factor. However, when deciding on an enterprise search solution, it can’t be the only factor.
In order to find a solution for enterprise search that has all the necessary requirements, you will want to stay away from the low cost solutions, as they generally lack the robust toolset needed.
For example, they are often keyword based.
But that does not mean the most expensive option is always the best. In the case of enterprise search solutions, the “Goldilocks Principle” wins the day.
5. Minimal Human Effort
While AI and cognitive computers are hugely hyped, their adoption within organizations is still minimal. The reason for this is they often require significant man-power to set up.
Pre-tagging data, in particular, can be incredibly time consuming and can drain much needed resources from other critical areas of the organization.
An enterprise search solution needs to be designed to understand natural language and parse unstructured data. Without it, the human effort and man-power costs will be prohibitively high.
6. Minimal Deployment Time
Some enterprise search solutions can take up to 24 months to deploy. For most decision makers, that type of time commitment is a hard pill to swallow.
However, any solution that claims to be entirely “plug and play” likely does not have the type of functionality needed.
The “sweet spot” for full deployment of an enterprise search solution should be somewhere between 4-12 weeks.
7. Applicable to all Workflows
No two organizations are the same. As such, organizations tend to have disparate workflows.
When researching the right enterprise search solution for your business, it’s important to get clarification that the solution can be overlaid to meet the needs of your unique workflow. If it cannot easily do this, the deployment time, human effort, cost and accuracy will suffer.
8. High Level of Accuracy
Organizations demand accuracy, and those demands are no less applicable when it comes to enterprise search.
Unlike in the consumer world, businesses need a high level of accuracy. As such, an enterprise search solution must provide somewhere between 95-98% accuracy. Anything below that will, over time, create bottlenecks and frustrations throughout the company. In the end, the organization will be back out looking for a new enterprise search solution to solve these issues.
9. Continuously Learning
Humans are constantly changing. As such, an enterprise search solution based on AI and natural language processing also needs to learn. Any enterprise search solution that your organization considers should get better over time – its algorithm should be learning constantly.
Data security is of tantamount importance, period.
When looking into any potential solution, including enterprise search, you must make sure it meets the security standards of your organization.
For enterprise search solutions, the key aspects to look for when assessing security are:
- Able to be hosted on a VPC or private server
- Not commingled with other data
- Once it is live, no one else (including the software provider) outside your organization has access
Looking for an enterprise search solution that meets all these criteria?
Coseer is the way for the new generation of cognitive computing companies that are building natural language search appliances for the enterprise search market. By using cognitive analytics to make a specialized version of itself that trains to each enterprise database. Built with continuously self-learning algorithms, it keeps getting better too – recursively improving search and boosting productivity.
You can request a demo of our product here.