You’ve probably heard the saying “change is the only constant in the world.” But when it comes to enterprises, there’s another constant along with change: data generation.
Enterprises generate a massive amount of data every day, and most of this data is valuable. As such, it’s important to store this data for further processing. And beyond the challenge of simply storing bulk data, enterprises face the challenge of storing that data in such a way that it can be retrieved in a timely manner. This created the need for a smart and fast way to fetch data. In this post, we’ll take a look at one such practice that makes it easy to find data within your organization: enterprise search.
What Is Enterprise Search?
Enterprise search is an information retrieval practice that makes it possible for employees to search for structured and unstructured data within the organization. Enterprise search enables you to search for data that can be stored in different kinds of sources—databases, email servers, network drives, etc.—in a single place. For example, you might set up a search environment or application to centrally store data from these various sources. Due to this, you can just use a single query to find what you need. There’s no need to run different queries for different types of data or for different sources.
To better understand enterprise search, let’s take a simple example of a Google search. When you want to find some information, you go to the search engine and search for what you’re looking for. When you do this, Google searches its data storage and retrieves data that’s relevant to your query. Then it presents that data to you. All of this happens within a fraction of a second.
Enterprise search is very similar to this, but the difference is that in enterprise search, the searcher is the employee of the enterprise, and the data they’re looking for is limited to the enterprise. The main aim of enterprise search is efficient and highly precise information retrieval.
Why Do You Need Enterprise Search?
Let’s say you’ve been collecting logs from your organization’s devices every day for almost a decade. Now, your manager puts you in charge of analyzing these logs to see if the network architecture can be improved. Going through these logs manually is out of the question—doing so would take decades, and you’d be frustrated within moments of starting. So, instead of looking at all the data, you want to analyze only the parts of data that would help you in improving the architecture. But what could guarantee that you are able to quickly gain access to the data you need? That’s where enterprise search comes in.
For every data search, efficiency and accuracy is really important. Without these two factors, data is less useful. In this example, rather than spending countless hours manually reviewing logs, an enterprise search system would help you home in on the data you need to make a quick, effective decision. Now, let’s try to understand more broadly how the accuracy and efficiency of enterprise search benefits organizations.
Decision-Making
To make the right decision, you need to consider all the facts. And when you can easily retrieve the data you need, it becomes very easy for you to make decisions.
With a well-designed enterprise search system, you can run a single query to get the data, and you get the results quickly. As a result, you aren’t wasting any time unnecessarily.
You might come across a lot of situations where you have to decide something in a couple of minutes. There’s no time for you to go through documents and filing cabinets. Enterprise search helps you not only in making quick decisions but also in making better decisions.
Productivity
Nobody likes wasting their time on unproductive tasks. If you ask employees to search for a single piece of data by going through hundreds of documents manually, they’ll just get frustrated and their morale will go down. And because such tasks would take up a lot of time, you’d be paying them to do something that’s not very beneficial to the company.
By implementing enterprise search, you make it easy for your employees to research and gain knowledge. When they are able to easily get the information they need, they can spend more time doing valuable tasks and will be more productive.
Response Time
Faster retrieval of data results in a faster response. This is especially important in time-critical situations such as cybersecurity. Most security teams store data related to malicious software, IPs, or intrusions. Let’s say you’re a cybersecurity analyst and your job is to prevent security breaches. If you see something fishy, you need to retrieve data quickly to know more about the anomaly and what action to take. Delays in such cases could result in great damage. Response time also matters in customer service. You have to be able to get the right data, and quickly, to respond in time.
Better Use of Data
Enterprises store a lot of data because they realize the potential of how data can be beneficial for them. Collecting, storing, and managing data requires efforts and resources. So, when all that is done, not using the data is just sad. Enterprise search encourages you to make better use of data and maximize its benefits for the enterprise.
How Enterprise Search Works
Enterprises collect data from different sources. Some of the common sources are log files and UI applications. The format of the data collected differs based on its source. For example, data collected using a form is easy to put into a tabular format. But raw log files are just lines of information.
Enterprise search systems are used to handle data irrespective of its format. And due to a lot of varying formats of data, designing an enterprise search system is subject to each use case. That’s why there’s no fixed way of doing it. But we can divide enterprise search into three different phases:
- Content awareness and processing
- Indexing
- Querying
Content Awareness and Processing
The first step in enterprise search is to collect data from different sources. This is the phase where you connect all the data sources and set up permissions to enable searches. Then, data is collected using APIs or commands. Data coming from different sources can be in different formats. To avoid switching between the way you search for different formats, the incoming data is converted into a common format. This common format can be plain text, JSON, or any other format based on your use case.
Indexing
Enterprise search is similar to the index of a book. It tells you where you can find the data you’re looking for. Once the data is processed, indexing is done so there’s a lookup table or metadata, which speeds up the process of finding the required data. This lookup information has information on what data is present and where you can find it. Indexing happens every time data comes in, not when an employee searches for data. Because of this, you save a lot of time when you search for data.
Querying
This is the phase where you reap the benefits of enterprise search. This is when you ask the enterprise search system for data. Your request can have keywords and filters, which the enterprise search system will use to get the results. When a query is made, the enterprise search system will go to the data storage based on the indexing information and match the existing data with your query. It will then return the results to you.
You can consider the first two phases as the foundation of enterprise search and the third phase as seeing the results of earlier phases.
Making Information Retrieval Easy
Earth has a lot of gold and other valuable elements in its core. But these elements get you money only when they are mined. Similarly, only having data is of no use if you don’t extract and make use of it. To get information retrieval right, all three phases we discussed above are important.
In this post, we saw in brief how enterprise search is beneficial for business. You can also use the event data cloud to help you organize and optimize data for various benefits. And to help you manage your data, try Scalyr, a one-stop solution for log management and observability.
This post was written by Omkar Hiremath. Omkar uses his BE in computer science to share theoretical and demo-based learning on various areas of technology, like ethical hacking, Python, blockchain, and Hadoop.
