You may have come across the term “analytics” without attaching much thought to it. For instance, to get into the college of your choice, you must have analyzed some number of colleges. This is because you didn’t want to be passive about your choice of college. However, analytics doesn’t end with college choices; it’s present in our everyday lives.
For example, firms rely on analytics to be able to make the right decisions. With analytics, they can store and analyze data and then use that data to find patterns. Analytics goes beyond analysis—it tries to predict the future with analyzed data patterns. You can use analytics for marketing, security, software development, and so on. In this article, we’ll explore what operational analytics is in depth.
What Operational Analytics Really Means
First, let’s understand what analytics really is. Analytics simply has to do with analyzing data or statistics. What this means is that you can tell what will happen next with analytics. There’s room to improve business progress with analytics—this comes into play by studying previous data. IT firms need to monitor and measure all their operations so they can find events that will—or won’t—lead to the success of their products.
Operational analytics is the observation and study of collected data to improve the overall operation of a business. DevOps teams need to understand how codes behave when they execute them. For example, teams will record logs to study and compare them over time. Code logs tell what’s really going on under the hood when you run codes, so developers can tell which code broke the program and propose a means to repair it. Also, developers can log users’ interaction on a software. This will allow IT firms to predict when there is low traffic or high traffic to their site and the reason why.
Not only will developers be able to measure user experience, they’ll be able to measure their software’s overall health. Everything from the database to the response time can be measured with proper operational analytics. With that said, let’s dive into why we need operational analytics in applications.
Why Operational Analytics Is Necessary
So, we’ve previously explored what operational analytics means. Now let’s look at why IT firms need to consider operational analytics as a priority. Of what use is operational analytics? Why should developers take it seriously?
User Experience
Have you ever wondered what would happen if IT firms can’t predict how users will interact with their software? For instance, how would IT firms predict which features users love most? With proper analytics, firms can predict which feature users love the most from user activity. This would give users the best experience, because whenever there’s a decline in product usage, IT firms can determine the cause and plan a fix.
Firms can tell which feature slows down an app’s response time. This data can be used to improve the product to provide better user experience.
Application Monitoring
Application monitoring is never possible without operational analytics. This works by aggregating and comparing real-time data from an application’s performance. This way, IT firms can tell how well their applications are running. Developers can also tell how their application performance is improving and how users engage with their application.
With application monitoring, you can make sure your software or application works as expected. For instance, DevOps teams can state that the response time of the software they created will be 0.1 seconds. Application monitoring will help firms keep track of the expected response time. Only analytics can tell if the response time recorded doesn’t correspond with what the DevOps team promised. In this case, logs can be studied to know the exact reason for the delay in response time. All this is possible only with proper operational analytics.
Database Monitoring
What is an application without a database? There are certainly no applications without a database system. Managing an application’s database system properly is a priority all firms must consider. No firm will want to risk data loss or expose data from users or from their own organization to cybercriminals. How do you hope to deliver jaw-dropping applications to users with an unstable database?
For example, how do you predict the memory space your software can consume next month with the space currently consumed? Database monitoring is the solution to such questions. With database monitoring, firms can study and compare the memory space an application consumed in previous times. This can in turn be used to predict how much space can be used in the future in relation to application growth. A healthy database means that your application won’t crash on you without notice. It’s also possible to get logs about sensitive information like keys and passwords that isn’t protected. This reminder will help organizations protect sensitive data from cybercrime.
Application Updates
How do real-life updates in applications sound? For instance, Amazon can update the price of its products regularly throughout the day. This will allow customers to buy a product at an updated price as it’s sold everywhere around the globe. Updates like this are almost impossible without operational analytics.
First, Amazon will need to compare the price of products in its application with what the general market offers. After that, product prices will be updated according to the market trend. Proper operational analytics can be used for automated updates. What this means is that after comparing results, an action can be taken without human interference. In Amazon’s case, once a product’s price changes in general market, Amazon’s website can quickly reflect the price change without human intervention.
Tips For Efficient Operational Analytics
Operational analytics isn’t child’s play. Most times, an application’s future depends on operational analytics—how firms manage data regarding their applications. It’s important that firms carry out operational analytics efficiently for better company growth. Let’s look at some quick tips that will help us implement operational analytics for the best results.
Proper Logging
Documenting a software’s operations is usually underrated. But without logs, how can we understand what happens when each event is triggered in our application? The first step to proper logging is the ability to aggregate and analyze logs. With log management tools like Scalyr, you can get live log metrics on your dashboard. All you need is to sign up and integrate this tool into your application. Not only will you get proper logging reports, but Scalyr also allows teams to collaborate and work together.
Automated Analysis
Analysis is a subset of analytics. In fact, there’s no data analytics that hasn’t undergone analysis. A very simple example to explain this is collecting and studying data on user traffic. This is analysis—a firm collects trends on user traffic over time and begins to study how the data relate.
What happens next after firms have established a relationship between the data they have collected over time? Analytics—trying to predict future trends with already analyzed data. So, bad analysis will definitely lead to bad analytics. One way firms can ensure that analyses are up-to-date and correct is by automating them. This way, results can be free from lags and human errors.
Appropriate Data Analytics
Results need to be analyzed in the best possible means for firms to take their next actions properly. For example, you won’t want any bad presentation of an analysis; this makes it difficult to examine analysis. Searching logs effortlessly is a game changer in data analytics. This way, teams can search through logs and collaborate independently, without needing to stop work because a team member is absent. Getting embedded metrics, real-time alerts, and live tail are benefits that efficient analytics promises.
The Best Part
This article has explored what operational analytics really is, why it’s necessary, and some tips for efficient operational analytics. We’ve also looked into some tools that improve operational analytics. However, this isn’t all that operational analytics offers. Better performance with reduced cost is why most firms implement operational analytics. Tools like Scalyr Elasticsearch provide the best experience for developers to retain and manage data at cheaper rates. How about delivering the best scale to performance and cost ratio with architecture? If you want to know more about how Elasticsearch can affect operational analytics positively, this article is a great fit for you. Also, you can look more into how log aggregation can make or break operational analytics.
