事例:コインチェック(仮想通貨交換所)〜 ログのモニタリング体制を最短で整備

さらに詳しく
video に戻る

2014年12月19日

Transaction Analytics Overview Video

Uncover Transactional Context for Deeper Business Insights

SECTION 1. Introduction to Transaction Analytics

This advanced component is built to help organizations understand the interconnected events within transactions across distributed environments. Deeper insights enable you to make informed business decisions and address critical issues.

SECTION 2. Value of Transaction Analytics

By leveraging Sumo Logic Transaction Analytics, your organization can:

a) Reduce Mean Time to Identification

Expedite root cause analysis through analyzing events within transactions across distributed environments including 3rd party environments. Faster root cause analysis translates to a considerable reduction in Mean Time to Identification.

b) Automate transactional data analysis

Gain deeper insight into transactions through streamlined, automated analysis.

c) Identify core user interaction

Drive informed business decisions through the identification of core interaction between users and systems.

d) Visualize complex interconnected events in real-time

And understand how elements in transactions are interconnected through advanced transaction flow diagrams.

SECTION 3. E-commerce Use Case Demo

Lets demonstrate a popular use case that reveals the flow of user interaction on a fictitious ecommerce website named ecommark. This example will highlight the stages involved in the purchase of a product and the process leading up to a shipment confirmation.

a) Step 1: First, a customer would create and submit a transaction query:

The results displayed include the count of occurrences of each element within the transaction.

b) Step 2: Second, all it takes is a click of the flow diagram icon to launch a view of the interconnected elements within the transaction.

c) Step 3: When monitoring all the transactions in a given day, if you were to notice a sudden decline in the number of orders shipped, you would be able to identify where the issue might have originated within various states of the entire transaction. In this example, you can clearly see a drop-off in the billingVerification process implying that the billing Server may be unavailable. This will also help you efficiently delegate the issue to the appropriate team for remediation, which can help you minimize the negative impact to customer satisfaction and revenue.

SECTION 4. Additional Uses Cases

Transaction analytics can be applied to a wide variety of use cases, here are some other examples of how our customers can use this capability:

1) Take a look at this familiar type of transaction. If you look closely, you’ll see that this flow diagram consists of some of the typical elements that you would see in a site registration process.

By observing this diagram, you may conclude the following:

a) the most popular referral traffic comes directly from the users b) referral campaign 2 doesn’t appear as successful as campaign 1 so you might consider pulling it. c) and even more importantly, sign-ups tend to drop off during the EnterPreferenceAction state so perhaps there might be a bug or problem preventing the completion of site registrations.

2) In this next example, we can see a simple distributed system with two internal components such as QueryProcessor and Dashboard.

The data for the two components are replicated in three databases and requests are load-balanced across the three databases.

a) On display here is a representation of normal latency: b) Now, here is a representation of potential deviations in latency that may prompt further investigation to identify the source elements contributing to the latency between 3am and 5am.

SECTION 5. Availability Details

As you can see, Transaction Analytics has a broad applicability and can really help you make better informed business decisions.

部門

スポットライト