Match and Manage your Data on Cloud

We left the last blog with two questions.

A few weeks back I wrote on IBM Bluemix Data Connect. If you missed it, then watch this video on how you can put data to work with IBM Bluemix Data Connect.

Now, Business Analysts will be able to leverage Entity Matching technology using Data Connect. The Match and Manage (BETA) operation on Data Connect identifies possible matches and relationships (in plethora of data sets, including master data and non-master data sets) to create a unified view of your data. It also provides a visualization of the relationships between entities in the unified data set.

For example, you have two sets of data : One containing customer profile information and the other containing a list of prospects. A Business Analyst can now use intuitive UI to do the Match and Manage operation to match these two data sets and provide insights to questions such as:

  •  Are there duplicates in the prospect list?
  • How many of the prospects are already existing customers?
  • Are there non-obvious relationships among prospects and customers that can be explored?
  • Are there other sources of information within that could provide better insights if brought together?

The two data set are matched using Cognitive capabilities which allows the MDM– matching technology to be auto-configured and tuned to intelligently match across different data sets:

dataconnect

Business Analyst can understand the de-duplicated datasets by navigating through a relationship graph of the data to understand how the entities are related across the entire dataset. Now they can discover new non-obvious relationships within the data that were previously undiscoverable. The following generated canvas enables them to interactively explore relationships between entities.

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In the above example it was illustrated as how clients can now easily understand the data they hold within their MDM repositories and how now they can match their MDM data with other data sources not included within the MDM system. This simplifies the Analytical MDM experience where MDM technologies are accessible to everyone without the need to wait for Data Engineers to transform the data into a format that can be matched and rely on MDM Ninja’s to configure matching algorithms.

Summary:

IBM Bluemix Data Connect provides a seamless integrated self-service experience for data preparation. With addition of entity analytics capability, business users are empowered to gain insight from data that wasn’t previously available to them. Now organizations can extract further value from their MDM data by ensuring it is used across the organization to provide accurate analytics. Entity analytics within Data Connect is now available in beta. Go ahead and experience the next evolution of MDM.

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3 Compelling Use cases for Entity Analytics

Entity analytics is used to detect non-obvious relationships, resolve entities, and find threats and vulnerabilities that are hiding in your disparate collections of data. Through the medium of three use cases, let’s try to understand how Entity Analytics can help organizations enhance their customer experience.

entityanalytics1Scenario 1

Entity Analytics can detect non-obvious relationships between entities. It can also analyze new data sources in context leading to new insights and opportunities. In this scenario you have some data in an MDM system and another set of data in a spreadsheet file. Suppose you want to run a marketing campaign to target high-net-worth clients to sell them a premium bank account. The information in the one MDM system in isolation doesn’t give you the needed information. You want to bring these two sources together and determine if you can identify individuals that can be targeted for the new account.

In the MDM system, John Smith lives with Mary Smith. The spreadsheet file shows that John Smyth (spelled differently) is actually a high-net-worth client. Combining this information we can say that John Smith is actually the same person across the data sets. He’s a high-net-worth client, and he has a wife. With this information you want to target Mary Smith with a premium bank account because she lives with a high-net-worth individual. Entity analytics enables you to discover and understand this opportunity.

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Scenario 2

Entity Analytics can find where threats and vulnerabilities are hiding in big data and respond efficiently. In this scenario for a risk assessor in an insurance firm, severe rainfall is predicted within a geographical area that includes the client’s residential location. When pulling up the client data from MDM and the flood warnings being issued from the environmental agency, we can match across the data sets to identify that a number of properties are at risk. So, the client can then be provided an early warning to help mitigate risk and increase the flood risk value on the client’s property renewal. Also, if you have an elderly customer that is at severe risk; you can take action to notify the emergency service to ensure a proactive resolution to any potential threat.

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Scenario 3:

Lets see how using Entity Analytics, MoneyGram International Inc.,  a money transfer company gets notified of questionable activities in real time for faster predictive and preventive decision making. This helped them to save $200 million in just two years!

Summary

Entity analytics help organizations by launching more target-oriented campaigns and by reducing the risk of fraud. With the help of entity analytics, organizations can predict and preempt suspicious activity faster and with reduced costs. Entity analytics further help by allowing enterprises to detect the entities that are the same, regardless of whether the entities are hidden or masked. So following questions can be raised:

  • Does this Analytics require an MDM Ninja or can something be set up easily by a Business user?
  • Do we have Entity Analytics available on Cloud for decisions that cannot waaaaiiiiittttt?

Stay tuned for my next blog.