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:

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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.

24th Year of Patent Leadership

IBM broke the U.S. patent record with 8,088 patents granted to its inventors in 2016, marking the 24th consecutive year of innovation leadership. IBM passed the milestone as the first organization to deliver more than 8,000 U.S. patents in a year. When you do the math, that’s more than 22 patents granted to IBM inventors per day in 2016. IBM’s 2016 patents output covers a diverse range of inventions in artificial intelligence and cognitive computing, cognitive health, cloud, cybersecurity , IoT and other strategic growth areas for the company.

INNOVATION has been a focus at IBM since day one, and it is at the core of IBM’s values. IBM’s patent leadership is key in demonstrating it’s strategic commitment to the fundamental R&D necessary to drive progress in business and society, and an important barometer of innovation. Inventions are a great source of value to IBM, to clients, to business partners and society as a whole.

The Top Ten list of 2016 U.S. patent recipients* includes:

  1. IBM – 8,088
  2. Samsung Electronics – 5,518
  3. Canon – 3,665
  4. Qualcomm – 2,897
  5. Google – 2,835
  6. Intel – 2,784
  7. LG Electronics – 2,428
  8. Microsoft – 2,398
  9. Taiwan Semiconductor Manufacturing Co. – 2,288
  10. Sony – 2,181

*Data provided by IFI CLAIMS Patent Services

In the area of cognitive computing and artificial intelligence, IBM inventors patented more than 1,100 inventions that help machines learn, reason, and efficiently process diverse data types while interacting with people in natural and familiar ways. Here is sample of some of the Patents filed in 2016:

  • Machine learning to secure the best answers: Providing accurate answers to questions that are posed by users. (US Patent #9,384,450)
  • Planning the best route for a traveler’s cognitive state: IBM inventors have developed a method for planning a trip route based on the state of travelers that affects driving risk the most: their state-of-mind. Had a long day or easily overwhelmed? This system will help you navigate a less stressful route home. (US Patent #9,384,661)
  • Using images to better gauge heart health: IBM researchers have developed a method for categorizing human heart disease states by using cardiac images to characterize the shape and motion of the heart.  (US Patent #9,311,703)
  • Using drones to clean microbes in hospitals and agricultural fields: In this patent, surveying, testing and measuring contamination is controlled by a cognitive facility that manages drones. The drones could enter a contaminated area, collect specimens then confirm and map and sterilize contamination.  (US Patent #9,447,448)
  • Measurement and Integrated Reporting of Public Cloud Usage in a Hybrid Cloud Environment:  This innovation enables enterprises to monitor and measure employee and application usage and reduce information technology costs. (US Patent #9,336,061)
  • Pre-emptively detecting and isolating cloud application network intrusions:  When network breaches are detected, networking between applications – or their subcomponents – can be locked down to minimize the impact of an attack. (US Patent #9,361,455)
  • Managing incoming communications to prevent phishing and the spread of malicious content: IBMers invented a system to create levels of permission and trust for inbound communications such as e-mails and text messages. This system determines a level of trustworthiness to assign to an inbound communication, and how much of that communication to forward on to a user. (US Patent #9,460,269)

 

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.

 

The 4 Personas for Data Analytics

Due to new modernization strategies, data analytics is architected from  top down or through the lens of the consumers of the data. In this blog, I will describe the four roles that are integral to the data lifecycle. These are the personas who interact with data while uncovering and deploying insights as they explore this organizational data.

Citizen analysts/knowledge workers

A knowledge worker is primarily a subject-matter expert (SME) in a specific area of business—for example, a business analyst focused on risk or fraud, a marketing analyst aiming to build out new offers or someone who works to drive efficiencies into the supply chain. These users do not know where or how data is stored, or how to build an ETL flow or a machine learning algorithm. They simply want to access information on demand, driving analysis from their base of expertise, and create visualizations. They are the users of offerings like the Watson Analytics.

Data scientists

Data scientists can do a more sophisticated analysis, find a root cause to a problem, and develop a solution based on an insight that he discovers. They can use SPSS, SAS, etc or open source tools with built-in data shaping and point-and-click machine learning to manipulate large amount of data.

Data engineers

They focus enable data integrations, connections (plumbing) and data quality. They do the underlying enablement that a data scientist and citizen analyst depend on. They typically depend on solutions like DataWorks Forge to access multiple data source and to transform them within a fully managed service.

Application developers

Application developers are responsible for making analytics algorithms actionable within a business process, generally supported by a production system. Beginning with the analytics algorithms built by citizen analysts or data scientists, they work with the final data model representation created by data engineers, building an application that ties into the overall business process. They use something like Bluemix development platform and APIs for the individual data and analytics services.

Putting it all together

Image a scenario where a Citizen analyst notices (from a dashboard) that retail sales are down for the quarter. She pulls up Watson Analytics and uses it to discover that the underlying problem is specific to a category of goods and services in store in a specific region. But she needs more help to find the exact cause and a remedy.

She engages her data scientists and engineer. They discuss the need to pull in more data than just the transactional data the business analyst already has access to, specifically weather, social, and IoT data from the stores. The data engineer helps create the necessary access – the data scientists can then form and test various hypothesis using different analytic models.

Once the data scientist determines the root cause, he then shares the model with the developer who can then leverage it to improve the company’s mobile apps and websites to be more responsive in real-time to address the issue. The citizen analyst also shares the insight with the marketing department so they can take corrective action.

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DataStage now available on Cloud

For data integration projects, DataStage has been the work horse for many years. It is used by Data Engineers to extract data from many different sources, transform and combine the data, and then populate them for applications and end users. DataStage has many distinct advantages over other popular ETL tools.

ETL on CloudUntil recently, these capabilities were only available with the on-premises offering. Now DataStage is available on the Cloud as a hosted cloud offering. Customers can take advantage of the full capabilities of DataStage and without the burden and time consumption of standing up the infrastructure and installing the software themselves. Customers can quickly deploy a DataStage environment (from ordering to provisioning it on the cloud) and be up and running in a day or less. There is no up-front capital expenditure as customers only pay a monthly subscription based on the capacity they purchase. Licensing is also greatly simplified.

Using DatasStage on Cloud, existing DataStage customers can start new projects quickly. Since it is hosted in the IBM cloud, the machine and operating system are managed by IBM. The customer will not have to spend time to either increase the current environment or create a new one. In other words, Cloud elasticity makes them ready to scale and handle any workload. DataStage ETL job developers can immediately be productive and the data integration activities can span both on-premises and cloud data if necessary, as the DataStage jobs can be exported from the cloud and brought back to an on-premises DataStage environment.

datastage-on-cloud2As an example; A customer has data sources such as Teradata, DB2, etc. in their data center as well as SalesForce, MongoDB and other data residing in the Cloud. They need access to their existing data sources and their cloud data sources for a new customer retention project . This project requires some sophisticated data integration to bring it all together but they don’t have the IT resources or budget to stand up a new data integration environment in their own data center for this project. So, an instance of DataStage on the Cloud can be deployed for their use. The customer can access the DataStage client programs on the Cloud to work with DataStage. The access would be either through the public Internet or a private connection via the SoftLayer VPN. DataStage ETL jobs running in the Cloud can access the customer’s on-premise data sources and targets using secured protocols and encryption methods. In addition, these DataStage jobs can also access cloud data sources like dashDB as well as data sources on other cloud platforms using the appropriate secured protocols.

So with DataStage hosted on the Cloud you can:

  1. Extend your ETL infrastructure: Expand your InfoSphere DataStage environment or begin transitioning into a private or public cloud with flexible deployment options and subscription pricing.
  2. Establish ad hoc environments: Extend your on-premises capacity to quickly create new environments for ad hoc development and testing or for limited duration projects.
  3. Start new projects in the cloud: Move straight to the cloud without establishing an on-premises environment. Realize faster time-to-value, reduce administration burden and use low-risk subscription pricing.

Lift your Data to Cloud

database_migrationTo stay competitive and reduce cost, several Enterprises are realizing the merits of moving their data to Cloud. Due to their economies of scale cloud storage vendors can achieve lesser cost. Also Enterprises escape the drudgery of [capacity] planning, buying, commissioning, provisioning and maintaining storage systems. Data is even protected by replication to multiple data centers which Cloud vendors provide by default. You can read this blog listing the various advantages to move data to cloud.

But now the BIG challenge is to securely migrate the terabytes of Enterprise data to Cloud. Months can be spent coming up with airtight migration plan which does not disrupt your business. And the final migration may also take a long time impacting adversely the users, applications or customers using the source database.

Innovative data migration

In short, database migration can end up being a miserable experience. IBM Bluemix Lift is a self-service, ground-to-cloud database migration offering from IBM to take care of the above listed needs. Using Bluemix Lift, database migration becomes fast, reliable and secure. Here’s what it offers:

  • Blazing fast Speed: Bluemix Lift helps accelerate data transfer by embedding the IBM Aspera technology. Aspera’s patented and highly efficient bulk data transport protocol allows Bluemix Lift to achieve transport speeds much faster than FTP and HTTP. Moving 10 TB of data can take a little over a day, depending on your network connection.
  • Zero downtime: Bluemix Lift can eliminate the downtime associated with database migrations. An efficient change capture technology tracks incremental changes to your source database and replays them to your target database. As a result, any applications using the source database can keep running uninterrupted while the database migration is in progress.
  • Secure: Any data movement across the Internet requires strong encryption so that the data is never compromised. Bluemix Lift encrypts data as it travels across the web on its way to an IBM cloud data property.
  • Easy to use: Set up the source data connection, provide credentials to the target database, verify schema compatibility with the target database engine and hit run. That’s all it takes to kick off a database migration with Bluemix Lift.
  • Reliable: The Bluemix Lift service automatically recovers from problems encountered during data extract, transport and load. If your migration is interrupted because of a drop in network connectivity, Bluemix Lift automatically resumes once connectivity returns. In other words, you can kick off a large database migration and walk away knowing that Bluemix Lift is on the job.

Speed, zero downtime, security, ease of use and reliability—these are the hallmarks of a great database migration service, and Bluemix Lift can deliver on all these benefits. Bluemix Lift gets data into a cloud database as easy as selecting Save As –> Cloud. Bluemix Lift also provides an amazing jumping-off point for new capabilities that are planned to be added in the future such as new source and target databases, enhanced automation and additional use cases. Take a look at IBM Bluemix Lift and give it a go.

IBM Bluemix Data Connect

I have been tracking the development on IBM Bluemix Data Connect quite closely. One of the reason is that I was a key developer in the one of the first few services that it launched almost two years back under the name of DataWorks. Two weeks back I attended a session on Data Connect by the architect and saw a demo. I am impressed at the way it has evolved since then. Therefore I am planning to re-visit DataWorks again, now as IBM Bluemix Data Connect. In this blog I will reconcile the role that IBM Bluemix Data Connect play in the era of cloud computing, big data and the Internet of Things.

Research from Forrester found that 68 percent of simple BI requests take weeks, months or longer for IT to fulfill due to lack of technical resources. So this entails that the enterprises must find ways to transform line of business professionals into skilled data workers, taking some of the burden off of IT. It means business users should be empowered work with data from many sources—both on premises and in the cloud—without requiring the deep technical expertise of a database administrator or data scientist.

This is where cloud services like IBM Bluemix Data Connect comes into picture. It enables both technical and non-technical business users to derive useful insights from data, with point and click access—whether it’s a few Excel sheets stored locally, or a massive database hosted in the cloud.

Data Connect is a fully managed data preparation and movement service that enables users to put data to work through a simple yet powerful cloud-based interface. The design team has taken lot of pain to design the solution in most simplistic way, so that a basic user can quickly get started with it. Data Connect empowers the business analyst to discover, cleanse, standardize, transform and move data in support of application development and analytics use cases.

Through its integration with cloud data services like IBM Watson Analytics, Data Connect is a seamless tool for preparing and moving data from on premises and off premises to an analytics cloud ecosystem where it can be quickly analyzed and visualized. Furthermore, Data Connect is backed by continuous delivery, which adds robust new features and functionality on a regular basis. Its processing engine is built on Apache Spark, the leading open source analytics project, with a large and continuously growing development community. The result is a best-of-breed solution that can keep up with the rapid pace of innovation in big data and cloud computing.

So here are highlights of IBM Bluemix Data Connect:

  • Allow technical and non-technical users to draw value from data quickly and easily.
  • Ensure data quality with simple data preparation and movement services in the cloud.
  • Integrate with leading cloud data services to create a seamless data management platform.
  • Continuous inflow of new and robust features
  • Best-of-breed ETL solution available on Bluemix  – IBMs Next-Generation Cloud App Development Platform