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Membership online training - Tuesday, October 2nd

CiviCRM - Thu, 09/27/2018 - 15:02

If you missed the Cividesk online training course, the Fundamentals of Membership Management in August, we have scheduled another session for this Tuesday, October 2nd from 9 to 11 am am MT.  This informative course covers all the basics of managing memberships and will help you get off to a great start using CiviMember.

Categories: CRM

How to load your Salesforce data into NetSuite

SnapLogic - Thu, 09/27/2018 - 13:06

Connecting customer relationship management (CRM) software with enterprise resource planning (ERP) technology is a fairly common integration requirement for organizations looking to complete a series of business goals from sales forecasting, revenue accounting by product or portfolio, to identifying highest revenue by industry or geography. To achieve these goals, integrators need to synchronize data across[...] Read the full article here.

The post How to load your Salesforce data into NetSuite appeared first on SnapLogic.

Categories: ETL

From Dust to Trust: How to Make Your Salesforce Data Better

Talend - Wed, 09/26/2018 - 17:15

Salesforce is like a goldmine. You own it but it’s up to you to extract gold out of it. Sound complicated? With Dreamforce in full swing, we are reminded that trusted data is the key to success for any organization.

According to a Salesforce survey, “68% of sales professionals say it is absolutely critical or very important to have a single view of the customer across departments/roles. Yet, only 17% of sales teams rate their single view of the customer capabilities as outstanding.”

As sales teams are willing to change into high-performing trusted advisors, they are still spending most of their time on non-selling activities. The harsh reality is that sales people cannot wait to get clean, complete, accurate and consistent data into their systems.  They often end up spending lots of time on their own correcting bad records and reuniting customer insights. To minimize their time spent on data and boost their sales numbers, they need your help to rely on single customer view filled with trusted data.

Whether you’re working for a nonprofit that’s looking for more donors or at a company looking to get qualified leads, managing data quality in your prospects or donator CRM pipeline is crucial.

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Quick patches won’t solve your data quality problem in the long run

Salesforce was intentionally designed to digitally transform your business processes but was unfortunately not natively built to process and manage your data. As data is exploding, getting trusted data is becoming more and more critical. As a result, lots of Incubators’ apps started emerging on the Salesforce Marketplace. You may be tempted to use them and patch your data with quick data quality operations. 

But you may end up with separate features built by separate companies with different levels of integration, stability, and performance. You also take the risk of having the app not supported over the long term, putting your data pipeline and operations at risk. This in turn, will only make things worse by putting all the data quality on your shoulders whereas you may rely on your sales representative to resolve data. And you do not want to become the bottleneck of your organization.

After the fact Data Quality is not your best option

Some Business Intelligence Solutions have started emerging, further allowing you to prepare your data at the Analytical Level. But this is often a one-shot option for one single need and not solving the fulfilling the full need. You will still have bad data to input into Salesforce. Salesforce Data can be used in multiple scenarios by multiple persons. Operating Data Quality directly into Salesforce Marketing, Service or Commerce Cloud is the best approach to deliver trusted data at its source so that everybody can benefit from it.

The Rise of Modern Apps to boost engagement:

Fortunately, Data Quality has evolved to become a team activity rather than a single isolated job. You then need to find ways and tools to engage your sales org into data resolution initiatives. Modern apps are key here to make that it a success.

Data Stewardship to delegate errors resolution with business experts

Next-generation data stewardship tools such as Talend Data Stewardship give you the ability to reach everyone who knows the data best within the organization. In parallel, business experts will be comfortable editing and enriching data within UI friendly tool that makes the job easier. Once you captured tacit knowledge from end users, you can scale it to millions of records thru built in machine learning capabilities within Talend Data Stewardship.

Data Preparation to discover and clean data directly with Salesforce

Self-service is the way to get data quality standards to scale. Data analyst spend 60% of their time cleaning data and getting it ready to use. Reduced time and effort mean more value and more insight to be extracted from data. Talend Data Preparation deals with this problem. It is a self – service application that allows potentially anyone to access a data set and then cleanse, standardize, transform, or enrich the data. With it’s ease of use, Data Preparation helps to solves  organizational pain points where often times employees are spending so much time crunching data in Excel or expecting their colleagues to do that on their behalf.

Here are two use cases to learn from:

Use Case 1: Standardizing Contact Data and removing duplicates from Salesforce

Duplicates are the bane of CRM Systems. When entering data into Salesforce, Sales Rep can be in a rush and create duplicates that stay for long. Let them pollute your CRM and it will impact every user and sales rep confidence in your data.

Data Quality here has a real direct business impact on your sales productivity and your marketing campaigns too.

Bad Data mean unreachable customers or untargeted prospects that escape from your customized campaigns leading to low conversion rate and lower revenue. 

With Talend Data Prep, you can really be a game changer: Data Prep allows you to connect natively and directly to your Salesforce platform and perform some ad-hoc data quality operations.

  • By entering your SDFC Credentials, you will get native access to customer fields you want to clean
  • Once data is displayed into Data Prep, Quality Bar and smart assistance will allow you to quickly spot your duplicates
  • Click the header of any column containing duplicates from your dataset.
  • Click the Table tab of the functions panel to display the list of functions that can be applied on the whole table
  • Point your mouse over the Remove duplicate rows function to preview its result and click to apply it
  • Once you perform this operation, your duplicates can be removed
  • You can also register this as a recipe you may want to apply it to other data sources
  • You also have some options in Data Prep to certify your dataset so other team members know this data source can be trusted
  • Collaborate with IT to expand your jobs with Talend Studio to fully automate your data quality operations and proceed with advanced matching operations

Use case 2:  Real time Data Masking into Salesforce

The GDPR defines pseudonymization as “the processing of personal data in such a way that the data can no longer be attributed to a specific data subject without the use of additional information.” Pseudonymization or anonymization therefore, may significantly reduce the risks associated with data processing, while also maintaining the data’s utility.

Using Talend Cloud, you can process it directly into Salesforce. Talend Data Preparation enables any business users to obfuscate data the easy way. After native connection with Salesforce Dataset:

  • Click the header of any column containing data to be masked from your dataset
  • Click the Table tab of the functions panel to display the list of functions that can be applied
  • Point your mouse over the Obfuscation function and click to apply it
  • Once you perform this operation, data will be masked and anonymized

When confronted with in-depth fields and more sophisticated data masking techniques, data engineers will take the lead operating pattern data masking techniques directly into Talend Studio and perform them into Salesforce within personal fields such as Security Numbers or Credit Cards.  You can still easily spot data to be masked into Data Prep and ask data engineers to perform anonymization techniques into Talend Studio in a second phase.


Without data quality tools and methodology, you will then end up with unqualified, unsegmented or unprotected customers’ accounts leading to lower revenue, lower marketing effectiveness and more importantly frustrated sales rep spending their time for trusted client data.  As strong as it may be, your Salesforce goldmine can easily transform itself into dust if you don’t put trust into your systems. Only platforms such as Talend Cloud with powerful data quality solutions can help you to extract hidden gold from your Salesforce data and deliver it trusted to the whole organization.

Want to know more? Go to Talend Connect London on October 15th & 16th or Talend Connect Paris on October 17th & 18th to learn from real business cases such as Greenpeace, Petit Bateau.

Whatever your background, technical or not, there will be a session that meets your needs.  We have plenty of use cases and data quality jobs we’ll expose both in technical and customer tracks.





The post From Dust to Trust: How to Make Your Salesforce Data Better appeared first on Talend Real-Time Open Source Data Integration Software.

Categories: ETL

Building Agile Data Lakes with Robust Ingestion and Transformation Frameworks – Part 1

Talend - Wed, 09/26/2018 - 10:31

This post was authored by Venkat Sundaram from Talend and Ramu Kalvakuntla from Clarity Insights.

With the advent of Big Data technologies like Hadoop, there has been a major disruption in the information management industry. The excitement around it is not only about the three Vs – volume, velocity and variety – of data but also the ability to provide a single platform to serve all data needs across an organization. This single platform is called the Data Lake. The goal of a data lake initiative is to ingest data from all known systems within an enterprise and store it in this central platform to meet enterprise-wide analytical needs.

However, a few years back Gartner warned that a large percentage of data lake initiatives have failed or will fail – becoming more of a data swamp than a data lake. How do we prevent this? We have teamed up with one of our partners, Clarity Insights, to discuss the data challenges enterprises face, what caused data lakes to become swamps, discuss the characteristics of a robust data ingestion framework and how it can help make the data lake more agile. We have partnered with Clarity Insights on multiple customer engagements to build these robust ingestion and transformation frameworks to build their enterprise data lake solution.

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Current Data Challenges:

Enterprises face many challenges with data today, from siloed data stores and massive data growth to expensive platforms and lack of business insights. Let’s take a look at these individually:

1. Siloed Data Stores

Nearly every organization is struggling with siloed data stores spread across multiple systems and databases. Many organizations have hundreds, if not thousands, of database servers. They’ve likely created separate data stores for different groups such as Finance, HR, Supply Chain, Marketing and so forth for convenience’s sake, but they’re struggling big time because of inconsistent results.

I have personally seen this across multiple companies: they can’t tell exactly how many active customers they have or what the gross margin per item is because they get varying answers from groups that have their own version of the data, calculations and key metrics.

2. Massive Data Growth

No surprise that data is growing exponentially across all enterprises. Back in 2002 when we first built a Terabyte warehouse, our team was so excited! But today even a Petabyte is still small. Data has grown a thousandfold—in many cases in less than two decades‚—causing organizations to no longer be able to manage it all with their traditional databases.

Traditional systems scale vertically rather than horizontally, so when my current database reaches its capacity, we just can’t add another server to expand; we have to forklift into newer and higher capacity servers. But even that will have limitations. IT has become stuck in this deep web and is unable to manage systems and data efficiently.

Diagram 1: Current Data Challenges


3. Expensive Platforms

 Traditional relational MPP databases are appliance-based and come with very high costs. There are cases where companies are paying more than $100K per terabyte and are unable to keep up with this expense as data volumes rapidly grow from terabytes to exabytes.

4. Lack of Business Insights

Because of all of the above challenges, business is just focused on descriptive analytics, like a rear mirror view of what happened yesterday, last month, last year, year over year, etc., instead of focusing on predictive and prescriptive analytics to find key insights on what to do next.

What is the Solution?

One possible solution is consolidating all disparate data sources into a single platform called a data lake. Many organizations have started this path and failed miserably. Their data lakes have morphed into unmanageable data swamps.

What does a data swamp look like? Here’s an analogy: when you go to a public library to borrow a book or video, the first thing you do is search the catalog to find out whether the material you want is available, and if so, where to find it. Usually, you are in and out of the library in a couple of minutes. But instead, let’s say when you go to the library there is no catalog, and books are piled all over the place—fiction in one area and non-fiction in another and so forth. How would you find the book you are looking for? Would you ever go to that library again? Many data lakes are like this, with different groups in the organization loading data into it, without a catalog or proper metadata and governance.

A data lake should be more like a data library, where every dataset is being indexed and cataloged, and there should be a gatekeeper who decides what data should go into the lake to prevent duplicates and other issues. For this to happen properly, we need an ingestion framework, which acts like a funnel as shown below.

Diagram 2: Data Ingestion Framework / Funnel

A data ingestion framework should have the following characteristics:
  • A Single framework to perform all data ingestions consistently into the data lake.
  • Metadata-driven architecture that captures the metadata of what datasets to be ingested, when to be ingested and how often it needs to ingest; how to capture the metadata of datasets; and what are the credentials needed connect to the data source systems.
  • Template design architecture to build generic templates that can read the metadata supplied in the framework and automate the ingestion process for different formats of data, both in batch and real-time
  • Tracking metrics, events and notifications for all data ingestion activities
  • Single consistent method to capture all data ingestion along with technical metadata, data lineage, and governance
  • Proper data governance with “search and catalog” to find data within the data lake
  • Data Profiling to collect the anomalies in the datasets so data stewards can look at them and come up with data quality and transformation rules

Diagram 3: Data Ingestion Framework Architecture

Modern Data Architecture Reference Architecture

Data lakes are a foundational structure for Modern Data Architecture solutions, where they become a single platform to land all disparate data sources and: stage raw data, profile data for data stewards, apply transformations, move data and run machine learning and advanced analytics, ultimately so organizations can find deep insights and perform what-if analysis.

Unlike traditional data warehouses, where business won’t see the data until it’s curated, using the modern data architecture businesses can ingest new data sources through the framework and analyze it within hours and days, instead of months and years.

In the next part of this series, we’ll discuss, “What is Metadata Driven Architecture?” and see how it enables organizations to build robust ingestion and transformation frameworks to build successful Agile data lake solutions. Let me know what your thoughts are in the comments and head to Clarity Insights for more info

The post Building Agile Data Lakes with Robust Ingestion and Transformation Frameworks – Part 1 appeared first on Talend Real-Time Open Source Data Integration Software.

Categories: ETL

The Future of Java and How It Affects Java and Liferay Developers

Liferay - Wed, 09/26/2018 - 05:25

Java 11 has just been released (on Sep, 25th) and it comes with the consolidation of a series of changes, not only to the language and the platform, but to the release and support model that has lead to some noise on the future of Java.

Probably the two most notable concerns are the end of public updates for Java 8 and the uncertainty of the rights to use Oracle JDK without paying for commercial support.

Although it is true that with the new changes, Oracle is going to put focus on only the latest java version, and will offer commercial support for its JDK, it is also true that we -as Java and Liferay developers- will still be able to use Java and the JDK freely.


The changes in the release cadence and model

In 2017, it was already announced that Java was going to move faster, scheduling a new feature release every six months, on March and September. That meant that after Java 9, released on September 2017, Java 10 was going to be released on March 2018 and java 11 on September 2018, which just has happened.

The second big change has been the introduction of the concept of LTS (Long Time Support) versions, which are versions that are 'marked' to be maintained for more than six months. And this mark is not a compromise from Oracle, but a recommendation for the industry and community.

On the other side, the differences between Oracle JDK and OpenJDK have been eliminated. In fact, Oracle is leading the work on OpenJDK LTS code base during the six first months after the release. This makes OpenJDK the new default. 

After that, Oracle will provide updates for their Oracle JDK only to those customers that have a commercial license. But at the same time, Oracle will allow and encourage other vendors (like IBM, RedHat, Azul or the community based AdoptOpenJDK) to work on the OpenJDK LTS codebase to keep providing updates.

That means that Oracle will provide free updates for every Java version during the first six months after release, and other vendors and community initiatives will provide free updates for LTS versions for a longer period.


Will Java 8 still be freely available?

Java 8 was a LTS, so it is replaced by Java 11, which is also a LTS. And that means that oracle has announced that OpenJDK 8 will end its official support for commercial use in January 2019.

But the good news is that Red Hat has already applied to lead the development and updates of OpenJDK 8 after that date, and other companies like Amazon, Azul Systems or IBM have also announced that they will support Red Hat.

So we will actually have free Java 8 updates at least until September 2023, based on OpenJDK.


In conclusion

Although Oracle is focusing their effort on the six month release, there is still support for free updates for the LTS versions of Java, first provided by Oracle and, after that, maintained and updated by other vendors which will offer free updates and, in some cases, also will offer commercial support.

If you want to dig a little bit more on the details of all these changes, there is a comprehensive document with the title "Java is Still Free" written and updated by the community of Java Champions that has a lot of details this topic, and includes and updated table with the plans for support and updates, which so far, is as follows:

And for Liferay, we will also pay attention to this changes and the plans to support the different versions of Java in order to update our Liferay JDK Compatibility Support accordingly. 

David Gómez 2018-09-26T10:25:00Z
Categories: CMS, ECM

Product maintenance in CiviCRM

CiviCRM - Tue, 09/25/2018 - 21:23

As our North American colleagues (and those who have made the big trip over there) head into the governance sprint now seems like a good time to recap on product maintenance in CiviCRM. Product maintenance, as I discuss, is the monthly routine processes we do to incorporate patches & contributions into the CiviCRM product. This blog is kinda long & weedsy - so if it’s not for you then take a look at this baby octopus instead.


Categories: CRM

How JDK11 Affects Liferay Portal/DXP Users

Liferay - Tue, 09/25/2018 - 16:20

With the release of JDK11, Oracle's new Java SE Support Policy (and here) brings sweeping changes to the Java Enterprise community.

If you would like a good explanation of the changes to come, I highly recommend this video.

Here are my thoughts on how some of these changes will affect Liferay Portal/DXP users:

Starting with JDK11, you will no longer be able to use Oracle JDK for free for commercial purposes.

All commercial projects will require a subscription/service from Oracle to get the Oracle JDK.  The majority of Liferay users are commercial users who deploy on Oracle JDK.  If you do not pay for support from Oracle for their JDK or one of their other products such as Web Logic, you will need to make a decision on whether you wish to continue to use Oracle JDK.

An OpenJDK binary is now your free option

Oracle will continue to provide a free alternative with their Oracle OpenJDK binary.  There will also be others such as Azul, RedHat, IBM, and AdoptOpenJDK which will also provide their own binaries.  For now, Liferay has only certified, Oracle's OpenJDK binary.  We have to yet to determine whether all OpenJDK libraries can fall under the same name or if we need to certify them individually.

A new JDK will be released every 6 months and some of them will be marked as LTS release.

Prior to JDK9, all JDK's were essentially LTS releases.  We were able to receive years of bug fixes before we had to worry about a new release.  We will now see a new JDK release every 6 months (March, September).

As of now, Liferay has decided we will not certify every single major release of the JDK.  We will instead choose to follow Oracle's lead and certify only those marked for LTS.  If you have seen our latest compatibility matrix, you will notice that we did not certify JDK9 or JDK10.  We will instead certify JDK11 and JDK17 and JDK23 as those have been the ones marked as LTS.  This is subject to change.

Oracle will only guarantee 2 updates per major release

JDK8 has received 172 updates so far. In contrast, JDK9, the first release that Oracle implemented this policy, had 4 updates, while JDK10 only got the minimum 2 updates. Although JDK11 is designated as an LTS release, there is no guarantee of more than 2 updates from Oracle.

We will have to wait until JDK12 is released to see what will happen with JDK11.  The optimistic side of me feels that the Java open source community will continue to backport security issues and bugs well after Oracle decides to stop.  We will have to wait and see.

January 2019 will be the last free public update for JDK8. 

If you are a Liferay user and you have not made a plan for your production servers, please start!

I will provide the paths available currently but these are in no way recommendations provided by Liferay.  It is up to you to make the best decision for your own company.

  • Continue to use Oracle JDK8 without any future patches
  • Continue to use Oracle JDK8 and pay for a subscription
  • Switch to Oracle JDK11 and pay for a subscription
  • Switch to Oracle OpenJDK11 binary (knowing that you will have to make this decision again in 6 months)
  • Switch to a certified version of IBM JDK.

I will try to update this list as more options become available i.e. we decide to certify AdoptOpenJDK, Azul Zulu, RedHat JDK.

I am eager to see how the rest of the enterprise community reacts to these changes. 

Please leave a comment below with any thoughts on Oracle's changes or suggestions on what you would like to see Liferay do in regards to JDK support.

David Truong 2018-09-25T21:20:00Z
Categories: CMS, ECM

How code-heavy approaches to integration impede digital transformation

SnapLogic - Tue, 09/25/2018 - 15:30

At the heart of digital transformation lies the urge to survive. Any company, no matter how powerful, can go bankrupt, suffer a wave of layoffs, or get thrust into the bad end of an acquisition deal. Market disruption, led by those who have put digital transformation into practice, contributes heavily to such calamities. Look no[...] Read the full article here.

The post How code-heavy approaches to integration impede digital transformation appeared first on SnapLogic.

Categories: ETL

Overview: Talend Server Applications with Docker

Talend - Tue, 09/25/2018 - 08:53
Talend & Docker

Since the release of Talend 7, a major update in our software, users have been given the ability to build a complete integration flow in a CI/CD pipeline which allows to build Docker images. For more on this feature, I invite you to read the blog written by Thibault Gourdel on Going serverless with Talend through CI/CD and Containers.

Another major update is the support of Docker for server applications like Talend Administration Center (TAC). In this blog, I want to walk you through how to build these images. Remember, if you want to follow along, you can download your free 30-day trial of Talend Cloud here. Let’s get started!

Getting Started: Talend Installation Modes

In Talend provides two different installation modes when working with the subscription version. Once you received your download access to Talend applications, you have a choice:

  • Installation using the Talend Installer: The installer packages all applications and offers an installation wizard to help you through the installation.
  • Manual installation: Each application is available in a separate package. It requires a deeper knowledge of Talend installation, but it provides a lighter way to install especially for containers.

Both are valid choices based on your use case and architecture. For this blog, let’s go with manual installation because we will be able to define an image per application. It will be lighter for container layers and we will avoid overload these layers with unnecessary weight. For more information on Talend installation modes, I recommend you look at Talend documentation Talend Data Fabric Installation Guide for Linux (7.0) and also Architecture of the Talend products.

Container Images: Custom or Generic?

Now that we know a bit more about Talend Installation, we can start thinking about how we will build our container images. There are two directions when you want to containerize an application like Talend.  Going for a custom type image or a generic image:

  • A custom image embeds part of or a full configuration inside the build process. It means that when we will run the container, it will require less parameters than a generic image. The configuration will depend of the level of customization.
  • A generic image does not include specific configuration, it corresponds to a basic installation of the application. The configuration will be loaded at runtime.

To illustrate this, let’s look at an example with Talend Administration Center. Talend Administration Center is a central application in charge of managing users, projects and scheduling. Based on the two approaches for building an image of Talend Administration Centre:

  • A custom image can include:
    • A specific JDBC driver (MySQL, Oracle, SQL Server)
    • Logging configuration: Tomcat logging
    • properties: Talend Administration Centre Configuration
    • properties: Clustering configuration
  • A generic image
    • No configuration
    • Driver and configuration files can be loaded with volumes

The benefits and drawbacks of each approach will depend on your configuration, but :

  • A custom image:
    • Requires less configuration
    • Low to zero external storage required
    • Bigger images: more space required for your registry
  • A generic image
    • Lighter images
    • Reusability
    • Configuration required to run.
Getting Ready to Deploy

Once we have our images, and they are pushed to a registry, we need to deploy them. Of course, we can test them on a single server with a docker run command. But let’s face it, it is not a real-world use case. Today if we want to deploy a container application to on-premise or in the cloud, Kubernetes has become de facto the orchestrator to use. To deploy on Kubernetes, we can go with the standard YAML files or a Helm package. But to give a quick example and a way to test on a local environment, I recommend starting with a docker-compose configuration as in the following example:


version: '3.2' services:   mysql:     image: mysql:5.7     ports:     - "3306:3306"     environment:       MYSQL_ROOT_PASSWORD: talend       MYSQL_DATABASE: tac       MYSQL_USER: talend       MYSQL_PASSWORD: talend123     volumes:       - type: volume         source: mysql-data         target: /var/lib/mysql   tac:     image: mgainhao/tac:7.0.1     ports:     - "8080:8080"     depends_on:       - mysql     volumes:       - type: bind         source: ./tac/config/         target: /opt/tac/webapps/org.talend.administrator/WEB-INF/classes/       - type: bind         source: ./tac/lib/mysql-connector-java-5.1.46.jar         target: /opt/tac/lib/mysql-connector-java-5.1.46.jar volumes:   mysql-data:

The first MySQL service, creates a database container with one schema and a user tac to access it. For more information about the official MySQL image, please refer to:

The second service is my Talend Administration Centre image, aka TAC, a simplified version as it uses only the MySQL database. In this case, I have a generic image that is configured when you run the docker-compose stack.  The JDBC driver is loaded with a volume like the configuration.

In a future article, I’ll go in more details on how to build and deploy a Talend stack on Kubernetes. For now, enjoy building with Talend and Docker!



The post Overview: Talend Server Applications with Docker appeared first on Talend Real-Time Open Source Data Integration Software.

Categories: ETL

Red alert, shields up - The work of the Joomla Security Team

Joomla! - Tue, 09/25/2018 - 04:00

A CMS-powered website has all the ingredients for an IT security nightmare: it is publicly accessible, it’s running on powerful machines with great connectivity and the underlying system is used countless times around the globe, making it an attractive target for attackers.
The Joomla Security Strike Team (JSST) is working hard to make sure that this nightmare doesn’t become reality for Joomla users!

Categories: CMS

Red alert, shields up - The work of the Joomla Security Team

Joomla! - Tue, 09/25/2018 - 04:00

A CMS-powered website has all the ingredients for an IT security nightmare: it is publicly accessible, it’s running on powerful machines with great connectivity and the underlying system is used countless times around the globe, making it an attractive target for attackers.
The Joomla Security Strike Team (JSST) is working hard to make sure that this nightmare doesn’t become reality for Joomla users!

Categories: CMS

Vtiger recognised as “One to Watch” and voted 4th best in cloud CRMs!

VTiger - Tue, 09/25/2018 - 01:26
It’s always a delight to receive great feedback from our customers. It’s our way of knowing that our efforts are being acknowledged and loved. Excitingly enough, Vtiger was recently recognised as “One to Watch” in the mid-size market category by CRM Magazine as part of their 2018 CRM Market Awards. Also, we take great pleasure […]
Categories: CRM

Data Scientists Never Stop Learning: Q&A Spotlight with Isabelle Nuage of Talend

Talend - Mon, 09/24/2018 - 14:21

Data science programs aren’t just for students anymore. Now, data scientists can turn to open online courses and other resources to boost their skill sets. We sat down with Isabelle Nuage, Director of Product Marketing, Big Data at Talend to get insight on what resources are out there:

Q: How would you characterize the differences between data science research processes and machine learning deployment processes?

Isabelle: In the grand scheme of things, Data Science is Science. Data Scientists do a lot of iterations, through trial & error, before finding the right model or algorithm that fit their needs and typically work on sample data. When IT needs to deploy machine learning at scale, they’ll take the work from the data scientists and try to reproduce at scale for the enterprise. Unfortunately it doesn’t always work right away as sample data is different in that real life data has inconsistencies often missing values as well as other data quality issues.

Q: Why is putting machine learning (ML) models into production hard?

Isabelle: Data Scientists work in a lab mode, meaning they are often operating like lone rangers. They take the time to explore data, try out various models and sometimes it can take weeks or even months to deploy their data models into production. By that time, the models have already become obsolete for the business. Causing them to have to go back to the drawing board. Another challenge for Data Scientists is data governance, and without it data becomes a liability. A good example of this is in clinical trial data where sensitive patient information has to be masked so it is not accessible by everyone in the organization.

Q: What are the stumbling blocks?

Isabelle:  There is a lack of collaboration between the Data Science team and IT where each tend to speak their own language and have their own set of skills that the other might not understand. Data Science is often considered to be a pure technology discipline and not connected to business needs as the asks are often tied to the need for fast decision making in order to innovate and outsmart the competition. Existing landscapes, such as enterprise warehouses, are not flexible enough to enable Data Science teams access to all the historical and granular information as some data is stored on tapes. IT is needed to create a Data Lake in order to store all that historical data to train the models and add the real-time data enabling real-time decisions.

Q: How are enterprises overcoming them?

Isabelle: Enterprises are creating Cloud data lakes (better suited for big data volumes and processing) and leveraging the new services and tools such as serverless processing to optimize the cost of machine learning processing on big data volume. Additionally they are also creating a center of excellence to foster collaboration across teams as well as hiring a Chief Data Officer (CDO) to really elevate data science to a business discipline.

Q: What advice might you offer enterprises looking to streamline the ML deployment process?

Isabelle: Use tooling to automate the manual tasks such as hand-coding that foster collaboration between the Data Science and IT teams. By letting the Data Science team explore and do their research, but let IT govern and deploy data so it’s not a liability for the organization anymore. And doing this in a continuous iteration and delivery fashion will enable continuous smart decision making throughout the organization.

Q: What new programs for learning data science skills have caught your attention and in what ways do they build on traditional learning programs?

Isabelle: I’m most interested in new tools that democratize data science, provide a graphical, easy-to-use UI and suggest the best algorithms for the dataset, rather than going through a multitude of lengthy trials and errors. These tools make data science accessible to more people, like business analysts, so more people within the enterprise can benefit from the sophisticated advanced analytics for decision-making. These tools help people get a hands-on experience without needing a PhD.

Q: What are some of your favorite courses and certifications?

Isabelle: I’d say, Coursera as it offers online courses where people can learn at their own pace, they even offer some free data science and  free machine learning courses too. Another great option is MIT eLearning, which also offers course for Data Science and Big Data.

Check out Talend Big Data and Machine Learning Sandbox to get started.


The post Data Scientists Never Stop Learning: Q&A Spotlight with Isabelle Nuage of Talend appeared first on Talend Real-Time Open Source Data Integration Software.

Categories: ETL

Knowledge Sharing in Software Projects

Open Source Initiative - Mon, 09/24/2018 - 13:15

In a special guest post, Megan L. Endres, Professor of Management, at Eastern Michigan University provides a debrief of data gathering from a recent survey on Knowledge Sharing promoted by the OSI.

Thank you!

We are extremely grateful to those who filled out the survey. We feel that our research can help create better environments at work, where team members can share knowledge and innovate.

Purpose of the Study
Our research is focused on knowledge sharing in ambiguous circumstances. Six Sigma is a method of quality control that should reduce ambiguity, given its structured approach. We ask whether the reduction in ambiguity is coupled with a reduction in knowledge sharing as well.

Who responded?

A total of 54 people responded, of whom 58% had a bachelor’s degree and 26% had a master’s degree. Average of full-time work experience was 13.9 years, and average of managerial experience was 6.7 years.

Most respondents (53%) reported working in an organization with 400+ full-time employees, although a strong second (37%) reported working with 100 or fewer.

Most reported that they work on a team of 3 members (21%), although a large percentage work on teams with 4 members (18.4%) and 5 members (13.2%). The complexity of the team tasks was moderately high, rated 3.66 on a 1 to 5 scale (least to most complex) (s.d. = 1.05).

Knowledge and Sharing

Respondents believed they brought considerable expertise to their team projects, which could be a result of good team assignments according to knowledge and skill. The average expertise reported was 4.13, on a scale of 1 (very low) to 5 (very high) (s.d. = 0.99).

Important variables we gathered are below with the mean and standard deviation. These are the average of a set of questions that was tested for reliability and averaged. It is important to note that standard deviations are all about 1 and, given a 5-point scale, this indicates general agreement among those who responded. The average of these variables was the same for varied years of experience, years of management, size of company, and level of education.

Variable Mean I share knowledge on my teams 4.35 My team shares knowledge with me 3.51 Knowledge sharing is valuable 4.43 My teams are innovative/creative 4.05 I have clear goals/feedback 3.17




Relationships in the Data

We will be gathering more data in order to perform more complex data analysis, but correlations show relationships that may prove to be important.

Significant relationships include:

  • Higher self-reported knowledge sharing is related to more clear goal setting at work, more innovative teamwork, and positive knowledge sharing attitudes. This is not surprising because an environment with positive knowledge sharing has better communication between team members and, therefore, clarifications are more likely when goals aren’t clear. Those who worked for larger organizations (400+ employees) said that their goal setting was clearer. This also is not surprising because more formal structure in the organization probably is associated with formal performance reviews and procedures.
  • Higher team knowledge sharing is associated with less likelihood one will have a Six Sigma belt and with lower Six Sigma knowledge. This may indicate that knowledge sharing, and Six Sigma are negatively related, but until a larger sample of responses is gathered, this is only a proposition.
  • The open source software questions did not reveal important information so far. That is because you are a part of a sample that uniformly has positive attitudes toward open source (in general). Others will fill out the survey in the future who are not affiliated with open source groups and variation in the responses will allow us to study relationships with other data.

Megan L. Endres, Professor of Management, Eastern Michigan University

Knowledge Sharing in Software Projects, by Megan L. Endres, CC-BY 4.0. 2018

Knowledge-sharing, by Ansonlobo. CC BY-SA 4.0. 2016. From Wikimedia Commons.

Categories: Open Source

Membership Renewal online class - Wednesday, September 26th

CiviCRM - Sun, 09/23/2018 - 16:51

Join Cividesk on Wednesday, September 26th at 9 am PT/ 10 am MT/ 11 am CT/ 12 pm ET for this informative 2 hour online session that will cover best practices for organizing membership renewals in CiviCRM.  This class is recommended as the next step in your CiviCRM training for those who have taken the "Fundamentals of Membership Management" session. 

Categories: CRM

The 2018 Strata Data Conference and the year of machine learning

SnapLogic - Fri, 09/21/2018 - 15:10

Recently, I represented SnapLogic at the September 2018 Strata Data Conference at the Javits Center in New York City. This annual event is a good indication of trends in data – big and otherwise. If 2017 was the year of Digital Transformation, 2018 is the year of machine learning. Many of the exhibitor’s booth themes[...] Read the full article here.

The post The 2018 Strata Data Conference and the year of machine learning appeared first on SnapLogic.

Categories: ETL

What’s In Store for the Future for Master Data Management (MDM)?

Talend - Fri, 09/21/2018 - 12:37

Master Data Management (MDM) has been around for a long time, and many people like myself, have been involved in MDM for many years. But, like all technologies, it must evolve to be successful. So what are those changes likely to be, and how will they happen?

In my view, MDM is and will change in important two ways in the coming years. First, there will be technological changes, such as moving MDM into the cloud or moving into more ‘software as a service’, or SaaS, offerings, which will change the way MDM systems are built and operated. Secondly, there are and will be more fundamental changes within MDM itself, such as moving MDM from single domain models into truly multi-domain models. Let’s look at these in more details.

Waves of MDM Change: Technical and Operational

New and disruptive technologies make fundamental changes to the way we do most things. In the area of MDM, I expect that to change in two main areas. First comes the cloud. In all areas that matter in data we are seeing moves into the cloud and I expect MDM to be no different. The reasons are simple and obvious, the move towards MDM being offered as a SaaS offering brings cost savings in build, support, operation, automation and maintenance and is therefore hugely attractive to all businesses. I expect that going forward we will see MDM more and more being offered as a SaaS.

The second area I see changes happening are more fundamental. Currently, many MDM systems concentrate on single-domain models. This is the way it has been for many years and currently manifests itself in the form of a ‘customer model’ or a ‘product model’. Over time I believe this will change. More and more businesses are looking towards multi-domain models that will, for example, allow models to be built that have the links between customer and partners, products, suppliers etc. This is the future for MDM models, and already at Talend, our multi-domain MDM tool allows you to build models of any domain you choose. Going forward, its clear that linking those multi-domain models together will be the key.

Watch The 4 Steps to Become a Master at Master Data Management now.
Watch Now MDM and Data Matching

Another area of change that is on the way is in regards to how MDM systems do matching. Currently, most systems do some type of probabilistic matching on properties within objects. I believe the future will see more of these MDM systems doing ‘referential matching’. By this, I mean making more use of the reference database, which may contain datasets like demographic data, in order to do better data matching. Today, many businesses use data that is not updated often enough and so becomes of less and less value. Using external databases to say, get the updated address of your customer or supplier, should dramatically change the value of your matching.

Machine Learning to the Rescue

The final big area of change coming in the future for MDM is the introduction of intelligence or machine learning. In particular, I forecast we will see intelligence in the form of machine learning survivorship. This will like take the form of algorithms which ‘learn’ how records survive and will, therefore, use these results to make predictions about which records survive, and which don’t. this will free up a lot of time for the data steward. 


Additional changes will likely also come around the matching of non-western names and details (such as addresses). At the moment they can be notoriously tricky as, for example, algorithms such as Soundex simply can’t be applied to many languages. This will change and we should see support for more and more languages.

One thing I am certain of though, many of these areas I mentioned are being worked on, all vendors will likely make changes in these areas and Talend will always be at the forefront of development in the future of Master Data Management. Do you have any predictions for the future of MDM? Let me know in the comments below.

Learn more about MDM with Talend’s Introduction to Talend’s Master Data Management tutorial series, and start putting its power to use today!


The post What’s In Store for the Future for Master Data Management (MDM)? appeared first on Talend Real-Time Open Source Data Integration Software.

Categories: ETL

Great CiviCRM meetup in Antwerp!

CiviCRM - Fri, 09/21/2018 - 07:45

On Tuesday 11 September 2018 we had quite an enjoyable and interesting CiviCRM meetup in Antwerp.


A total of 32 participants gathered at the University of Antwerp Middelheim campus to share their stories around CiviCRM.

Categories: CRM

Introducing Diet Civi

CiviCRM - Fri, 09/21/2018 - 07:21

Diet Civi is a new "working" group within the CiviCRM community.


Our main objectives are to

  • improve CiviCRM’s ability to support a variety of different workflows on the common core data model.

  • define, coordinate, foster, and fund projects to achieve this.


Categories: CRM

API World recap and self-service integration with AI

SnapLogic - Thu, 09/20/2018 - 16:57

It’s been about a week since I attended API World in San Jose, California to present an hour-long keynote, “Supercharging Self-Service API Integration with AI,” and I wanted to share some of my takeaways from this great conference. For those who were unable to attend, I shared SnapLogic’s journey with machine learning (ML), going from[...] Read the full article here.

The post API World recap and self-service integration with AI appeared first on SnapLogic.

Categories: ETL
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