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A Guide to the Best Javascript Visualization Libraries

May 26, 2020 Sisense

Developing analytic apps is a bold new direction for product teams. The Toolbox is where we talk development best practices, tips, tricks, and success stories to help you build the future of analytics and empower your users with the insights and actions they need.

JavaScript data visualization tools are in greater demand now than ever before because of the enormous growth of data. Marketing, finance, and sales teams all rely on visualizations to help them understand their data. Javascript visualization libraries empower product teams and other builders to create custom visuals that support their partners and can even be used to create new revenue streams in the form of customer-facing analytic apps.

Whatever their specialty or goals, users want their data to be displayed visually so that they can see insights from their data easily and act on them quickly. Data visualizations of key performance indicators (KPI) can even be sent automatically to users as they change, allowing them to make faster, smarter decisions.

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Understanding Javascript data visualization libraries

JavaScript data visualization libraries don’t just create beautiful visuals, they help make business intelligence (BI) more real to end-users. The developers building these visualizations must be guided by their colleagues who will actually be making use of them. They must also have access to flexible BI platforms (like Sisense) that can handle visualizations from a wide array of Javascript visualization libraries, which will allow them to input custom visuals that show users what they need to see in a way that makes sense to them. These visuals can include:

Customizable charts — sunburst, Sankey, dependency, and others
Flexible API with clear documentation and sample code
Vibrant chart animation capabilities
User interactivity with chart elements
Compatible with React, Angular, Vue, and other libraries
Chart/Data export to diverse formats: PDF, JPG, PNG
Multiple data type compatibility: XML, JSON, CSV

Of the 50 or so competing JavaScript visualization libraries available, we’re going to explore 5 libraries that deliver a wide array of features to try to help you determine which one is the best for your application.

This decision may be dictated by a single factor such as compatibility with an existing Angular app or by an end-user performance requirement such as minimum chart rendering time. We’ll explore the intricacies of the different JavaScript libraries by reviewing example charts and code snippets.

Stunning chart diversity with D3.js

A Guide to the Best Javascript Visualization Libraries — This Sankey diagram shows the relationship between energy source supply and demand (source).

The D3.js data visualization library supports an impressive array of chart options. Combining user interactivity with chart animation puts this library in our top five. The best place to start comparing the capabilities of visualization libraries is to look at example charts and the code that generates their elements. So, let’s jump right into D3.js with a popular sample chart from this library’s GIT repo.

This example chart interactively reveals the hierarchy of relationships among the classes of an application. In this screenshot of the interactive animation, hovering the mouse over “Data” lights up the functions shared among the classes. In this scenario, the class functions which it imports highlight as red curves, while classes which import its functions are shown as blue curves:

When reviewing the best visualization libraries, it is important to be aware that many other libraries are actually built with D3.js. For example, Nivo, Plotly.js, and Recharts are all built from the open-source D3.js library.

The D3.js API is flexible and features the popular d3.js dashboard which is also widely implemented in the best analytics applications — apps which demand the best quality chart performance. This code snippet contains the function which maps the hierarchy in the above chart (source):

function hierarchy(data, delimiter = ".") {
  let root;
  const map = new Map;
  data.forEach(function find(data) {
    const {name} = data;
    if (map.has(name)) return map.get(name);
    const i = name.lastIndexOf(delimiter);
    map.set(name, data);
    if (i >= 0) {
      find({name: name.substring(0, i), children: []}).children.push(data);
      data.name = name.substring(i + 1);
    } else {
      root = data;
    }
    return data;
  });
  return root;
}

An impressive selection of D3.js chart types, along with interactive examples and code snippets, is available at the D3.js GIT repo.

Google Charts — all the basics

Featuring a comprehensive chart gallery and extensive documentation of the API, Google Charts fulfills the expectations one might have when “standing on the shoulders of a giant.” The Chart Gallery contains examples of 29 chart types with example code for developers who are getting started charting with JavaScript. Since we began on page one with a D3.js Sankey diagram, let’s compare that with Google’s Sankey:

Google Charts’ version has glowing color mouseover interactivity with popup comments and a rich set of features to bring the chart to life and keep the user focused on critical data. Here is the sample code to define the colors in the chart (source):

var colors = ['#a6cee3', '#b2df8a', '#fb9a99', '#fdbf6f',
                  '#cab2d6', '#ffff99', '#1f78b4', '#33a02c'];
    var options = {
      height: 400,
      sankey: {
        node: {
          colors: colors
        },
        link: {
          colorMode: 'gradient',
          colors: colors
        }
      }
    };

Google Charts documentation is concise and clearly organized. It’s also is free to use, but not open source. Data must be piped to the Google Charts API for plotting. The implication here is that users may not be comfortable with the liability of shipping confidential client data out of the environment when the Google source can’t be hosted in-house.

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Highcharts: Strong community and API reference

A robust charting library, Highcharts has both free and paid versions. The source code can be installed in-house or in a Cloud PaaS, which is advantageous for analytic apps with strict data security constraints. A significant set of large enterprises use Highcharts, and so the community of contributing users is large. Beyond the opening list of standard features, Highcharts’ high points include:

Excellent API reference, and community showcase.
Compatible with Angular, React, and .NET plus more.
Charts export to all web image formats.

Let’s look at an interactive Highcharts example. A variation on the Sankey chart concept, the dependency wheel is also a flow diagram. However, the nodes are mapped to the edge of a circle with the links connecting the nodes. As with the Sankey, the width of the links and size of the nodes are proportional to the quantity of flow. Unlike the Sankey, though, the dependency wheel flow is multidirectional and single-layered. In the following example, a country’s export dependency on other countries is highlighted on mouseover event:

amCharts: Great for touchscreens

A distinguishing feature of the amCharts JS visualization library is specialized touch screen interactivity for charts displayed on mobile devices. This will appeal to endpoint users such as sales engineers who want to monitor product performance quickly while in the field. Zoom charts which illustrate more data at deeper levels can now be explored readily on tablets and phones.

amCharts is compatible with all the major frontend development frameworks like Angular and React. The amCharts Sankey diagram is configurable for animation. Here is an example with a distinctive color scheme which shows how the relative size of nodes and links illustrates quantitative proportionality in this chart of tech revenue distribution.

Although amCharts’ primary product is a paid version, it is possible to use the library for free on the condition that all generated charts contain the amCharts logo. So, this will not work for companies offering a whitelabel analytics app. There is a rich set of demos of all supported chart types with complete documentation and code samples, all of which lends well to the customization of charts.

Recharts: built for React

Although Recharts is actually built from D3.js components, it is important to our discussion for two reasons. First, Recharts is specifically intended for use with React, as essentially all of the D3.js library is wrapped in React classes. Secondly, Recharts is very popular, boasting a number of downloads not far behind Highcharts. Now that we are operating in the age of ubiquitous white-labled web apps, many React developers will find themselves using Recharts without knowing that they’re actually using the D3.js core.

Another thing to note about the Recharts is that there are two significant disadvantages to using it. For one, there are substantial open issues on the GIT repo, and bugs seem to appear faster than they are resolved. Additionally, Recharts can only be used in React projects. The large number of downloads also speaks indirectly about the wildly popular React JavaScript framework. This paradoxical limitation to React projects likewise refers back to our initial point that the choice of visualization library may be dictated solely by the application’s pre-existing development framework.

Choosing the right JavaScript visualization library for you

Having surveyed five of the most popular JavaScript data visualization libraries, you can see that there are substantial differences. Can your infrastructure host the library locally? Is the library license paid or free and does that depend on the intended use? Here is a list of basic criteria to consider when making the decision:

Type of charts required: Gantt, Sankey, Dependency wheel.
Level of support for library customization.
Size and characteristics of dataset
Browser support for library of choice: Chrome, Firefox.
The JavaScript framework used for the app: Angular, React, etc.
Intended device: desktop, mobile, or both.

And yet there is still one more very important criterion for choosing the right visualization app: are you adapting the library for use with an analytic app? For example, Sisense Analytics can be extended to integrate with any javascript visualization library using our JavaScript Add-On Framework.

Detailed documentation examples and code snippets are crucial for the quick and easy library usage. You can find both in the official Sisense marketplace and Community-Led Add-Ons. Sisense implements a wealth of expert domain knowledge in the field of data visualization to generate a ton of actionable insights on live streaming data.

Eitan Sofer is a seasoned Sisenser, having spent the last 13 years building and shaping our core analytics product, focusing on user experience and platform engineering. Today, he runs the Embedded Analytics product line which powers thousands of customers and businesses, making them insights-driven. Eitan is also an avid music fan and surfer.

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How to Operationalize Your Data Science with Model Ops

May 24, 2020 TIBCO Spotfire

Reading Time: 3 minutes

Just as you wouldn’t train athletes and not have them compete, the same can be said about data science & machine learning (ML). You wouldn’t spend all this time and money on creating ML models without putting them into production, would you? You need your models infused into the business so they can help make crucial decisions.

Model Operations, or Model Ops, is the answer. Model Ops is the process of operationalizing data science by getting data science models into production and then managing them. The four main steps in the Model Ops process — build, manage, deploy/integrate, and monitor — form a repeatable cycle that you can leverage to reuse your models as software artifacts. Model Ops (aka ML Ops) ensures that models continue to deliver value to the organization. They also provide critical insights for managing the potential risks of model-based decision making, even as underlying business and technical conditions change.

Model Ops is a cross-functional, collaborative, continuous process that focuses on managing machine learning models to make them reusable and highly available via a repeatable deployment process. What’s more, Model Ops encompasses various management aspects, such as model versioning, auditing, monitoring, and refreshing to ensure they are still delivering positive business value as conditions change.

Organizations need to realize the value of data science and machine learning models holistically, rather than as simply a process of developing models. While data science and ML processes are focused on building models, Model Ops focuses on operationalizing the entire data science pipeline within a business system. Model Ops requires orchestration and coordination of many different personas within an organization including data engineers, data scientists, business users, IT operations, and app developers.

In fact, many organizations have a dedicated Model Ops Engineer to facilitate this process. ML models that are people-facing must be unbiased, fair, and explainable; that is what the public demands and regulatory agencies and bodies increasingly require. For such applications, the ML Ops lifecycle must be designed to enable transparency and explainability when it comes to various risks.

The four-step approach to Model Ops

In order to solve common pain points when it comes to model operationalization — such as the long delay between initiating a data science project and deploying the model — companies are taking a four-step approach: build, manage, deploy/integrate, and monitor.

How to Operationalize Your Data Science with Model Ops

Build

Data scientists use languages like Python and R, as well as commercial applications, to create analytics pipelines. They use innovative ML algorithms, they build predictive models, and they engineer new features that better represent the business problem and boost the predictive power of the model. When building predictive models, data scientists need to consider both how the data is structured in production environments. Similarly, for feature engineering, data scientists need to make sure that any new features that are created can be created fast enough in real-time production environments.

Manage

Models have a life cycle that is best managed from a central repository where their provenance, versioning, approval, testing, deployment, and eventual replacement can be tracked. Besides the metadata associated with model artifacts, the management platform and the repository should track accuracy metrics as well as dependencies between models and data sets.

Deploy/Integrate

A data science pipeline is taken from its original development environment and expressed in a form that can be executed independently and integrated into business applications. In the end, you need to be able to deploy the pipeline in a format/language that is appropriate to the target runtime environment.

Monitor

After a model has been deployed, it is monitored for the accuracy of its predictions and impact on the business. The model needs to remain accurate even as the underlying data changes. This takes into account input from a human expert, or automatically, through ongoing retraining and champion-challenger loops with approval by a human, for example.

Realize the value of data science through Model Ops

By utilizing this four-step approach, organizations can realize the value of data science through Model Ops. It ensures that the best model gets embedded into a business system and that the model remains current. And companies that practice this have a big competitive advantage over those that consistently fail to operationalize models, and who fail to prioritize action over mere insight. Organizations can move beyond merely building models to truly operationalizing their data science usage.

Learn more about how to operationalize your data science by downloading this ebook.

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Data for Good in the Fight Against COVID-19

May 22, 2020 TIBCO Spotfire

Reading Time: 3 minutes

More than any other time in recent memory, we are seeing the private sector step up to make a difference. In response to the coronavirus pandemic, companies have taken action to retain their workforces, extend benefits, and support those who are quarantined. Some have donated money or protective equipment to frontline healthcare workers and local nonprofits. At TIBCO, our response includes all of these elements plus one more: leveraging our core competency in software and data to make predictive tools about the pandemic available to all who need them.

For 20 years, TIBCO has helped solve complex data challenges for companies and organizations globally. I’m a huge believer in the power of the #Data4Good movement, and TIBCO has a history of engaging in social issues through data-driven insight. Our data scientists have tackled problems as diverse as the San Francisco area’s housing crisis to how local authorities in South Africa can prevent the poaching of endangered species. COVID-19 presents our greatest challenge – and opportunity – to use data for good.

Here’s a screenshot of one of our dashboards. It’s part of our suite of data visualizations that provide valuable insights about the progress of COVID-19 to partners:

Data for Good in the Fight Against COVID 19

In March, we dedicated significant resources to gather, analyze, visualize, and publish comprehensive data tools tracking the outbreak and response. Early in the pandemic, our data team built a hub of visual and predictive analytics based on trusted, publicly available sources, along with local and county-level data. It is all available at TIBCO.com/covid19 – a data-fueled and thoughtful exploration of the coronavirus pandemic and what is being done about it.

Our goal is to understand the outbreak in real time at a community level, and assess the effects of non-pharmaceutical social interventions. Our tools offer guidance for resource planning, along with economic and supply chain considerations for healthcare facilities across the world. To be clear, we aren’t a team of epidemiologists. We are data scientists and technologists providing a service that helps empower public health officials through actionable insights and information. In the hands of governments, health institutions, researchers, and others on the frontline of the pandemic, these tools are helping to predict the effects of interventions and track viral recoveries in real time.

This data is critical in planning how and when we reopen our communities and restart our economy.

What’s inspiring to me is how much this exercise has galvanized our team. They’ve always been highly motivated and this event has built even stronger bonds – an important development, especially when we are all home working remotely.

I’m also inspired that we’re not alone. Many companies – big and small – are putting their core capabilities to work in service of pandemic response. In some cases, people, products, and innovation can be businesses’ greatest contribution. For example, Mercedes AMG helped develop breathing aids that were deployed to COVID-19 patients in the UK. Change Healthcare has developed an analytical tool that gives hospitals a centralized place to visualize a range of data types and to model various scenarios impacting clinical, financial, and operational aspects of healthcare organizations.

Providing our online dashboard tools at no cost was an easy decision for us. We invite partners to use our tools at TIBCO.com. We’re making them available to everyone online and are partnering more deeply with governments and institutions responding to this crisis. If you think these tools can help in new ways, let us know. This is a defining moment in our lives, and it is also a defining moment for businesses to step up and lead. I’m inspired by the steps so many have taken – and we’re ready to keep going, together.

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From Crude Estimates To Accurate Precision In The Oil Fields

May 17, 2020 TIBCO Spotfire

Reading Time: < 1 minute

Determining proper oil well placement and spacing is a difficult task, but getting it right is vital to effective production. More complex than one might think, well spacing depends on a number of factors—there is no one-distance-fits-all to determine the perfect grid pattern for oil fields.

Each reservoir has its own distinct properties that must be analyzed before drilling a well; drilling too closely to another reservoir could mean millions of dollars worth of oil is lost while leaving too much space in between is a missed opportunity for extraction.

That’s where Texas-based oilfield intelligence company OAG Analytics comes in. The firm has designed a solution that combines reservoir properties with machine learning capabilities in order to determine optimal well placement. With the help of data visualization technologies, OAG Analytics has helped well operators automate their spacing process. They are now able to determine spacing up to five times faster and at one-tenth of the cost of previous efforts.

OAG Analytics saw an opportunity when an abundance of evidence had shown that producers that were open to using technology in the field were more successful than those who had yet to embrace technology to its fullest potential. While industry standards to estimate production already existed, OAG Analytics put machine learning algorithms behind those estimates to automate and vastly accelerate the process. The solution is now being used by every major unconventional basin in the United States.

For more details on how OAG Analytics helps well operators save billions of dollars with its data visualization solutions, read the full case study.

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How SIBS Created New Customer Value With Open Banking

May 15, 2020 TIBCO Spotfire

Reading Time: 2 minutes

In heavily-regulated industries, regulations can often be a barrier to new business. But SIBS, a leader in payments processing and financial technology services in Europe, saw increasing regulations as a major opportunity and used that as a starting point to increase its revenue streams.

SIBS provides financial, modern, reliable and secure services to more than 300 million users from different geographies, processing more than three billion transactions annually. That said, it needed the right technology to fuel its forward-thinking strategy to create a differentiated customer experience. Thus, SIBS leveraged Open Banking to delight customers and prepare for future requirements.

The company built a revolutionary API-led platform, the SIBS API Market platform, from scratch using TIBCO, changing the way two million banking customers and over two-dozen banks in Portugal do business. Additionally, it became a marketplace for all Portuguese banks and customers.

This innovation led to numerous positive business results; about 95% of all bank accounts in Portugal are made available to trusted third parties, 24 banks are consuming APIs from the open-banking platform, and nine million daily transactions are supported.

Additional benefits include:

Creating an Open Banking marketplace for the Portuguese economy where users can find new services and banks can share their apps
Staying ahead of regulations to lead the market by leveraging data with the help of a flexible, interconnected technology stack

SIBS API Market contributes to continuous technological advancement in the digital economy and towards a more cashless society. According to a recent study conducted by the European Banking Association, SIBS API Market is one of the top three most complete and comprehensive platforms, among more than 300 available. Moreover, in Portugal, all the members of the SIBS API Market were exempted by the Central Bank because they implemented a contingency mechanism that was compliant with the European requirements for safe communication with third-party providers.

Innovative companies, like SIBS, can stay ahead of new regulations to lead the market and provide new value to customers. This resulted in using cutting-edge, API-led technology to build the open-banking platform of the future, including an innovative open marketplace where banks and consumers can find and share new types of financial services.

To learn more about how SIBS created a best-of-breed solution to create a richer customer experience, see the full success story.

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Viva Las Vegas: Ten Sure Technology Bets for 2020–Part 3

May 13, 2020 TIBCO Spotfire

Reading Time: 3 minutes

In part one of this blog series I contrasted technology predictions (less than sure projections of the future) so prevalent at the start of every year with sure technology bets (those things you can be 100% sure will happen). The goal of this distinction was to help you understand how to improve the payouts from your technology bets.

In part two, I provided my list of 10 sure bet technology trends you can count on to continue in 2020. With data the common denominator across all of them, I concluded that investing in your data was certainly your wisest bet.

Continuing the Las Vegas theme, in this blog I identify an unbeatable hand that you can use to increase your odds of winning at the data game. Spanning people, processes, technology and architecture, these combine to guide your data management efforts so you can tap the power of data and deliver the business transformations you need to thrive in 2020 and beyond.

Winning with Four Aces

You don’t need to be a Las Vegas sharpie to know that four aces is nearly always a winning hand in five card stud poker. The problem is the odds of getting four aces are pretty slim. In fact you have just a 1 in 54,145 chance when dealt five cards randomly.

But unlike poker, when it comes to the 10 data opportunities listed in part two, your hand does not have to be the random result of five cards slid across the table by your dealer.

Instead you can deal yourself these four aces.

Your First Ace: Adaptive Data Architecture

As your data grows, becomes more real-time, distributes more widely, and shifts toward the cloud, you may end up with an accidental data architecture that constrains rather than enables your business objectives.

Your winning hand includes an adaptive data architecture ace that:

Embraces today’s distributed data topology rather than trying to apply traditional data centralization paradigms. Logical Data Warehouse, Data Fabric, Data-as-a-Service, and Data Layer are typical labels.
Leverages what you already have, avoiding rip and replace. Have a data warehouse for your historical analysis? Keep it. Adding a data lake for your data scientists? Include it. Migrating data from on-premises to the cloud? Redirect it.
Decouples how you manage data from how you consume it. Your business users no longer need to know IT internals such as schemas and syntax. And your IT team is free to manage each data type optimally, independent from how they choose to provision it to the business.
Starts small so you can quickly deliver value. Then grows when you are ready to take on broader efforts such as standardizing your enterprise data model or scaling to support larger user counts and stricter SLAs.

Your Second Ace: Agile Methods

Data fuels customer intimacy, operational excellence, and business reinvention. And in today’s data-driven world, victory goes to the swift.

Your winning hand includes an agile methods ace that:

Leverages data virtualization so you can quickly configure new datasets in hours or days, avoiding the complex software development lifecycle (SDLC) and long time-to-solution associated with ETL and data warehousing.
Allows you to focus on your highest-return data management activities instead of boiling the ocean. For example, controlling master or reference data for a few key domains, then circling back later for less critical domains.
Supports both “Mode 1” industrial IT projects and “Mode 2” experiments so you can deliver the best of both worlds.

Two More Aces and a Wild Card to Complete Your Winning Hand

In my final blog of this series, I will add two more aces and a wild card to your hand. So get your 2020 wagers ready.

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Data Virtualization Needs to be Part of your Data Integration Toolbox

May 10, 2020 TIBCO Spotfire

Reading Time: 3 minutes

Any craftsman worth their salt has a deep toolbox, knowing that the right specialty tool at hand makes all the difference when completing a high-quality job in a timely manner.

The same can be said for IT, and especially data engineers, responsible for providing data to business consumers. To perform their work, quickly and well, they need to have all the right tools in their data integration toolbox.

Right Tool, Right Job

A broad toolbox with the right combination of data integration capabilities can be extremely valuable when responding rapidly to data requests. It can mean the difference between frustrating delays or punctual satisfaction and better business results.

But there are a variety of data integration tools available today. And it is important to select the right tool if you want to get the job done successfully. To help data engineering leaders ensure their team’s toolbox has everything they need, there are 7 recognized styles for data delivery:

● Bulk/batch (ETL, ELT)

● Replication

● Messaging

● Data services orchestration

● Synchronization

● Data virtualization

● Streaming data integration

Contrary to what some may believe, it’s not about selecting just one of the above tools and assuming it will solve your data integration needs. Many companies will need a combination of data delivery styles to find success—a data integration toolbox that meets their various business goals. According to Gartner, “By 2021, more than 80 percent of organizations will use more than one data delivery style to execute their data integration use cases.”(1)

Why Include Data Virtualization in Your Toolbox

Of all those different data delivery styles, data virtualization is one of the most crucial ones and should be included in your toolbox. Data virtualization is a very useful tool that augments the existing data integration tools that you may already have. It provides a pragmatic integration approach that organizations are increasingly adopting to use along with their favorite ETL/ELT and replication tools.

In fact, according to Gartner’s 2018 Market Guide for Data Virtualization, “Through 2022, 60 percent of all organizations will implement data virtualization as one key delivery style in their data integration architecture…In 2011, only 11% of surveyed organizations reported that they were utilizing data virtualization in a focused set of use cases.” (2)

Cloud Data Sharing is Driving Data Virtualization Demand

But let’s face it, most people try to get by with as few tools as possible. So even when they know about power saws, they won’t buy one until they are faced with a “big job” that requires hundreds of cuts. Cloud data sharing is proving to be just that kind of “big job.”

Why is that? Well, while data and workloads are rapidly moving to the cloud, many legacy systems remain on-premises. This creates a number of new cloud data integration patterns including:

● Hybrid: Integrating on-premises and cloud data

● Multi-instance: Integrating multiple data sources within a single cloud provider

● Multi-cloud: Integrating data across multiple cloud providers

● All of the above: Integrating on-premises, multi-instance, and multi-cloud

With this “big job” of cloud data sharing, comes a need for the right tool to get the job done. Data engineering teams have found that data virtualization is the right integration tool across all these cloud integration patterns. Regardless of the on-premises and cloud topology, data virtualization provides complete, consistent, business-friendly data wherever it happens to live, without moving or replicating it. And even better, its virtualized, metadata-driven data views adapt quickly as your cloud topology changes.

To learn more about how data virtualization can help complete your data integration toolbox and improve your cloud data sharing, check out these educational data virtualization resources we’ve curated for you.

And stay tuned for my next blog on who can most benefit from data virtualization and how to get started!

(1) Gartner, Magic Quadrant for Data Integration Tools, Ehtisham Zaidi, Eric Thoo, Nick Heudecker, 1 August 2019
(2) Gartner Market Guide for Data Virtualization, Sharat Menon, Mark Beyer, Ehtisham Zaidi, Ankush Jain, November 16, 2018

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Apache Kafka®: A New Approach for Providing Digital Communication

May 8, 2020 TIBCO Spotfire

Reading Time: 2 minutes

The world is changing fast and it seems like no one can keep up. But, the one aspect that has remained constant is the need for communication. Application communication and integration are critical capabilities when it comes to rapid business change and adaptation. However, effective communication can be a challenge, especially in the digital world. That’s why we welcome new technologies like Apache Kafka that can help you create a truly digital enterprise that can quickly respond to business changes with a tightly coupled communication infrastructure.

Apache Kafka: A Newcomer to Messaging Communications

One of the newest players in the field of messaging communications technology, Apache Kafka, was developed by LinkedIn in 2010 and later donated to the Apache Software Foundation. Kafka was originally designed to provide a distributed approach to streaming logs for data processing. The software provides a common framework for streaming real-time data feeds with a focus on high-throughput and distributed workloads.

Kafka has become the de-facto standard for open-source streaming of data for stream processing. Since its launch, Kafka has been rapidly adopted by businesses to build a digital nervous system for stream processing and event-driven architectures. The platform provides scalable, high-volume, persistent data distribution in real-time. Kafka’s new open-source approach allows users to deploy messaging communication technology quickly and simply. By leveraging free, open-source software, developers can stand up Apache Kafka nodes and stream data for numerous application types in minutes.

Do More with Kafka & TIBCO Messaging

Scaling application layer deployments to enterprise-level volumes and integration points can be a challenge. With the support of TIBCO^® Messaging, your enterprise can implement a wide-scale adoption of Apache Kafka. This combination gives your developers, managers, and enterprise architects the core functionality of Kafka with the support of TIBCO.

Users can now fully integrate Kafka into their enterprise nervous system. TIBCO’s support for Kafka allows organizations to expand their existing usage of the system for added integration points, simpler stream processing, and native support for streaming analytics. Together, TIBCO and Kafka facilitate a new approach for better integration and enterprise messaging.

Get started with Apache Kafka with this whitepaper to learn how you can adopt the platform into your enterprise nervous system for integration services, data analytics, and data distribution for stream processing.

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Talking Blockchain in Healthcare with TIBCO CTO Nelson Petracek

May 6, 2020 TIBCO Spotfire

Reading Time: 3 minutes

This article first ran in The Australian on March 30, 2020, and has been republished to the TIBCO blog with permission from the original source.
Author: David Swan, Technology Editor, The Australian

Blockchain has the ability to help the healthcare industry automate and cut down its processes, which can help it in turn save costs that can be funnelled back into important patient care, according to one of the top executives from US cloud computing specialists TIBCO.

TIBCO chief technology officer Nelson Petracek told The Australian that despite a decline in search traffic for blockchain across the board, now was the time for the technology to prove itself in important ways, like in the healthcare sector.

As reported in The Australian today, blockchain is the top ‘hard skill’ when it comes to current technology job opportunities. Blockchain is a distributed, decentralised, public ledger; a system in which a record of transactions made in bitcoin or another cryptocurrency are maintained across several computers that are linked in a peer-to-peer network.

Mr Petracek said that despite an overall downturn the ACT had spiked in searches for blockchain, which was likely driven by government interest in the technology following the publication of Australia’s National Blockchain Roadmap.

“The fact that this was done by the government likely also triggered some interest among blockchain-related technology companies to see what business opportunities the roadmap might present to industry,” he said, adding that the finance industry was also showing heavy interest given it’s under increased pressure to reduce fraudulent activity and restrict money laundering activities.

“Blockchain is often touted as a potential solution to these business problems, and given that many organisations in the Asia Pacific region have at least conducted initial POCs with the technology, the combination of government attention, public facing banking issues, and some experience with the technology has led to renewed interest beyond the initial hype,” he said.

According to Mr Petracek, the healthcare industry is also experiencing increased attention when it comes to the cost of healthcare, even before the COVID-19 crisis.

“There is pressure on the cost of healthcare as well as the management and governance of personal information,” he said.

“We have, of course, the health pandemic of today, which is severely stressing global healthcare systems, but even before this situation became a crisis, the need to provide better healthcare at a lower cost was receiving more attention.

“Again, given the applicability of blockchain in this area (as a potential partial solution to certain types of problems), and the intersection with government involvement and technology experiences, blockchain has received renewed interest.”

“These areas are driving more interest in blockchain technology, design and application of blockchain as governments – and not just early adopters – need to clearly understand the technology and its impacts on legislation and regulation.”

Mr Petracek said Australia has taken a leadership position with its blockchain roadmap, which lays out ideas about industry and educational institutions might collaborate with government when it comes to healthcare.

The roadmap was released last month and focuses on three main areas: “regulation and standards;” “skills, capability and innovation;” and “international investment and collaboration.”

“I think it will be looked at as an example of what other countries could do,” Mr Petracek said. “Especially with Australia being in the middle of Asia Pacific, I think you have a fair amount of activity and investment in places like China for example.

“That means Australia is going to have to be a bit more on the cutting edge in terms of understanding the technology and where it could be applied, but there is an opportunity to be a leader in this space and help define how it can work.

“There is a real opportunity for Australia to lead.”

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How to Build an Outcomes-Focused Data Strategy

May 5, 2020 Sisense

Money never sleeps and neither does your data. In Monetizing Your Data, we look at digital transformation: the ways of turning data into new revenue streams and apps that boost income, increase stickiness, and help your company thrive in the world of Big Data.

The first waves of digital transformation started decades ago and the ripples of this trend continue to be felt to this day. However, what exactly a digital transformation looks like varies widely from company to company. One common theme among many transformations, however, is trying to make better use of data, whether to build analytic apps to unlock new revenue streams or to make smarter decisions internally (or both).

While these are worthwhile applications, one blind spot that many teams charged with these projects share is that they look at the data they have on-hand before figuring out what kind of problems they wish to solve with it.

“I recommend starting your data strategy with a right-to-left approach, focusing on the desired business outcomes first, instead of the data, to support those outcomes,” says Charles Holive, Sisense Managing Director of Data Monetization and Strategy Consulting. “And there are primarily three areas that industries across the world look to improve: the top line, the bottom line, and customer satisfaction.”

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Define your desired outcome — before you start building

Every company knows they need to digitally transform in order to survive and excel in the modern era. However, many organizations fail to define their goals for this process before they start, and predictably encounter obstacles or outright failures instead of paving a path for future success.

Business goals should be defined at the very beginning of the digital transformation in the “right-to-left strategy” that starts by answering this question: What is the organization specifically looking to solve or improve? Understanding the details is key, otherwise “digital transformation” will be merely a corporate buzzword that causes headaches, heartbreaks, and lost money instead of producing measurable improvements.

From there, rather than trying to accumulate and house the company’s entire dataset, the digital transformation team should identify the specific actionable insights and associated data needed to solve for (and measure) agreed-upon outcomes.

“Not every dataset is made equal; some are more valuable than the others. So being outcome-focused is a way that can you stack-rank the data sets that are most important. Your team can then begin moving that most-important data into your warehouse.”

Experiment to guide a winning data strategy

Just as the waterfall method of software development—the strategy of gathering all the requirements upfront and then building and releasing a complete application—has fallen out of favor for agile methods, the same thing should happen when creating an outcome-first data strategy: Rather than trying to build a complete data warehouse right from the outset, approach data strategy as an “innovation factory.”

“Identifying the exact data you need to solve a singular problem results in a perfect candidate to go into your warehouse on the first cycle. This is because you know exactly what the business is going to do with that data set,” Charles explains. “It’s powerful because it’s already informing or measuring a specific business outcome.”

And when this data is warehoused and accessible to business partners to make key decisions, you already have a chance to quickly prove this outcome-first data strategy. You’ve immediately created an experiment to win.

Another piece of advice that Charles talks about in his “Hacking the Analytic Apps Economy” video series is where the innovation factory should live. Namely, not in a mature business unit, but in an agile, fast-reacting department that reports to a Chief Innovation Officer or similar highly-placed exec. This team can deliver on new ideas quickly and won’t get bogged down in pre-existing frameworks or goals that don’t work for what the new data experiments are trying to achieve.

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Create an innovation factory at your company

“Creating an innovation factory for your company results in faster innovation. You can do these smaller experiments more cost-efficiently, saving money over the traditional data strategy. This also should help your team prioritize projects for the data warehouse that deliver the greatest value, as opposed to the department that screams the loudest.”

And while any experiment can fail, but here are some solid tips to help improve your likelihood of success and to maximize the impact of triumphant experiments:

Start by listening to the frontline employees who use the data to make decisions—this will improve the odds of success for your experiment out of the gate.
If your experiment works, find other departments that can benefit from that same data—this is where it is key to have a good semantic layer on top of your data warehouse (courtesy of your data analytics software) so you can repurpose the same dataset for different ends.
If your experiment fails, see if you can tweak the dataset or use case to apply elsewhere in the company.

Regardless, approaching data strategy with a focus on business outcomes will put you on the right course.

“Everything else in the company is business-centered. It just seems counterintuitive not to approach data strategy in the same way.”

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Jack Cieslak is a 10-year veteran of the tech world. He’s written for Amazon, CB Insights, and others, on topics ranging from ecommerce and VC investments to crazy product launches and top-secret startup projects.

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