Watch this video to learn why the embedded analytics marketplace is experiencing explosive growth.
Without effective analytics, companies cannot compete. But not just any analytics will do. Users want analytics in the applications they use every day. They want to use data that may reside in multiple locations. And this is across virtually every line of business (LOB).
Moreover, the embedded analytics market touches several stakeholders in addition to LOB users. Independent software vendors (ISVs) are eager to tap the growing interest in embedded analytics. Right now at least, it looks like very low hanging fruit. And enterprise IT needs to come to terms with the age old tech question: buy or build?
From this video you’ll learn what embedded analytics offer that standalone solutions don’t.
Marketers are big proponents of embedded analytics. But it’s not just about embedding one technology in another for convenience. They want to “raise the analytic bar” in their organizations. When analytics are integrated in everyday processes and workflows, it builds a stronger analytical culture.
Delivering analytics within a job role or function-specific tool also helps companies make more efficient use of their data. Moreover, when users engage with data on a self-service basis via embedded analytics, the tools they use show a high rate of adoption. And we’re not just talking about simple dashboards. Companies are embedding tools with a broad range of capabilities.
This video reveals what various stakeholders have to gain from embedding analytics.
Different types of companies have different motivations for embedding analytics in their applications. Independent software vendors embed analytics to gain competitive advantage for their offerings. Whatever the core purpose of their software, analytics can improve its functionality and value for potential customers.
On the other hand, enterprises often use embedded analytics in internal and customer-facing applications to find and act on opportunities for cross-sell and upsell. A customer-facing portal can provide value for customers, but it also gives visibility into their behavior for the enterprise delivering that application. It’s a data mining tool that can pay for itself many times over.
Watch this video to find out the pros and cons of building versus buying embedded analytics.
When companies decide they want to embed analytics in an application, they almost always struggle with the build versus buy dilemma. Build often turns out to be a short-term solution. Over the long haul, partnering with a third-party whose specialty is embedded analytics turns out to be the smarter play.
There are a variety of reasons for this. First, is time to market. It’s faster to buy. Second, is total cost of ownership. Building the embedded analytics application is only part of the equation. It has to be maintained and updated as well. And, typically, those that buy embed a wider range of capabilities because they’re collaborating with a specialist that has a large embedded toolset.
Watch this video to dive deeper into embedded analytics use cases for the healthcare and manufacturing industries.
Many application providers and users can show strong results from the use of embedded analytics across various functional areas. But the embedded story also includes industries. Two of the many that have found the potential of embedded analytics promising are healthcare and manufacturing.
From bringing actionable data closer to the point of patient interaction in healthcare to using more kinds of data for predictive and search-based analytics in manufacturing, analytics baked into everyday tools produce a higher level of satisfaction than standalone applications. And research from both industries points to improved decision support -- faster and more efficient -- as the reason.
In this video, you’ll learn that analytics are embedded in virtually every consumer-facing web application and in many enterprise applications such as CRM, ERP, and finance.
On the corporate side, companies generally embed analytics to increase revenue or deliver a better customer experience. Whether consumer or enterprise, application users have come to expect some type of analytics and often it’s the part of the application they interact with most.
In both scenarios, developers must decide whether to build or buy the embedded analytics platform. In addition, they have choices about how deeply to embed analytics in the parent application.
Watch this video to learn how embedding analytics can be simple or complex depending on the use case.
Simple white labeling is really just rebranding one company’s product to make it appear as its own. White labeling traces its roots all the way back to promotional practices used by record companies -- vinyl records.
But embedding can also encompass deep integrations that include customized visualizations and connectors to customer-specific, proprietary data sources. In between those two extremes are other options, including lightweight integration with iFrames and API-based embedding that requires use of an SDK.
This video will give you an idea of what features you should look for when choosing an embedded analytics technology.
First, of course, make sure it’s designed to be embedded. A lot of BI vendors out there peddle legacy technologies that have been retooled for embedding analytics -- with very mixed results.
Some other important capabilities include speed. Your embedded analytics technology should be fast. Users don’t want to watch a spinning hourglass while they wait minutes for query results. It should also natively accommodate modern data sources and make it easy to add new sources. When your embedded technology has these and a few other essentials, it can keep up with the analytical demands of big data.
In this video, you’ll learn the pros and cons of building your embedded analytics solution.
Although building your own embedded analytics solution may look like a smart play, it’s usually not. Even if you have analytics expertise in house, which is often not the case. Sure, you’ll get something that meets your precise requirements. But then you have to devote lots of resources to maintaining it — forever. Total cost of ownership (TOC) includes a lot more than the initial build.
Buying embedded analytics from a domain expert can slash time to market and TOC. Unless you want to invest in developing and maintaining in-house embedding expertise, buying deserves serious consideration.
If you’re a CTO, product manager, or software engineering leader, you may be curious about whether to build or buy an integrated analytics solution, how to evaluate the available offerings for embedded analytics, and how to most efficiently embed data visualization and analytics into your product. Join us in this webcast as we discuss these points and more. To frame our discussion in a real-world context, we’ll hear how robotic process automation company Automation Anywhere worked through these issues.
In this video, customer success lead engineer Jonathan Avila introduces his class on embedding visualizations in applications and dashboards.
Jonathan’s an expert when it comes to embedding custom charts into third party applications. There are definitely best practices when it comes to embedding and Jonathan will share what he has learned.
Watch this video to learn how important it is to keep things simple when embedding visualizations.
With hundreds of thousands of fields to choose from for any chart, it’s a good idea to use filters to give users fewer options. For example, certain visualizations like a typical sales chart simply don’t need fields like latitude and longitude. You want enough options to make the chart useful and flexible but not enough to overwhelm the user. And a common pitfall with many charts is giving users the ability to select a combination of fields that return no data.
Check out this video to see how to create queries for dropdowns in your charts.
Good examples of this function would be query options for payment type, country, and city. It’s also important when changing dropdown query options that you update the filters for the main query that’s driving the visualization.
Watch this video to learn how to apply styles to your charts or dashboards.
You want to make sure that visualization styles match the visual style of the application or dashboard. Color palette is another important consideration. You also want to make sure that users cannot change the chart type. The code editor is where you can implement a lot of styling changes via Cascading Style Sheets (CSS). The video also covers use of the container component.
This video shows Jonathan making a change to a dashboard.
Modest changes can make a big difference in the appearance of a chart. Background colors are important. Button and button label styles also contribute a lot to the overall look of a visualization. These can be controlled through the specific class of CSS.
Watch this video to learn how to use colors and tool-tips to provide additional context to your charts.
Sometimes, combining two charts into one is the best way to display two metrics that might each occupy its own chart. For example, color can display one metric; size can illustrate the other. A sales by city bar chart works as a common example, using sales and percent change as the metrics. The video also shows how to create a tooltip and an items array.
In this video, you’ll learn how D3.JS compares with other charting frameworks.
One of the most common visualization frameworks is D3.JS. It has an extensive API and is very powerful. But it has a pretty steep learning curve. Other frameworks like Highcharts or E Charts are easier to use although they don’t offer the range of options that D3 does. For example, side-by-side comparisons using the same chart illustrate the pros and cons of D3.JS and E Charts at the code level, including the ability to add labels to a chart.
In this video, you’ll learn why businesses of all sizes are investing so heavily in big data.
Big data and big data analytics are big business. IDC projects the market to grow to $50 billion by 2019. So what do organizations that invest in big data expect to achieve. Is there still a role for intuition in decision-making? Essentially, businesses pursue three objectives with big data: understanding the past, improving the present, and predicting the future. We’re also increasingly surrounded by data-driven smart systems that are reshaping the way we work and the economy we work in.
Watch this video to find out where most organizations are right now in terms of data monetization maturity -- and how to move past that.
A five-stage model describes the path to data monetization maturity. But did you know that although many organizations have invested heavily in data and analytics since the 1990s, most have not moved beyond the first stage of the model -- distributing analytics internally? The next four stages chart the development of analytics from a cost center to a profit center. And each stage has its own requirements.
In this video, we explore the best way to data about your products and services into a valuable product on its own.
As Edd Wilder-James once said, “Data products are the reason data scientists are lately treated like rock stars.” Data products operationalize analytical insight. And that’s what monetizing data is all about. The automobile is a good example for the potential of monetizing data. It illustrates that data about a product can be just as valuable -- if not more valuable -- than the product itself for generating revenue and increasing customer loyalty.
Watch this video to learn the uses and limits of customizing embedded analytics through white labeling.
When a third-party software application is integral to the way a business delivers products or services, many organizations want that software to look like its home grown. White labeling is a way to do that relatively simply; and it’s sufficient in many situations. A lot of cosmetic fine tuning can be done with logos, color palettes, fonts, icons, and background images. In combination, these changes can make an embedded analytics blend well with its parent application.
In this video, you’ll find out how iFrames extends the customization of embedded analytics beyond what’s possible with white labeling.
There are a lot of cases when white labeling isn't sufficient,especially when you want to have data analytics alongside other functionality. A good example would be a customer portal where you have several columns with different widgets. You might have a news feed, a weather map, and other features plus analytics tools.
One way to do this would be embed an analytics web application into the portal via an iFrame. Of course, like white labeling, iFrames have limits.
This video goes beyond customization via white labeling and iFrames to the use of a software development kit (SDK).
Using iFrames and white labeling for customization, there's always a tradeoff between security, interconnectivity, and the integrity of the user experience. You have more control and more connectivity with an SDK. You can build a truly custom application without starting from scratch. A robust SDK should include the ability to embed out-of-the-box charts and new charts as well as their accompanying data and metadata. It should also allow you to embed just data that can feed pre-built visualizations and, very important, integrate using REST APIs.
Watch this video to learn how using REST APIs alongside of an SDK can multiply the power of both.
An API is to software code what a UI is to users. It helps different bits of software interact with each other. And can help you turn a data application into a data platform. If you have access to them, you can do a lot with REST APIs that weren’t built into a particular SDK. For example, you could accept user inputs and pass them to the platform to be stored. Or acquire data and metadata from the application or from users. This can be very powerful when building portals.
This video explores how applications connect to data sources and what that means to an embedded application.
Database query languages like Structured Query Language (SQL) and the Open Database Connectivity Protocol (ODBC) have been around a long time. SQL since the early 1970s and ODBC since 1986. And for as long as people have been querying data, reducing the length of time it took to get answers back -- query latency -- has been a problem. As databases have changed and new types emerged, solving the problem has become even more complex. Custom data source connectors are a solution.
Watch this video to learn why there’s a lot more to embedding applications than what you see -- the charts, graphs, and dashboards.
Of course, visualizations like charts or dashboards are part of it. But for a platform to be embeddable, it needs to align with the parent application’s architecture and development technology. It needs to be compatible with that application’s infrastructure. And, it must integrate with its security model. In addition, SaaS or cloud-native companies also put special demands on the embedding platform, including the need to support automated user provisioning. Supporting multi-tenancy also means isolating metadata and providing row-level security.
In this video, you’ll find out about the three “A”s of security and get an overview of the first “A” -- authentication.
Authentication confirms that users are who they say they are. But in embedding, the embedded application has to support the authentication standard of the parent application. And there are multiple standards. The most common is form-based, which prompts the user for a username and password. So users don’t have to login more than once, embedding platforms should also support single sign-on, for which there are also multiple standards including SAML, Kerberos, and the X.509 client certificate.
In this video, we cover the second “A”: authorization, which refers to defining and enforcing privileges and permissions for a user.
There are two common methods for authorizing users: role-based access control (RBAC) and the access control list (ACL). In the first, a user is defined as a member of group -- say finance administration -- and the group as a whole is assigned permissions. Another group in finance -- finance accounts payable -- could be assigned a different level of permissions. ACLs provide a finer-grained level of control. For embedding purposes, users, groups, and roles should be defined by the parent application.
Watch this video to learn how the two models of SaaS multi-tenancy affect the deployment of embedded platforms.
Multi-tenancy models typically fall within one of two approaches: isolated and shared. The isolated model is arguably safer, but the shared model is more efficient for the SaaS company. An embedded analytics platform needs to support both. Moreover, to support the co-mingled or shared model, the embedded platform needs to support row-level security. Row-level security ensures that tenants only see the data relevant to that customer. Row-level security can be implemented via the embedded platform or it can be enforced by the data source.
This video explains the importance of auditing to the security environment for embedded applications.
Especially in highly regulated industries like healthcare and financial services, it’s not enough to correctly configure authentication and authorization. You have to be able to prove it that you’ve done it. External regulators and internal auditors will want to review a record -- an audit trail -- of user activities within the application. To provide that, the parent application logs user activities. The embedded application should support centralized logging so it can use the parent application’s logging environment.
Watch this video to learn how administration and automation via REST APIs makes life easier when a SaaS company or large enterprise is embedding a BI platform for use by tens or hundreds of thousands of users.
In that scenario, you want to look for ways to automate the provisioning of new users and groups. So, it's important for a BI platform to offer administrative APIs that can be scripted from your application. These are usually REST APIs, and they really help ease the administrative load when users want to sign up for the embedded service from the parent application.
This video recaps why embedded applications are like icebergs -- a lot happens under the surface.
Visualizations are what we see from an embedded application. That’s the part of the iceberg that’s above water. But under the hood, you have to make sure that the embedded BI platform will work with the parent application's platform and its development environment. You have to be able to deploy it on the same kind of infrastructure. And it has to work with the parent application’s security model.
Can you imagine easily incorporating visual analytics into any app? Zoomdata makes it a reality.
By embedding BI directly into the software, it gives users a visual roadmap to uncover insights - data is presented in the right place for the right people to find, with the right visual cues. But it is a challenge to deliver this with more and more data and a wider variety of modern technologies to store and process new data types.
When ClickFox wanted to launch its Journey Watch solution on the Hadoop data stack, it embedded Zoomdata for data visualization. With Zoomdata, ClickFox customers can:
See unique journey metrics in interactive dashboards
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Turn insight into action that boosts the bottom line.
Although most organizations already provide internal business users with Bl tools to improve decision making, many now embed analytics into core business applications to broaden the reach and improve the timeliness of insights.
Organizations are eager to monetize their data assets by injecting outward-facing applications with reports, dashboards, and self-service analytics.
Written by Wayne Eckerson from Eckerson Group, this eBook provides a rationale and framework for monetizing data and explains the key success factors for developing the data-driven applications that are the backbone of any data monetization strategy. The eBook is geared to business and technical executives who want a concise guide to harnessing data for business gain.
White label business analytics as a visually consistent part of your application or business process. Zoomdata empowers technology and software vendors with analytics on big data and real-time streaming data, while enabling re-branding of Zoomdata to create a visually seamless user experience.
Embed visualizations and business analytics with ease. Zoomdata empowers technology and software vendors with embedded analytics on big data and real-time streaming data, as well as traditional data sources.
Easily embed analytics in your application with Zoomdata. You have the domain expertise in your application, we bring the modern analytics best practices. Deliver appealing analytics to the users of your application. Users simply love it. Finally see your investments in providing analytics within your application pay off.
Raise the analytics bar in your organization by implementing embedded analytics. Learn why marketers and users alike love embedded BI.