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Tag: Embedded analytics

  • How Embedded Analytics Can Transform Future Businesses

    How Embedded Analytics Can Transform Future Businesses

    It’s no secret that big tech companies like Google, Amazon and Netflix are not only leveraging data but also delivering embedded analytics and insights as a standard functionality of their software, services, applications and toolkits. The analytics appear to consumers as recommendations, usage habits and other helpful information. Such insights not only help consumers extract the most value out of the product they are using but also help providers understand how they can provide better services. Businesses that embed analytics to provide a differentiated user experience also have a direct impact on engagement and revenue.

    Have you considered how embedded analytics could differentiate your company’s own software and hardware products? Let’s understand how embedded analytics can help transform technology businesses:

    1. Analytics are expected and welcomed in your products

    By embedding analytics into existing enterprise software and applications, users can drill down into real-time and context-aware data (for example: How busy a location will be at various times of the day) and can benefit from faster data-driven decision-making without worrying about application compatibility. Commonly, software applications provide in-app analytics to delight and serve their customers, while hardware companies can provide helpful stats on a web portal or app.

    2. Embedded analytics is easier than ever today

    Cloud technology has completely disrupted tech-enabled industries. It’s easier than ever for software, apps and hardware to connect to a data repository to save logs that are a rich source of analytics. The invention of the S3 bucket and object store has made it fairly cheap to keep log data. Finally, the transition to platform-as-a-service (PaaS) and software-as-a-service (SaaS) has fundamentally added an “easy button” to tracking consumption patterns and resource utilization.

    3. Embedded analytics helps design better products and services

    Embedded analytics can give businesses more insight into application telemetry (how customers use the data, how they engage with the product) and provide insights into the challenges they face from a usability standpoint. Providers can use advanced analytics to quickly iterate, draw patterns and make predictions, troubleshoot customer challenges, discover new avenues to monetize, detect and mitigate security threats or create entirely new features and applications that put customers on a faster path to improved application experience and better decision-making.

    4. Embedded analytics increases operational efficiencies

    The tech industry is transitioning from traditional monolithic architectures where applications are self-contained to microservices architectures where different components are spun up as needed via API calls. By embedding analytics directly into developer tools, businesses can manage resources without sacrificing availability or performance. Companies can allocate and optimize resources dynamically and understand peaks and valleys in consumption. Businesses must be able to leverage historical and real-time data to build predictive models to proactively identify and mitigate issues with individual components of the application before the customer experiences a problem.

    Embedding Analytics in Your Application

    Before you start embedding analytics in your application, it’s important to choose the right database foundation, as this will support current needs and future goals. Below are the top capabilities that should be standard features in analytics databases:

    1. Handles massive data volumes: The database must scale easily and efficiently with minimal cost using massive parallel processing. Today’s scale might be terabytes, tomorrow it might be petabytes and so on.

    2. Enables speedy data analytics: Speed is a key factor in a positive user experience. Users want instant insights because they don’t like waiting for results. The database should be capable of optimizing load and query performance so that analytics is both timely and relevant.

    3. Embeds machine learning: Machine learning algorithms are critical for predictive analytics. A platform that can embed ML and iteratively learn from new data can make a big difference and add a whole new level of intelligence.

    4. Features native integrations: The more time developers spend figuring out tools and integrations, the less time is spent innovating. The platform must ideally have out-of-the-box integrations with popular applications, data sources (on-premises, cloud, multi-cloud or hybrid), ETL, and visualization tools (e.g., Microstrategy, Looker, Power BI, Qlik, Tableau). It should be flexible enough to support user-defined functions for things that don’t have native integration.

    5. Encourages self-service: The database should make it easy for data scientists to leverage tools and programming languages they already know such as R, Java, C/C++, Python and SQL. Data scientists will also appreciate a platform that has built-in functions for a wide range of analytics (such as geospatial, time-series, event-series, real-time, machine learning, text analytics, pattern matching and regression) and delivers the tools to spot trends, uncover anomalies and anticipate the unexpected.

    6. Supports highly advanced analytics: Many platforms can analyze single tables or do a simple look-up but few have the power to analyze dozens of tables, hundreds of data types, dimensions and attributes from numerous sources over several years of history.

    7. Unified: The database should be able to streamline everything right from data ingestion to exploration to production until delivery. Users should be able to analyze data in its place to always have a single source of truth.

    It’s not hard to visualize that the most disruptive companies of the future will be the ones that harness data as a core asset and embed analytics in their products and services. Embedding analytics requires the right database foundation; to get to the right foundation, IT teams need to cut through the noise and study the fine print. With the proper database, the future is yours to create.

  • Why an Embedded Analytics Maturity Framework is Essential

    Why an Embedded Analytics Maturity Framework is Essential

    Contextual analytics, or embedding analytics directly into a business application’s transactional environment and core workflows, is the most sophisticated stage of business intelligence (BI) currently available to organizations today. It is one level ahead of the traditional integration of analytics, which is a standalone capability presented as a separate dashboard or reporting module.

    Today, there is an increasing shift among organizations toward embedding transactional analytics into their enterprise applications. Because contextual analytics is the most advanced option in the market, there is also an increasing need for assessing our analytics readiness first.

    The fundamental difference of contextual analytics lies in how it offers analytics to the user to explore data or derive insight within their transactional environment rather than accessed as an external tool. This approach requires a deeper evaluation of the product’s analytical maturity level using a formal framework. Having such a framework can act as a deployment guide for your developers for every stage of embedded BI implementation, including contextual analytics.

    But first, what makes contextual analytics so different that it necessitates a readiness evaluation?

    Contextual Analytics Vs. Embedded Analytics

    With contextual analytics, dashboards, reports and charts are no longer separated but are part of the main user experience. This integral change necessitates a closer look at your data and integration capabilities to align what it brings with all aspects of your software, including existing analytics.

    Embedding these elements directly into core software workflows isn’t a replacement for any existing analytics, but is instead another big step toward making data and analytics a more seamless part of your end users’ everyday work.

    It should be stressed that contextual analytics accommodates various levels of implementation and business requirements. Analytics can be embedded lightly, such as in the form of basic in-line charts and graphs users can see as they explore your software, or embedded deeper to offer AI-assisted insights and real-time alerts that trigger action from users while they work. It can be integrated with an experienced BI vendor partner or in-house if you possess the skill set internally.

    Avoidable Pitfalls of an Improvised Approach

    The need to assess your application’s analytical maturity and readiness for embedding analytics directly into the transactional workflow comes down to avoiding unnecessary BI roadblocks. 

    For many organizations, BI and analytical capabilities are traditionally lower in priority than other aspects of the software. Intending to add BI features later is well-meaning and understandable, but leaving developers to add in features intermittently and leaving it to your users to demand them when needed creates an issue of visibility. Vendors typically lose sight of what their application currently is capable of analytically, or what development needs to make the addition of a new feature not only technically achievable but worth the time and cost investment.

    If you take the time to reassess your application’s analytical capability, your developers and leaders can better understand that your product could be at an earlier stage of readiness than previously believed, and prepare accordingly before seeking out a contextual or other BI solution.

    For example, approaching contextual analytics ad-hoc could mean you find an unexpected older dashboard module, a cost that could have been accounted for in a formal maturity assessment. 

    What it comes down to is this: Implementing a basic dashboard module or data export feature, completely separate product features, is fundamentally different from fully embedded analytics in your users’ core workflow experience. Pursuing a formal evaluation to guide the introduction of contextual analytics—or even a form of embedded analytics at an earlier stage—means your users and product receive the best type of analytical capability your business is ready for today.

    The Five Stages of Embedded Analytics Maturity

    Based on our experience, there are five possible stages of product development, as illustrated in the embedded analytics maturity curve below. As contextual analytics is the final, most advanced stage of an embedded analytics integration available to product owners, the need for a formal assessment of maturity is most important, should it be the target state.

    Accelerating the implementation of contextual analytics comes down to your skillset, which will allow you to identify and evaluate your state of readiness accurately and determine a BI solution’s flexibility. If you do not have the internal expertise, implementing contextual analytics is easier with the help of specialized BI vendors that can help you in your assessment, and offer a contextual analytics platform built around openness. We recommend you look for vendors and partners that allow for:

    • Advanced APIs that lower total integration time and ensure the need to rearchitect the software is minimal to zero.
    • Customized experiences to make your desired contextual linkages, such as showing a user a relevant embedded chart when they click on a table of a client, possible.
    • An open platform that allows for options for on-premises, cloud, multiple operating systems that guarantee long-term security, scalability and flexibility with your analytics.
    • Rapid adaptation to your product development roadmaps with automated processes and administrative tools to lower total cost of ownership
    • White-labelling to make the rebranding and blending of the analytical solution and your native application components seamless.

    Ultimately, having an awareness of your product’s readiness and taking the time to examine your application’s current state means you avoid costly integration mistakes, save time and ensure developers implement the best solution according to your exact needs now.

  • Embedded Analytics Essentials: A Checklist for Success

    Embedded Analytics Essentials: A Checklist for Success

    Integrating the analytics capabilities into your application can be easier than you think

    Today, the ability to create and explore dashboards and reports, embedded directly into the apps your customers use every day, is no longer a luxury but a necessity to keep your product competitive and deliver value to users.

    Embedded analytics brings many benefits for different groups. For end users, it’s a valuable tool to explore their data and discover new insights. For analysts, it’s an integral capability for advanced performance analysis. For product managers, it’s the best way to enrich and cultivate a data-driven experience for your customers with the latest analytical capabilities, built-in.

    Moving away from decisions based on instinct toward decisions based on analytics and metrics delivered by centralized systems is one of the most impactful changes you can drive today. But for product managers in the business of delivering software or utilizing an application to drive customer interaction, the choices available for embedding analytics is a looming challenge.

    This article covers the key challenges, scenarios and checklist considerations product managers need to tick off before implementing any type of embedded analytics solution.

    Embedded Analytics: A Business Case for Buy vs. Build

    Typically, the decision around embedded analytics boils down to build or buy.

    Even with a need for fast technical integration and time-to-market pressures, many product teams first lean toward getting in-house developers to build embedded analytics into their app.

    It’s an understandable first thought, given many have a specific vision for their app that a third-party might not ‘get’ immediately. But opting to build-in analytics more often than not leads to long-term challenges that aren’t always initially obvious.

    The fact is creating an analytics platform from scratch is a complex task that takes years of work to create and refine. Aside from the advanced data expertise and technical integration required, having your developers focus on embedding analytics means they now have to:

    • Face a very high investment cost to recruit the necessary skills for implementation.
    • Provide all support, maintenance and upgrades for your homegrown BI functionality.
    • Replicate the look and feel of your core application to ensure UX consistency.
    • Match the analytical features of specialist BI solutions—a near impossible task.

    With the time and skills needed to build your own analytics into your app from the ground up, you could face higher costs, increased risk and delayed time-to-market for your product—not to mention less time innovating and improving the core product experience for your users.

    Your users ultimately expect to have data accessible to them for their day-to-day work. When they use your product, they will demand a certain level of reporting and analysis comparable to the BI tools they may already use in their own businesses at the moment—and yes, it will go beyond just having basic dashboards. If you don’t have confident answers for the most common questions required before building an embedded solution in-house—do you have the expertise, the manpower, time and knowledge—it’s not advised to pursue the build path.

    So, what about buying an embedded analytics solution for your application?

    What to Look for in an Embedded Analytics BI Solution

    Buying an established embedded analytics solution often does away with many of the problems of building it.

    You can shift away from home-grown reporting to a proven platform that can seamlessly integrate custom analytics into your app, amplify the reach of your product by engaging new and existing users with the latest analytical features, and even automate data discovery and analysis so you gain competitive advantage—though the last capability depends on the particular vendor.

    However, buying comes with its own very important considerations that your product team and business as a whole need to evaluate and answer before making a decision. Below, we list the most important things to look for in both the embedded solution and the partner behind it.

    Does it include everything I need to meet end-to-end operational reporting needs?

    Your users will have a range of functional needs from the embedded analytics solution you buy, so the first thing you must answer is whether it can cater to their day-to-day work and provide access to advanced analytics. For instance, your customers want to know what to do next: Some might only need tabular reports (traditional), while others will demand richly visualized and interactive dashboards that drive action (modern). Eventually, some may look to use automation and AI in their analysis (augmented). The best options are those vendors that let you embed it all—dashboards, authoring, charts, collaboration, data preparation and modelling, admin and security. How each element is embedded (iFrames, JavaScript libraries with REST API calls) and where embedded code can run (cloud, desktop, mobile, web) is just as important to factor in, too. It’s not just about having dashboards in your app, but ensuring you have future-proof capability that continuously scales with your users as their data needs grow.

    Can the solution be embedded seamlessly?

    It’s not just all about having the best and latest features. A cohesive, seamless user experience between the analytics portion of your product and the core software itself is a big part of delivering an exceptional product, so when choosing to embed analytics, ensure the solution allows your team to have exceptional control to make it look like a part of your app rather than like one different app hosting another. Many modern embedded solutions allow you to white-label; this is recommended to allow for consistent design across your platform, as you will eliminate your users’ potential concerns around security, performance or support with using another product.

    Can it help me ensure faster time to market?

    A common problem with creating an embedded analytics solution in-house is the investment in time and money it takes to get your product to market with the new capabilities. It’s also difficult to ensure the integration process can be done as quickly and efficiently as desired. The embedded tool you buy should accelerate that process and significantly reduce the development cycle, so your team is able to begin using your new analytics within days instead of weeks and your devs stay focused on improving your product’s core value instead of configuring reports.

    Next Steps

    Selecting an embedded analytics product can be a difficult task, but so long as you keep your product and analytics objectives aligned and follow best practices checklists, integrating the latest analysis capabilities into your application is not as far-off a future as you may think.

  • The Power of Embedded Analytics and the Build Versus Buy Dilemma

    The Power of Embedded Analytics and the Build Versus Buy Dilemma

    Embedded analytics are a powerful tool for adding value and stickiness to your desktop and mobile apps. Many app developers are providing these features as standard in their tools. But should independent software vendors (ISVs), systems integrators (SIs) or companies building internal apps build analytics tools in-house, or should they buy them from specialist providers?

    There are many considerations to the buy or build dilemma. One reason many developers want to build in-house is the limitation of support services from embedded analytics vendors. Although most providers offer support services, these are not normally included in the price of the package, and are offered at an additional cost. However, in the majority of cases, such support services are in fact effectively a requirement to ensure the visualization is tailored to the use case and look and feel of the app.

    A second consideration is cost. Standard price entry points for embedded analytics can start at $30K to $75K per year. However, behind the upfront pricing structure, there are often multiple levels of service, as well as limits on usage and number of applications the embedded analytics can be used in. This can make pricing far less predictable.

    Creating analytics features in-house may be the best option when working on smaller projects with limited sets of requirements—especially if your development team has a relevant skill set and previous experience developing embedded analytics and data visualizations.

    Lastly, one of the most convincing arguments for building in-house is that product managers remain fully in control over every aspect of their application, not just its functionality but the look and feel as well. By keeping all aspects of development in-house, product teams can control branding, user experience and functionality. The loss of this control is one of the main disadvantages of buying.

    On the other hand, there are many benefits to buying rather than building. The main disadvantage of building in-house is that developers have to switch their focus away from working on the core product to create complex embedded analytics features. Buying saves time and money over training a development team that may lack previous embedded analytics experience and eliminates the need for training where internal resources are simply not available.

    There is also a significant cost associated with building embedded analytics, which on average takes seven months to complete. The estimated average in-house cost can run as high as $350k (based on average U.S. salaries). This includes: four software developers for seven months, one QA professional for seven months, two UX/UI designers for six months and one data scientist for one month.

    Your in-house team is generally responsible for supporting anything built in-house. With the buy option, support is provided by the third party via the cloud and ISVs will not have to allocate resources to fixing issues if and when they occur. As much as 90% of the cost of software during its lifetime is tied to keeping it up and running. Maintenance costs can be significant.

    The time it takes to bring a product to market is another consideration. With average build-it-yourself times taking seven months or more, many product teams decide to buy a bolt-on analytics solution due to the need to release a product as quickly as possible. In a fiercely competitive SaaS market, and with CEOs demanding quick turnaround, buying a pre-built, off-the-shelf solution drastically reduces your time to market.

    Making a final decision on whether to buy or build is a complex task. But in the end, ask yourself:

    • Can my team develop powerful analytics in-house or do I need a specialist? Building analytics tools can be a drain on time and internal resources, and the backend powering even a simple dashboard can be slow and expensive to build from scratch. Plus, you need the resources to maintain it.
    • How does the vendor’s pricing model stack up against my budget? You shouldn’t really be paying any more than what it would cost you to simply build the visualization in-house, so an affordable vendor is essential. However, too often, overly complex pricing structures from vendors result in unpredictable costs, making ROI hard to judge.
    • Has the company received awards for its work? Are they well established and respected in the community? Third-party review sites are helpful here.
    • How much do you trust the vendor? If you are collecting sensitive data, you want to be sure that the vendor isn’t also collecting that information covertly and selling it.

    As more organizations realize just how important understanding their data is, embedded analytics have become the key to harnessing that power. Whether you decide to allocate internal resources or hire a vendor, including powerful embedded analytics and visualizations is virtually a must since it helps you differentiate your app and keep users engaged.

    — Casey McGuigan