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With Enterprise Business Intelligence and analytics leaders in mind, this article summarizes the main styles of reporting and analysis to serve as a reference for evolving enterprise BI strategies and solution portfolios so they
effectively address different users and analytics scenarios across the enterprise.
With roots dating back almost four decades ago, Business Intelligence (BI) software began with a fairly consistent profile of solution providers and users. Corporate IT was the dominant solution provider. BI insights were powered by data warehouses and IT-based developers performed all of the heavy lifting associated with data modeling and report development.
Users, primarily finance and accounting department managers anxiously awaited their batch reports. Thrilled by these early insights, the IT teams steering the BI ship could do no wrong. BI infrastructure and programs always cost at least a million dollars and large enterprises were the only companies who could afford the dedicated staff required to operate these early versions of business insight software.
Fast-forward twenty years though and we now see that just about everything is different. BI is faster, easier, and there are far more affordable options. Many sales and marketing have created their own BI programs and new departments are lining up for similar benefits each year. In some cases, IT isn’t even consulted as self-service, desktop, and cloud solutions make getting up-and-running as easy as the swipe of a corporate credit card.
As a result, we are beginning to see new working relationships amongst IT and functional leaders who took ownership of BI implementations. Amidst changing trends, paradigms, and moments of modern BI chaos, today’s enterprise BI and analytics leaders need to understand the major styles of BI. BI leaders need to know how each style of BI lends itself to supporting key usage scenarios across the entire enterprise. This eBook overviews five styles of enterprise BI to help readers build a better enterprise BI strategy including a BI solution portfolio that can successfully execute it.
Scheduled or on-demand report generation remains the most prevalent analytics use case. Managed reporting users demand consistent delivery, maximum availability with the freshest data, and access across desktop browsers, mobile devices, and attachments to emails.
Managed reporting typically begins with heavy upfront data and report design. Providing designers with a robust set of layout and data formatting options enable faster report development cycles. The most effective report design tools
automatically generate starting points while giving creators the power to achieve very specific visual layouts through
configuration and custom coding options.
Often overlooked during managed reporting product evaluations are features that deliver precise formatting results such as the appearance or placement of labels, bands, shading, or subtotals. Managed reporting requirements such as flexible scheduling options, multiple output formats, alerts, and notifications are critical for users with less technical expertise and those who are always on-the-go.
Competitive markets demanding constant vigilance together with millennials who grew up with the web have produced a new generation of tech-savvy users who demand levels of agility only offered by self-service analytics.
Self-service users want access to insights without waiting or depending on IT (Information Technology) so they can make informed decisions faster.
Responsive interfaces are important to achieving the zero-training characteristic of true self-service offerings.
Core analysis capabilities include fast, responsive slicing and dicing, drill downs, pivoting, filtering and sorting.
Automatic use of best-fit layouts and auto-aggregations are critical features that help self-service users overcome
challenges in the getting started phase.
Dashboards are a collection of related charts which users and developers can create depending on the tool. By showing current progress against goals, dashboards help unify small and large teams in achieving shared business targets. When business success depends on minute-by-minute or hour-by-hour performance, dashboards may also need to support real-time data integration.
Dashboards should support an array of data visualizations out-of-the-box, providing the richest portrayal of any given metric. Along with the need for a library of compelling visualizations, dashboards should enable users to personalize dashboards via custom tabs for different metrics, save custom widget layouts, set refresh schedules, and predefine filtering and sorting preferences.
While many self-service users’ requirements are addressed with prebuilt reports and dashboards, data discovery and advanced visualization activities are performed by power users and data analysts. These users often seek breakthrough insights from data sets that they craft on their own. Discovery and visualization users typically have technical expertise and demand a more hands-on tool allowing custom code.
Data discovery and advanced visualization features available on the market vary widely as does the technical level of the analyst, developers, and data scientists who practice this style of BI. Certain products present advanced features like regression analysis as a simple push button capability while others offer integration with R packages to achieve predictive analytics.
Regardless, the most compelling insights come when analyzing data from multiple sources. Once reserved for architects and developers, innovations in software technology now enable savvy users to join data from databases, web services, and local files. However, connecting to different sources is just the beginning; more substantive data discovery and advanced visualization scenarios require transforming, cleaning, and amending data for successful integration with other sources.
To select the right solution for this usage scenario, BI leaders need to collect details regarding who will perform integration and analysis tasks, their development skill level, and their target data sources.
More and more organizations are turning to embedded analytics to expedite better actions from Enterprise Business Intelligence insights. Like managed reporting, embedded analytics users are presented with specific insights.
However, embedded analytics differ from opening emailed reports or dedicated BI tools because they are integrated within application screens to assist users with specific tasks.
Embedded analytics approaches offer deployment advantages since users do not have to install or learn additional tools. To achieve success, embedded analytics solutions need to provide extensibility so developers can craft and integrate the right user experience for the given usage context.
Since embedded analytics deployments usually support many users or workers, the solution must effectively scale from a performance and cost perspective. Lastly, companies deploying an embedded analytics solution usually prefer that they look and feel like they belong within the host application. To this point, embedded analytics solutions should offer completely customizable branding and appearance.
Efficiency, revenue growth, and competitive intelligence imperatives will continue to fuel evolution in both BI strategy and technology. While integrated platforms promising to serve the entire company exists, BI and data management leaders need to first understand user needs as they relate to evolving BI usage paradigms such as the ones in this eBook.
Knowing the above, BI leaders can successfully map business needs against technologies and refine solution roadmaps. The five styles of enterprise BI discussed in this eBook serve as a base reference. Factors such as organization size, industry, or even availability of IT resources play major roles in influencing the need and even the feasibility of each approach.
Regardless, it’s important to characterize BI needs based on their usage contexts while taking into consideration user skills, availability of time, and required levels of functionality. Enterprise BI technologies, their costs, and associated ownership responsibilities have changed tremendously over the past few decades.
BI and data management leaders play a critical role in managing an increasingly complex solution portfolio now owned by multiple departments. While evaluating new BI technologies, enterprise leaders need to differentiate between the key styles of modern enterprise BI and ensure products align with both functional and non-functional requirements associated with targeted BI usage scenarios. Contact Musato Technologies to learn more about our ICT services that empower businesses and organizations to yield great results. An article first published by Jinfonet.
Gideon Ebonde M. is the CEO and Chief Software Architect at Musato Technologies. He is experienced Software developer with a demonstrated history of working in the information technology and services industry. He has a strong engineering professional skilled in Mobile Application Development, Enterprise Software, AI, Robotics, IoT, Servers, Cloud and business application. He is an accomplished DevOps software engineer and a visionary computer scientist and engineer.
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