The ability of management to steer their organization toward achieving success is directly dependent upon the information they have before them and their decision-making process. Having the right information at the right time requires a data and analytics ecosystem that can present accurate and timely data in an easy-to-understand manner. A best practices approach to analyzing data is to start with highly summarized information and then investigate those areas that are outside the bounds of expectation. Summarized information in the form of measures or Key Performance Indicators (KPIs) provide individuals with the information they need to assess performance and make informed business decisions.
Relevant information can be presented in different forms. KPIs are meaningful, predefined measures that provide individuals with the information that they need to assess previous actions. KPIs can then be compared to target performance and provide individuals with the ability to assess past performance. For example, if the goal is to improve customer satisfaction, then several KPIs can help to monitor that goal, including order cancellations, response times to customer inquiries, and customer churn. Looking at the customer churn KPI in greater detail, the purpose of this KPI is to monitor the rate of customers who stopped being a customer. This KPI should raise several questions such as:
While KPIs present individuals with meaningful information for decision-making purposes, there are several factors that one must consider.
1. Defining the indicators of performance. KPIs can be difficult to define because the definition requires knowing what performance to measure and how to measure them. In addition, consensus from the individuals who are being evaluated based upon the KPI is critical. Without a commonly accepted definition of a KPI, it will not be accepted or used.
2. Obtaining the necessary data. Once the KPI has been defined, the necessary data needs to be acquired and curated. Depending on the complexity and number of the operational systems of the organization, this task can become quite daunting. Ideally, the information that is needed is already stored within a repository such as a data lake or data warehouse.
3. Calculating the values according to the KPI definition. Applying the business rule or calculation to a set of data in order to derive a KPI requires a clear understanding of its definition by the individual who is responsible for this task. Incorrect calculations are primarily caused by a lack of understanding.
4. Performing timely updates. The frequency at which KPIs need to be updated is important for ensuring the data is available and scheduling the calculation of the measures. Keeping the KPIs updated on a periodic or on an as needed basis is critical to providing individuals with current meaningful information for decision making.
5. Visualizing KPIs. Data, by itself, can be overwhelming and difficult to analyze. Through visualization, graphical representations of data can highlight important aspects within the data and assist the viewer in focusing on important items within the set of data being analyzed. In certain cases, visualization of information can assist the viewer in being more efficient with his/her analysis.
6. Presentation of KPIs. When presenting KPIs in the form of a report or a series of KPIs in a dashboard, it’s important to understand logical groupings and the number of measures. Too many KPIs presented together in one form is overwhelming to most individuals, so less is more. Also, the grouping of KPIs by subject matter or ranking of importance to the viewer makes them easier to interpret.
7. Analytics Catalog and Enterprise KPIs. Establishing common enterprise-wide business definitions and KPIs for individuals within an organization to use and access is critical to supporting fact-based decision making. An Analytics Catalog makes information available by providing business and data definitions as well as access to and discovery of KPIs, reports, and dashboards for insights and investigation.
In order to effectively make informed business decisions, individuals must be able to easily locate relevant information. Curating reports and dashboards into a single, central Analytics Catalog simplifies the ability to quickly access and view KPIs – especially when those indicators may have been created in different underlying tools or applications. By centralizing KPI access, individuals can quickly see and assess relevant and timely information so that they can make informed and ideally better decisions.
The Analytics Catalog within ZenOptics Analytics Hub centralizes reports and dashboards from across the analytics ecosystem to establish an internal KPI marketplace for organization. Individuals can quickly and easily access enterprise KPIs, standard business definitions, and the supporting analytic assets to assess performance and further investigate what the indicators are communicating. To learn about ZenOptics Analytics Hub, please visit: https://zenoptics.com/platform
To learn about how ZenOptics delivers a KPI marketplace for its customers, please request a demo at https://zenoptics.com/demo
Organizations across industries face the pressing need to extract valuable insights from their data and analytics more efficiently. This is where analytics automation becomes crucial. Analytics automation is a powerful functionality to streamline the discovery and use of analytic assets (such as reports, dashboards, and visualizations) from one or multiple source systems. In this blog, the value and capabilities of analytics automation are explained and related to the other components of an Analytics Hub, including the BI portal and analytics catalog.
ZenOptics simplifies the process of extracting and integrating analytics asset metadata into the Analytics Catalog through its smart connectors and platform. Analytics automation incorporates AI (augmented intelligence, in this case) into the analytics catalog to provide cross-platform indexing, metadata about the analytics assets, and usage data – all in the centralized location of the ZenOptics analytics catalog. ZenOpics then leverages machine learning algorithms and smart algorithms to identify patterns, relationships, and trends in data. This automation enables ZenOptics to generate meaningful, robust recommendations that foster greater efficiency during analyses and decision-making processes.
Most people typically know, or at least have an idea, of which analytic assets they commonly work with to conduct their business. However, they don’t always know if there is a supplementary – or perhaps even better – analytics asset that they should be using unless they have some sort of assistive intelligence helping to recommend useful information. For example, analyzing sales results for the month of August by region provides a comparison by geography for a specific time period. However, if the required analysis is a trend or budget comparison, then a different analytic asset is needed to obtain the knowledge and insights of the data over time. Conducting an exploratory search in the available BI tools and reporting applications to see if an analytic asset already exists is a time-consuming exercise. Further, if you don’t have access to the analytic asset or the BI tool, you won’t know what else is available – and in those cases, you either build or request another report and thereby contribute to the report sprawl within your organization.
Analytics automation aids the discovery of enterprise analytic assets to individuals with relevant recommendations related to a search term, file name, keyword, metadata element, metric, KPI, or other term that is being viewed. Within ZenOptics Analytics Hub, an individual can conduct a search and have a list of analytic assets compiled – both of the assets that the individual has access to and those they may not. For those analytics assets where the individual does not have permission, s/he will be able to view the name of the report, the description, helpful metadata for that report, and the report owner and then be able to request access. This functionality facilitates discovery of information that individuals may not be aware of and greatly reduces requests to build new analytic assets.
By automating repetitive and time-consuming analytical tasks, organizations can streamline their data processes and focus on critical analysis, strategic planning, and decision making. Analytics automation offers benefits such as faster analytical analyses, enhanced accuracy, and increased agility, therefore enabling businesses to gain a competitive edge.
Analytics automation in ZenOptics workflows is the ability to bundle analytical assets from one or many different sources (e.g., BI tools) that have been integrated into the analytics hub to support a business process. For example, financial month end closes require the reconciliation of subsidiary ledgers and systems to the main accounting system each month in order to close the financial records. As part of the reconciliations that need to take place, comparing revenue and sales from a CRM system to revenue reported in the accounting systems requires reviewing reports from each of those systems to ensure that all of the sales transactions in the CRM system have been appropriately accounted for in the accounting system and that revenue recognition has been handled correctly. This activity requires consistency each month and utilization of the appropriate and same analytical assets that correspond to the same time period. Within ZenOptics Analytics Hub, an Analytics Workflow is a bundle of analytical assets that have been organized, synchronized and shared with colleagues to help facilitate analytical processes and collaboration such as a financial month-end closes, regulatory reporting, and compliance.
Utilizing analytics automation brings numerous benefits to organizations:
Increased Efficiency: Automating analytics discovery and workflows minimizes the manual tasks associated with gathering and preparing assets for business processes – therefore reducing the time and effort required to derive insights. This boosts operational efficiency and allows teams to focus on high-value analysis and decision-making.
Enhanced Accuracy and Consistency: Manual business processes are subject to human errors, but with ZenOptics, the risk of mistakes is minimized because the analytic assets are curated, defined, and certified as trusted. This ensures the use of appropriate analytic assets and the standardized application of analytical processing, thereby resulting in consistent, reliable insights.
Accelerated Time-to-Insights: ZenOptics streamlines the end-to-end analytics process, enabling organizations to obtain actionable insights faster. By automating repetitive tasks through workflows and providing intuitive collaboration and efficiency features, ZenOptics shortens the time between analytics discovery and meaningful analysis.
Improved Collaboration and Governance: ZenOptics promotes collaboration among teams by providing a centralized platform for analytics access, sharing, and collaboration. It enhances analytics governance through its analytics catalog and provides analytics discovery, certification of analytical assets, security and compliance, analytics usage statistics, and monitoring across the organization.
In the era of advanced analytics, organizations need robust solutions to automate and streamline their analytical processes effectively. ZenOptics Analytics Hub supports this need through its analytics automation functionality, which enables businesses to unlock the full potential of their data and analytics. By leveraging ZenOptics’ capabilities for analytics integration, preparation, workflow, governance and collaboration, organizations can achieve increased efficiency, enhanced accuracy, accelerated time-to-insights, and improved governance and collaboration. With ZenOptics, individuals and organizations can confidently navigate the complex data and analytics landscape and make information-driven decisions that deliver success in today’s competitive business environment.
Information-driven decision-making in today’s fast-paced business environment has become paramount for organizations seeking a competitive edge. However, the ever-increasing volume and complexity of data and analytics within an organization pose significant challenges to ensuring effective decision-making processes. A critical component of accessing the right information at the right time in a fast-paced business environment is an analytics catalog coupled with analytics governance to harness the true potential of an organization’s analytic assets (e.g, , reports, dashboards, visualizations, etc). ZenOptics’ Analytics Hub software platform is purpose-built to establish a unified view and catalog of an organization’s enterprise analytic assets and to enable a streamlined analytics governance process to facilitate dec. In this blog post, we will explore the imperative of analytics governance and how ZenOptics software facilitates data-driven decision making through effective governance practices.
Analytics governance unlocks the transformative power of information while fostering operational efficiencies and reducing organizational risk. By implementing robust governance policies, processes, and controls across the entire analytics pipeline – from quality and accuracy of data to the discoverability and accessibility of the reports – the accuracy, consistency, and security of analytics assets, analyses and initiatives are well supported and trusted throughout decision-making processes. With a unified approach to governance, risks are mitigated, trust is built in data-driven insights, and value is derived and maximized from analytics investments. In addition, misinformation, redundancies of effort and associated costs are reduced or eliminated, thereby improving financial and operational success.
Analytics governance is not without its challenges, particularly given the complex nature of legacy BI and self-service ecosystems. The proliferation of report silos, multiple BI tools, report sprawl from self-service BI, and the compounding pool of no-longer-used and unverified reports contributes to confusion, inefficiencies and suboptimal performance with operational analytics processes. Moreover, inconsistent business terms and KPI definitions hinder the ability to derive consistent, meaningful insights. in order to reap the benefits of governance, organizations require a comprehensive solution that can address these obstacles – not just technically, but also with respect to people and process issues. This is where ZenOptics software steps in to provide a holistic solution.
The foundation of ZenOptics’ Analytics Hub is its ability to connect into various BI tools, applications, and storage systems in order to centralize analytics assets in a unified BI portal and analytics catalog where individuals can easily discover and access the information they desire. By providing a centralized analytics catalog of an enterprise’s analytic assets, search capabilities facilitate the quick discovery of information. Individuals no longer need to log into each BI tool, application, or storage system to locate the information that they are looking for, thereby saving time and effort. Even greater value comes from the fact that visibility across tools (and the assets in each) can preemptively thwart the creation of redundant analytic assets and reduce the report sprawl that so often occurs.
Through ZenOptics, a standardized business glossary is automatically derived from the analytic assets cataloged and managed. This drives consistent understanding and interpretation of KPI definitions across the entire organization. For example, the definitions of income, revenue, and sales are provided in a centralized manner so that everyone in the organization can understand the meaning and differences of each business term and KPI. By eliminating confusion and ambiguity, common understanding is supported, collaboration is enhanced, and confident decision making is fostered.
ZenOptics inherits the authorizations and permissions that an organization has established within its underlying source systems so that it is not necessary to create and maintain another cumbersome security layer. ZenOptics also provides administrator views of BI tools’ report usage history such that popular reports as well as unused reports are promptly identified. With this information, unused reports can be retired and popular reports can be reviewed to ensure quality compliance requirements associated with analytics governance standards.
Ensuring the quality of analytics is paramount for reliable decision making. ZenOptics provides a powerful analytics quality management capability through its certification process. The process assigns stewards to review and sign off on the analytic asset according to a standardized certification checklist. The asset can then achieve a certification status, which informs individuals that the report, dashboard, or visualization has been reviewed for usability. This empowers the organization to maintain high-quality analytic assets, providing a solid foundation for making informed decisions based on accurate information.
ZenOptics simplifies compliance efforts by offering robust features such as analytics metadata, data source identification, and report/analytics certification processes. These capabilities support accountability for regulatory and audit requirements – including the analytic asset and the source of information for the asset.
Analytics governance plays a pivotal role in driving an organization’s success. ZenOptics software has emerged as a fulcrum, enabling its customers to reclaim control over their analytics initiatives. By centralizing governance processes, ensuring analytics integrity, and promoting transparency, ZenOptics empowers individuals to make timely and confident information-driven decisions.
In today’s data-driven world, individuals and businesses rely heavily on analytics and data to make informed decisions and gain a competitive edge. However, managing and harnessing the vast amounts of data available can be overwhelming without the right tools. This is where ZenOptics, the leading analytics hub software, steps in. In this blog, we will explore the reasons why ZenOptics stands out as the best analytics software in the market, empowering organizations to maximize the return on their investment in BI, analytics and data assets.
A crucial feature of ZenOptics is its centralized analytics catalog, which acts as a BI Portal and a unified repository for all analytic assets across the organization. The analytics catalog allows users to easily search, discover, and understand available reports, visualizations and dashboards within a single intuitive interface. With ZenOptics, users can efficiently navigate the analytics landscape of their organization thereby reducing the time spent on hunting for relevant information and improving functional processes for analysis and decision making.
ZenOptics excels in supporting an analytics governance framework, ensuring analytics integrity, security, and compliance. With a customizable certification process, organizations can validate and certify the integrity of each analytic asset from its data sources to the business presentation layer of the report. The software enables efficient analytics usage tracking, Key Performance Indicator (KPI) management, and certification of analytic assets. These capabilities increase user trust and confidence in the accuracy and reliability of analytics and data to make informed business decisions.
ZenOptics facilitates seamless collaboration among business users, analysts, and information stewards. The software provides a user-friendly interface for discussions, feedback, ratings and knowledge sharing. This collaborative environment enhances team productivity, promotes analytic asset literacy, and encourages cross-functional insights while fostering an information-driven culture within organizations.
Understanding the data sources and impact of analytic assets is critical for ensuring analytics accuracy and for effectively managing changes. ZenOptics automates the process of identifying redundant reports by comparing the source and metadata. This functionality also enables impact analysis, allowing users to assess the potential consequences of changes to data sources or reports, ensuring confident decision making.
ZenOptics leverages machine learning and advanced algorithms to provide intelligent analytics discovery capabilities. The software recommends relevant reports and visualizations based on user preferences, usage patterns, and contextual information. This drives analytics exploration, empowering users to quickly find valuable insights and make information-driven decisions without the need for manual search or creating another redundant report.
Recognizing the need for seamless integration with existing systems, ZenOptics offers flexible options. It easily works with popular business intelligence tools, data visualization platforms and embedded analytic applications. This interoperability allows enterprises to leverage their existing investments while maximizing the value of ZenOptics as its analytics hub software.
ZenOptics is the top choice for organizations seeking a solution that combines governance, centralized analytics cataloging, collaboration, automated analysis, intelligent content discovery and automated integration. With ZenOptics, you can streamline business processes, improve confident decision making, and receive value from your analytics and data assets. Stay ahead of your competition and become the industry market leader with ZenOptics, the enabler of business productivity and confident and timely business decisions.
The understanding and interpretation of data is not always self-evident. Analytics assets such as reports, dashboards, and visualizations require at least a basic level of data and analysis skill as well as some understanding of the content in order to properly interpret the information and make decisions. When people have questions about the content within an asset, they usually turn to their immediate colleagues, the report producer, or owner to seek answers. Yet all too often, the same questions keep arising or there becomes a pattern of misunderstanding, perhaps because people forget the answers to their questions or they don’t understand them in the first place. This creates inefficiencies and adversely affects productivity and decision making, which are hidden costs that compound with the growth of an organization. Establishing a process of collaboration that documents the communications helps prevent recurring questions and creates efficiencies over time. According to Gartner, “Collecting different perspectives on data to establish a cohesive understanding is critical for decision making. Data and analytics leaders must introduce collaboration capabilities to achieve analytics democratization.”(1)
When people share an analytics asset, they often do so by attaching the document to an email, providing a link in an email message, or uploading the asset in a collaboration tool such as Slack. Often a thread of communication is created when people ask questions or share insights about the analytics assets. While the communication and collaboration may be meaningful, the analytics asset becomes nested and buried in the thread of responses. Also, recalling the communication based on a particular analytics asset is an exercise in remembering the topic and then searching and hunting for the information that you are seeking. These methods prioritize communication and not the analytics asset.
Unlike email or collaboration tools where the focus is on communication and the analytics asset is an artifact that gets buried in the thread of responses, analytics collaboration through an Analytics Hub prioritizes the analytics asset and combines the communication threads. Designed to increase the contextual value of each analytics asset, the Analytics Hub creates a unified view of all the assets and their associated metadata – including comments and other collaborative properties. The focal point is the analytics asset itself, and individuals can increase the understanding and value of that asset by rating it, posting comments, and providing feedback. In this manner, the important discussions and context about the analytics asset are captured and are easily accessible whenever a particular analytics asset is viewed. (For more information on Analytics Hub components including collaboration, analytics catalog, and BI Portal, read “Analytics Hub: A Single Source for Trusted Enterprise Analytics Assets.”)
“Collaboration is about the application of collaborative capabilities to analytics workstreams for organizations that want to provide an environment where a broad spectrum of users can simultaneously co-produce an analytics project, bringing insights into action”, according to Gartner. (2)
1. Increased Understanding: While each analytics asset has a name, the name by itself is typically not descriptive enough to provide anyone with an understanding of the content. Further, additional contextual information rarely accompanies the asset to describe the content, relevant business rules, metadata, parameters for appropriate use of the information, and business owner or subject matter expert.
For example, in looking at a report called “Sales by Customer,” one would think it would be a listing of customers and the corresponding sales that were made to each customer. Upon further thought, one may have questions such as:
In an analytics hub, this information can be provided and connected with the report itself, eliminating the need for each viewer to wonder and seek answers to the same set of questions. Any subsequent questions can collaboratively be explored (asked, pondered, and answered) and preserved alongside the asset. This yields greater clarity and understanding for everyone who views that analytics asset.
2. Improved Analysis: Analytics collaboration functionality within an analytics hub provides individuals an understanding of the content and associated business rules related to an analytics asset and gives them the ability to then determine how best to use the information.
Using the same prior example, if the “Sales by Customer” report contains data that is sourced from the organization’s accounting system, it will contain information that will also be reflected in financial reports used by management. If there is another report called “Customer Sales” that is sourced from the organization’s sales force automation (SFA) system, it will contain information that the sales group uses to track customer sales. However, the definition of a sale may be different between the accounting and the SFA systems! The accounting system will recognize a sale when there is a fully signed agreement and the product or service has been delivered. The sales force automation system will recognize a sale when there is a fully signed agreement, regardless of whether or not the product or service has been delivered. The difference can be significant. Knowing the difference between “Sales by Customer” and “Customer Sales” reports will improve analysis because there is greater understanding of the content that is being examined.
3. Better Decision Making: Making the best possible decision rests with the information that is being used. If one does not have a full understanding of the information being examined, then the analysis will be off. If the analysis is off, the best possible decision may not be made. So, producing better decisions requires an understanding of the information being examined – which includes the context of the data, the business rules being applied, the source of the data, and perspectives of other individuals who have used the analytics asset. With greater understanding of the information, the analyses are improved because individuals have an appropriate contextual appreciation for data within the analytics asset and how it should be best used.
The ability to share understanding and perspectives helps to provide context to the information that is being examined. Analytics collaboration facilitates the ability to provide understanding by gathering ratings, feedback, comments, metadata, sourcing, and other relevant information and combining it with each analytics asset within an analytics hub. Preserving this contextual information alongside the asset allows consistency and efficiency in analysis – without having to rehash questions that have already been addressed. As a result, analysis, interpretation, and decision making is improved because an increased level of common understanding is easily delivered and accessible.
References:
1) Sun, J., Pidsley D, (2 September 2022 – ID G00759403) Innovation Insight: Analytics Collaboration (Gartner Inc.)
2) Sun, J., Quinn, K., Pidsley, D., O’Callaghan, G., Ganeshan, A., Long, C., Schulte, W., Popa, A., Macari, E., Fei, F., Schlegel, K., Misclaus, R., Antelmi, J., (17 April 2023 – ID G00772210) Critical Capabilities for Analytics and Business Intelligence Platforms (Gartner Inc.)
The proliferation of self-service business intelligence (BI), embedded analytics, advanced analytics, and other software technologies that provide analytics are commonplace in organizations large and small. Yet many individuals within these organizations struggle to understand what analytics are available, where they can be found, and what information they provide. To address these needs, analytics catalog technology was created to compile a listing of analytic assets that are rendered or made available via a BI Portal. Behind the scenes of the technology, an analytics catalog captures and stores the metadata about each of the analytic assets to provide standardization and additional context for consumers of the information.
Not all digital documents within an organization are analytics assets. For example, a single-purpose document, such as an employee expense report for the month of November or an October customer purchase order are static and stored as supporting information in the accounting system of an organization. An analytics asset is a structured report, dashboard, or visualization that provides information from a data repository. For example, a customer report may list the customer names and the amount of revenue generated for a specific time period. The customer report could be run to display information for the month of October (or whatever time period is desired).
The advent of self-service BI unleashed the ability for anyone with access to the business information to create analytics assets – rather than relying on someone fromIT for assistance to build a report or dashboard. While IT no longer was a “bottleneck” for these resources, the discipline of consistently substantiating and validating the accuracy of an analytics asset was lost; this was the trade-off of embracing self-service BI – a challenge that we still grapple with today.
It is not uncommon for organizations to have multiple BI tools and applications that serve as best-in-class technology for rendering information in a meaningful manner. While it is a best practice to deploy the appropriate technology for a specific purpose, each tool, application, and platform contains a tremendous quantity of analytics assets available for use. Many organizations that we work with have thousands to tens of thousands of analytics assets that have been created as a direct result of self-service BI and that exist in a variety of underlying sources. Yet self-service also introduces challenges: If an individual can’t find exactly what s/he is looking for, they simply create a new analytics new one – one that may be a duplicate (or very similar).
An additional challenge is that In many cases, analytics assets have the same name with different content or different names with the same content. In either case, figuring out which analytics asset to use and trust is challenging and time consuming. For example, an analytics asset named “Revenue Report” may display gross revenue, net revenue that is gross revenue less discounts, or some other variation. Unless there is a good description of the report and the identification of the business owner who can be contacted with questions, trying to figure out if the revenue displayed is gross, net, or something else is an exercise in frustration.
The purpose of an analytics catalog is to address these types of challenges. A best practice for implementing an analytics catalog is to curate the assets before publishing them within the analytics catalog. The process of curating is applying a discipline of reviewing each analytics asset and ensuring that the:
The exercise of curating analytics assets is also a critical component of an analytics governance program that ensures decision making and analysis are performed using trusted and certified assets.
Once analytics assets have been curated, the next step is for the items to be organized in a manner that is intuitive and useful for individuals to search and discover. A best practice is to create a process of organizing analytics assets by subject matter, business function, business unit, legal entity, or whatever systematic structure that is meaningful to your organization. Organizing analytics assets provides a structure for access, context, and usage.
After an overarching structure of organization has been defined, the next step is to create categories within the analytics catalog. For example, if an enterprise software company wants to organize its analytics assets by business function then its categories would be:
Creating categories and then subcategories classifies analytics assets in a meaningful manner for individuals needing that information.
The curation, organization, and categorization of analytics assets in an analytics catalog facilitates the discovery and usage of trusted analytics assets that are available for business users and provides information about where they are sourced or produced as well as the business and technical definition. An analytics catalog combined with a BI Portal establishes a single interface for users to access enterprise analytics assets and search by name, category, content and/or metadata. As a result, individuals can discover the analytics assets that they have access to as well as those that they don’t have security permission. In those cases of non-access, users can request permissions directly within the tool, and a steward (or organizational role) will assess the appropriateness of the request.
The combination of a BI Portal with an analytics catalog lays the foundation of an Analytics Hub that ultimately provides increased usage of BI; greater efficiency, productivity, and confidence in working with analytics assets; and improved decision-making capabilities.
Read our Brown-Forman customer case study to learn about their journey and benefits, both qualitative and quantitative, in implementing ZenOptics. To learn about the comprehensive benefits of Analytics Hubs in driving value from analytics investments, please visit Resources.
There are many ways to access business information. A traditional approach is to log onto a business intelligence (BI) application, such as Tableau or Power BI, and navigate to the report that you need, search for one you hope exists, or create a new report altogether. Most organizations have multiple BI applications that require individuals to log onto each of those applications separately in order to find and access the desired report, dashboard, or visualization. A BI Portal serves as an application layer that simplifies the access by providing a single interface to multiple BI applications and their corresponding content.
First generation BI Portals, in many cases, were simply a web page with a series of links to BI content. Most were developed internally within organizations, if they decided to undertake such an endeavor. Search capabilities were limited to the report names and tags that were assigned to a report. Depending on how sophisticated the BI Portal, the reports were either static web pages or they were rendered from within the connected BI application. While first-generation BI Portals provided a centralized point of view for users, they were a maintenance nightmare for information technology departments since the links and tags had to be manually monitored and maintained.
Second-generation BI Portals evolved beyond a series of static reports and web links into an application layer that provided direct connectivity into the underlying BI applications. In many cases, the underlying user security of each of the BI applications was inherited into the BI Portal, thereby providing report-level security of the data.
To enhance user experience, a BI Portal is designed to centralize access to BI reports, dashboards, and visualizations. Through the BI Portal, users can find information from the corresponding connected BI applications. For example, an organization may use Tableau for its operational reporting and Power BI for its financial reporting. Analysts who need to access operational and financial reporting must log onto Tableau and Power BI separately to obtain that information. However, a BI Portal that is connected to both Tableau and Power BI allows the user to simply log onto the single interface and access both Power BI and Tableau content without having to log onto each of the underlying BI applications separately. With centralized access, users save time and are more productive.
Another benefit of a BI Portal is that users can search for and discover information quickly and easily without having to navigate across multiple systems. Users also want their tools to be intuitive so that they don’t need much training in order to utilize these resources effectively. A good BI Portal allows users to access the information they need, in the way that makes sense to them. The portal should be easy to find what you need, and it should be easy for users to navigate through. This means that they don’t have to learn how to use separate tools for each type of content. Users can search for content by title or keyword, view any related items (such as related reports or dashboards), drill down on the visualizations in a report, filter the data behind a visualization, and more.
BI Portals are an important way to access business information in a user-friendly way. This allows everyone — from executives to managers to salespeople — to be able to easily find and use the information they need when they need it. BI Portals make it easy for anyone within an organization to get their hands on everything from sales data to customer metrics.
Based on my past experience and observations, BI teams (composed of individuals who implement BI applications, create and maintain the BI application and corresponding reports, dashboards, and visualizations) spend one-third to two-thirds of their time answering basic inquiries about BI application connectivity and the names/content of reports from business users. With a BI Portal, connectivity is a simple pass-through of user access/security rights and a self-service environment that enables users to easily find and access the content they need.
The evolution of BI Portals continues. In recent years, the introduction of analytics hub technology has essentially taken second-generation BI Portals beyond BI applications to amplify value across the entire analytics ecosystem. While the core of an analytics hub is a BI Portal, an analytics hub provides users access to reports, dashboards, and visualizations that are embedded within applications (such as Salesforce.com), documents located in the cloud or network drives (such as PDFs), spreadsheets and other report types that are part of the modern technology stack.
In addition to BI Portal capabilities, analytics hubs incorporate analytics catalog, collaboration, automation and governance components. Information about analytics hubs can be found here.
BI Portals are an important way to facilitate access and search capabilities for business information in a user-friendly way. They provide users with a single point of access for reports and dashboards, which can improve the productivity of your organization. With BI Portals, users can interact with data and dashboards in ways that intuitively make sense to them. The future of BI Portals has evolved into analytics hubs that provide far greater information access, collaboration and automation.
Learn how organizations are using analytics hubs to drive more value from analytics.
In today’s business environment where established organizations seek digital transformations and newer organizations embrace a modern technology stack, one common and persistent goal holds true: the desire to utilize data in the most productive manner for the betterment of the organization. Innovation in the area of digital data generation, collection, and processing is now well established and embraced by many data-driven organizations. However, the utilization and consumption of data, information, and analytics in the form of reports, dashboards, and visualizations is still undergoing an evolution. Traditional business intelligence (BI) applications and BI Portals cannot address information formats which now include embedded analytics, augmented analytics, business applications, external documents, and data workspaces. They require a broader and more comprehensive approach to meeting the information needs of business users. An analytics hub represents the evolution of BI Portals and ushers in a new era of analytics productivity.
The foundational components of an analytics hub are application layers that enhance and provide rich capabilities for business users to search, discover, interact, and analyze information that facilitate business decision making. These foundational components are all critical to deliver a cohesive, intuitive analytics experience for users. [See Figure 1.]
At the core of an analytics hub is the BI Portal establishes a single interface for users to access and interact with all their analytics assets – including reports, dashboards, spreadsheets, PDFs, and others – from one place regardless of the underlying tool or application. User productivity increases because they no longer need to separately logon to or be trained on each of the separate BI and reporting applications.
Layered on top of the BI Portal, an analytics catalog serves as the essential technology to create a glossary of existing content – along with appropriate metadata for enhanced, contextual understanding of terms, metrics, and key performance Indicators. The analytics catalog further provides users with curated, organized content and the capabilities of classifying, searching, discovering and understanding the analytics metadata for standardization and consistency. As a result, users can discover the analytical assets that they have access to as well as those that they don’t have security permission. In those cases of non-access, the name and information about the analytic asset are displayed but not the content.
Adding to the capabilities of the analytics catalog, the next layer establishes analytics collaboration. Users benefit from communication tools, the ability to discover and connect with subject matter experts and report owners related to a certain topic of interest, and to connect and share resources directly with individuals and teams – all within the established governance and security parameters. With the example previously mentioned in the analytics catalog description, a request can be submitted for a non-access analytic asset by contacting the business owner who will determine the appropriateness of the request.
One of the unique and critical features of an analytics hub is analytics automation. Here, users are able to automate the assembly of analytic assets from the analytics catalog to support complex analyses and business processes. This further enhances the user experience, improves productivity, and supports analytics governance. For example, a cash forecast analysis can be automated by assembling analytic assets for current cash balance, check register, accounts receivable aging, accounts payable aging, forecasted sales and forecasted expenses. Having analytic assets grouped together in a meaningful manner, refreshed for a synchronized period and shared with a team, establishes a standard analytical process for completing the desired analysis as well as simulates knowledge sharing.
The collection of the previously discussed components combined with the ability to monitor usage and activities provides the capabilities for analytics governance. In addition, BI application management can be performed based on user licenses, usage and analytic assets that are measured and monitored. Value from analytics can be measured based on usage and feedback. With this layer, life-cycle management can be performed from concept to sunsetting of analytic assets.
As the innovator and recognized leader of Analytics hub technology by Gartner Group, ZenOptics released its Intelligent Analytics Hub in 2019 and was declared a Gartner “Cool Vendor” for its innovative technology. Today, ZenOptics continues to pioneer innovative capabilities and features that render tremendous business value to users of its technology in the form of increased productivity, greater insights, and confident business decision making.
ZenOptics has assembled a world-class team of experts in the field of data and analytics, backed by Silicon Valley and global investors, to execute on their vision of providing software that enables data driven organizations to succeed by utilizing trusted enterprise analytical assets. The result is a dedicated, customer-focused team that is passionate about driving analytics value.
Customers across industries worldwide have realized increased value from their analytics and digital transformation initiatives due to the power of an analytics hub to enable business end users. For example, beverages company Brown-Forman uses ZenOptics as a one-stop-shop for all reporting needs, therefore establishing an easy to access single source of trusted analytics resources. Global biotech enterprise Illumina amplified the power of its analytics competency center by creating a unified analytics hub to rationalize and simplify its reporting environment. Sysco Canada Food services has streamlined its reporting processes for executive reporting, saving time and enhancing productivity for reporting teams and executives.
These are merely a few examples of common use cases organizations recognize to drive increased value from analytics assets. Learn more here to find out what an analytics hub can do for you.