At ZenOptics, we continually research the crucial aspects of managing data and analytics effectively within organizations, and we’ve been discussing these insights and observations in our ZenTalk webinar series with guest speakers. ZenTalk 6 with renowned industry expert Donald Farmer and ZenOptics CTO Heena Sood is a special example of this, as we share real-world approaches for analytics governance – particularly regarding the challenges governing a decentralized environment.

Understanding Decentralized Governance

A pattern emerging in data and analytics organizations is the movement toward decentralized governance models. While centralization was once deemed necessary for effective data management, technological advancements such as data mesh and data fabric have shifted this preference toward decentralization.

Decentralized stewardship marks a significant development, allowing subject matter experts in business units to own their analytics assets within a comprehensive governance framework. This model enables teams to manage assets effectively, aligning with business objectives and enhancing accountability.

Four Approaches for Supporting Decentralized Analytics Governance

1. Create a KPI Command Center.
Establish a centralized repository of Key Performance Indicators (KPIs) and analytics assets where all the relevant indicators and metrics are curated, defined, and easily accessible for specific audience groups. This streamlines visibility over the KPIs/metrics while providing consistency in understanding them – regardless of where within various business units or functional areas the KPI originated. ZenOptics provides such a repository and subsequent dashboard from a list of certified KPIs/metrics in the Analytics Catalog.

2. Build confidence with governed, certified reports and analytics.
Starting with the KPI “Command Center” as mentioned above, create ties/links to certified reports and dashboards for supporting detail. The “Related reports” functionality in ZenOptics establishes linkages between relevant, trusted assets for further discovery and easy reference. When an individual is examining a KPI, they can then see the list of reports and dashboards where that particular KPI is being used.

3. Rationalize and optimize the reporting environment.
Rationalize the reports within your analytics environment up-front for a more targeted user experience. Then establish processes for ongoing report lifecycle management to maintain an optimized environment. With the ZenOptics “ROAR” (report optimization and rationalization) methodology and capabilities, ZenOptics provides visibility of the assets that exist in all the underlying tools, allowing the BI/Analytics teams to assess what may be duplicates, redundant, outdated, or unused. As a result, business end users spend less time finding the accurate, appropriate information they need to make business decisions.

4. A decentralized stewardship model helps drive governance at the organizational level, yet establishes individual accountability.
This provides guardrails for citizen developers or self-service analytics users to create and promote content in a governed manner. For some customers, ZenOptics becomes a centralized report catalog for promoting content from distributed content creation activities according to overarching policies and procedures.

Key Platform Capabilities

The webinar also emphasized the need for strong platform capabilities to support decentralized governance, including:

Utilizing these capabilities allows for a systematic approach to analytics governance, fostering transparency, accountability, and informed decision-making.

Final Thoughts and Takeaways

To conclude our series on analytics governance, Donald and Heena emphasize these critical points:

The effective navigation of analytics governance in a decentralized framework demands a strategic approach, using appropriate tools and methods to synchronize business objectives with data-driven insights. CIOs, CTOs, CDOs/CDAOs, and analytics leaders must view governance as a fundamental element of organizational success moving forward.  Watch the ZenTalk series with Donald Farmer here.

If you wish to learn more about ZenOptics and analytics governance, please request a demo.

Analytics governance is essential for informed decision-making, maintaining analytics and reporting integrity, and supporting consistent business decision-making processes. The ZenTalk 5 webinar with industry thought leader Donald Farmer of TreeHive Strategy and ZenOptics CTO Heena Sood provided an in-depth analysis of the challenges and importance of analytics governance in current business operations. The important insights from the discussion have been summarized for a quick read.

The Role of Information Stewards in Governance

The role of information stewards is to be the experts who facilitate the collaboration between business units and IT departments. These individuals are critical in curating and managing analytics assets effectively for appropriate utilization by business users. The discussion outlined the importance of the stewardship role in formal governance structures, particularly for compliance and adapting to the changing nature of analytics technologies.

Factors Influencing the Need for Robust Governance

Strong governance is especially critical because of factors such as market consolidation, evolving technical architectures, and the growing complexity of analytics systems. Challenges like scalability, managing distributed systems, and self-service governance require strategic responses to uphold effective analytics governance.

Governance and the Relationship with Innovation

Both Donald and Heena conveyed that governance should not be seen as a constraint but as a foundation for safe innovation. Properly implemented governance gives organizations the assurance to try new ideas and adapt to changes in business models and technology, and establishes confidence for end users that they are making decisions based on accurate and appropriate information.

Implementing Effective Analytics Governance

Effective analytics governance involves selecting pertinent KPIs that align with strategic business goals, establishing solid report management procedures, and using analytics governance platforms to gain insights into the utilization of data and the life cycles of reports. Such platforms can provide visibility and insights regarding the overall analytics ecosystem, and will help with rationalization and cleanup efforts – as well as maintaining a clean, streamlined reporting environment over time.

Governance as a Strategic Tool

The main takeaway of the webinar is that analytics governance is a critical element in managing data responsibly and with strategic intent. Governance enables organizations to approach analytics complexities with greater assurance and insight. Based on extensive experience and research, Donald and Heena offered substantial and prescriptive guidance on analytics governance, spurring important discussions on its application in organizations. Listen to the ZenTalk series with Donald Farmer here.

In the context of data-centric organizations, the significance of analytics governance is becoming increasingly apparent for guiding decision-making and ensuring data confidence. The ZenTalk 4 webinar provided an exploration of analytics governance, discussing its essential role in contemporary business practices. Below are the summarized insights and key points from the discussion with Donald Farmer, industry expert and Principal at TreeHive Strategy.

Understanding Analytics Governance

The webinar began with an explanation of analytics governance, highlighting its importance in creating an organized framework for managing analytics assets. The discussion detailed essential aspects of analytics governance, such as defining ownership and establishing governance processes, which are fundamental to a successful analytics governance strategy.

Enhancing Decision-Making through Analytics Governance

Rather than imposing constraints, analytics governance aims to enable effective decision-making. Through governance mechanisms, organizations can foster data accuracy, encourage standardized practices, and boost confidence in decision making. The importance of governance in fostering a culture of innovation and collaboration was a focal point of the discussion.

Technology’s Impact on Analytics Governance

Technology is crucial in supporting analytics governance, with platforms like ZenOptics offering vital capabilities for enabling and supporting analytics governance processes efficiently. Technology aids in managing assets, defining ownership, and tracking usage, thereby enabling organizations to apply analytics governance policies effectively and generate data-driven insights.

Essential Aspects of Analytics Governance

Key aspects of analytics governance, such as asset quality, asset rationalization, ownership establishment, and lifecycle management, were examined during the webinar. Concentrating on these elements helps organizations streamline operations, minimize report sprawl, and provide decision-makers with accurate and pertinent information.

Promoting Efficiency and Innovation through Analytics Governance

The aim of analytics governance extends beyond compliance; it is about enhancing efficiency and fostering innovation. Organizations can achieve higher productivity and sustainable growth by optimizing analytics workflows, proactively resolving issues, and standardizing processes for end users to use data and analytics.

Future Outlook: Embracing Change and Adapting Analytics Governance

As the organizations evolve, adapting analytics governance strategies becomes essential. The webinar stressed continuous improvement, change management, and effective communication as key to the ongoing success of analytics governance efforts.

The ZenTalk 4 webinar offered valuable perspectives on the significant role of analytics governance in business. With a systematic approach, effective technology use, and a focus on data and analytics quality and precision, organizations can adeptly manage the complexities of analytics lifecycle management and confidently make strategic decisions based on trusted analytics.

For a comprehensive understanding of analytics governance and its relevance to contemporary business practices, access the complete ZenTalk 4 recording.

Insights from ZenTalk #3 with Claudia Imhoff

Organizations seek advanced technologies to gain competitive advantages over their rivals and to improve operational performance. In our recent ZenTalk 3 webinar, guest speaker Claudia Imhoff and ZenOptics’ Heena Sood explored how artificial intelligence (AI) can significantly affect analytics. This session, aimed at strategically minded executives, provided strategies for utilizing AI to enhance business operations.

Understanding the Evolution of AI

The discussion on the evolution of AI provided a historical context for understanding augmented intelligence and machine learning. Claudia Imhoff and Heena Sood traced the development of AI, highlighting its progression from theoretical concepts to practical applications that enhance human decision-making. This brief historical overview established the basis for augmented intelligence’s role in analytics.

Key Insights and Strategies for AI Adoption

Slightly different from AI, augmented intelligence is a concept that uses machine learning and AI to complement human intelligence. In this particular discussion, the speakers explained that augmented intelligence is perfectly positioned to simplify analytics by automating tasks and offering intelligent recommendations, thus speeding up insight generation and promoting a data-centric culture.

For example, AI-driven algorithms can analyze user behavior patterns to predict future needs and suggest relevant insights in real time. This capability not only accelerates the discovery of actionable insights but also fosters a culture of data-driven decision-making at scale. By harnessing the power of augmented intelligence, organizations can realize greater efficiencies and support sustained growth in the rapidly changing business environment.

Enhancing Analytics Workflows with AI

The webinar emphasized the need for scalable, efficient analytics management. Augmented intelligence tools are crucial for proactively tackling issues and fostering innovation within analytics operations.

For example, AI-powered analytics platforms can automatically detect anomalies, identify performance bottlenecks, and recommend optimizations to enhance platform efficiency. By leveraging augmented intelligence tools, organizations can streamline operations, reduce manual efforts, and unlock new levels of productivity and innovation.

Managing the Analytics Environment for Efficiency

Addressing scalability, issue identification, and platform optimization is essential for effective data use in decision-making. This involves optimizing various aspects of the analytics infrastructure to ensure scalability, reliability, and performance.

Adapting to the Future with Augmented Intelligence and AI

Claudia Imhoff’s remarks on augmented intelligence highlight its role in improving work processes and decision-making speed.

“It’s ultimately helping everybody be more intuitive about the way that they go about things. And this … artificial intelligence augments the way that humans work.” This closing remark reiterates augmented intelligence’s value in driving business efficiency and growth.

The ZenTalk 3 webinar provided invaluable insights into the transformative potential of augmented intelligence in unified analytics. By adopting a pragmatic approach, leveraging metadata effectively, and prioritizing a people-centric mindset, organizations that innovate will grow in today’s dynamic business environment.

Listen to the full ZenTalk 3 recording for a deeper understanding of augmented intelligence’s benefits in analytics.

Self-service analytics is the practice of enabling people to easily access and understand information in today’s data-driven world. In ZenOptics’ ZenTalk #2, prominent data and analytics expert Dr. Claudia Imhoff explores the degrees of self-service analytics, offering meaningful information on its goals, difficulties, and the crucial role of governance.

Empowerment Through Self-Service Analytics

Empowerment is the first step toward a self-service analytics journey. According to Dr. Imhoff, self-service analytics refers to “analytical environments that enable business users to become more self-reliant and less dependent on IT.” This offers people the ability to leverage data without being constrained by conventional technical complexity or the need to have an IT professional create the request report or dashboard.

Making analytical tools user-friendly is a primary goal and the cornerstone of self-service analytics. It entails putting less emphasis on intricate coding and programming and more on easy-to-use interfaces that let people point, click, and obtain insights with ease.

The Pitfall of Isolation: Analytics Self-Sufficiency vs. Collaboration

Dr. Imhoff stresses that self-service analytics, however, shouldn’t result in seclusion. Instead of having people operate in silos, the goal should be to produce analytics that are advantageous to the entire company. Being self-sufficient in analytics does not mean being exclusive; rather, it means opening up analytics to other team members and working towards a common understanding of information.

The Critical Role of Governance in Self-Service Analytics

A crucial component of the self-service analytics equation is governance. Good governance ensures better decision-making and confidence by upholding standards and processes as well as guaranteeing the reliability of data. It answers queries about the origins of the analytics, their development process, and their dependability.

Accountability follows from good governance, which also keeps companies from having to start from scratch. It is the cohesive element that keeps self-service analytics from sprawling into a mess of analytics chaos.

Four Key Objectives of Self-Service Analytics

Dr. Imhoff lists the following four main goals for self-service analytics:

Ease of Use: The analytics tools need to be user-friendly, eliminating the need for extensive coding or technical expertise. The aim is to expand the audience’s access to analytics.

Ease of Consumption: Analytics should be easy to understand. It is ineffective to have complexity just for the sake of complexity. Improved data literacy, interpretation and more comprehensible data-driven insights should be the main goals of self-service analytics.

Fast Deployment and Easy Management: Self-service analytics solutions should be quick to deploy and manage. To minimize the duplication of effort, users should be able to find existing analytics assets with ease.

Accessibility: Ensuring that all analytics assets are secured and appropriately available to those who need them requires the creation of a centralized platform, such as a BI portal and an analytics catalog. With multiple BI tools and reporting applications in an organization, a BI portal provides a centralized location to access reports and dashboards while the analytics catalog provides the means to easily search and discover the information that is needed and available.

Balancing Data Empowerment and Governance

Enhancing self-service analytics through the balance of empowerment and governance helps organizations realize the value of their data and analytics investments by creating an analytics environment where reports and dashboards are secured, easy to access, discover and utilize. A balanced solution that blends governance with empowerment via user-friendly analytics tools is needed for success. ZenTalk 2 offers insightful information about these important facets of contemporary analytics.

Watch ZenTalk #2 to see the entire conversation about self-service analytics and governance.  To continue learning about how analytics is changing, please watch ZenTalk #3.

The ability to quickly access and analyze information is required in today’s competitive business environment. Self-service business intelligence (BI) and analytics tools have completely transformed the way that businesses utilize data – often to great benefit. However, organizations now also have to deal with some growing challenges and issues introduced by it.

The ZenTalk with featured speaker Claudia Imhoff entitled, “What Have We Done? The Mounting Problems with Self-Service BI and Analytics Part 1 of 3,” explores the difficulties of self-service BI. We have summarized the significant takeaways in the form of four primary challenges organizations face.

Inconsistency in Data Introduces Trust Issues

One of the glaring issues organizations face is the inconsistency in data and the subsequent trust issues that arise. In many organizations, different stakeholders come to meetings armed with diverse sets of data and reports that often include conflicting data points. This inconsistency in data definitions and calculations within reports can breed skepticism and mistrust in analytics. ZenTalk speakers highlighted a real-world example where a financial services company incurred a staggering $40 million rounding error due to the use of incorrect analytics assets. Although this is a dramatic example, such mishaps underscore the need for standardization and data definition uniformity in the analytics process.

Report Sprawl

Another prevalent challenge in the realm of self-service BI is report sprawl. The ease with which modern BI tools allow users to create reports and dashboards has led to a proliferation of reports and dashboards. The issue arises when anyone, regardless of expertise or understanding of the inherent business rules in data source structure, can generate reports. The consequence? A lack of control over the quality and accuracy of these reports. Plus, other people in the organization do not know which reports or dashboards should be used for analyses and decision making.

Lack of Analytics Governance

While data governance is a priority in many organizations, the usage of data in the form of reports and dashboards requires analytics governance as a complement to data governance programs. This layer of governance ensures that the analytics assets are accurate, relevant, and in alignment with organizational goals. It’s not just about managing data; it’s about managing the entire analytics process. The absence of governance not only results in report proliferation but also contributes to unverified accuracy of reports.

Adverse Impact on Decision Making

Perhaps the most critical issue of self-service BI and analytics challenges are the direct impact on decision making. When analytics assets lack standardization and are riddled with inconsistencies, individuals within an organization risk making decisions based on inaccurate data. Further, with the proliferation of reports that may be similar in nature, a decision-maker may not know which report or dashboard contains the appropriate information for use. This lack of validation can lead to a fractured decision-making process. The mounting problems in self-service BI are not merely operational issues; they can significantly impact the strategic direction of organizations and, in some cases, the company’s bottom line.

Conclusion

The ZenTalk concludes by highlighting how critical it is to identify and resolve the issues surrounding self-service BI. Critical issues that require attention include data inconsistency, report sprawl, the necessity for analytics governance, and the possible detrimental effect on decision-making. To realize the value of data as an asset, organizations must resolve the challenges with self-service BI and analytics.

To listen to the full ZenTalk discussing these challenges, and to hear the follow-up segments on how organizations can tackle some of the issues, please click here.

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.

Key Performance Indicators

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.

Conclusion

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.

Foundation for Analytics Automation

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.

Analytics Automation for Discovery

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.

Analytics Automation for Analytics Workflows

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.

Benefits of Analytics Automation

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.

Conclusion

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 decision making. 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.

The Power of Analytics Governance

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.

Navigating Governance Challenges

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.

1. Identification of Existing Analytic Assets

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.

2. Consistent Business Terms and KPI Definitions

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.

3. Security and Usage Monitoring

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.

4. Analytics Quality Management

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.

5. Compliance and Audit

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.

Conclusion

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 information age, enterprises increasingly aspire to become data-driven. An astounding 83% of CEOs, according to an IDC study, strive for their organizations to become more data-informed. Yet, the path to achieving this goal is fraught with challenges. On average, large organizations face the complexity of managing 4-7 distinct BI & Analytics platforms, 129 business applications, several legacy tools, and widespread reliance on spreadsheets.1

The Challenges with Current Analytics Environments

Saurbh Khera, CEO of ZenOptics Inc., recently spoke with industry expert and author of Delivering Data Analytics, Nick Kelly during a webinar about the top three challenges organizations encounter within the current analytics environment:

Productivity Loss

A significant issue plaguing organizations is productivity loss due to hunting for reports and information needed to do their job. Saurbh highlighted that analysts spend approximately 1.8 hours daily merely searching for data. This inefficiency translates into an annual loss of about $25 million.2

Duplication of Content

Duplication of content represents another formidable challenge. A report or analysis generated within an organization may not be easily discoverable or known to exist. When information can’t be found, users or developers will create a new one, which leads to duplicate reports and dashboards. The result is wasted time and redundancy, which can lead to significant annual losses, Saurbh noted.

Non-compliance

Saurbh also called out the operational risk of regulation non-compliance when information is not easily discoverable, with the potential for fines if information is missing or incorrect. Non-compliance with regulatory requirements can damage an organization’s reputation and credibility apart from the monetary loss.

Barriers to the Ideal Data-Driven Enterprise

Nick elaborated on the roadblocks that hinder the transformation into an ideal data-driven enterprise. These obstacles include poor adoption of analytics tools, perceived lack of business value from these tools, difficulties in locating and managing analytic assets, inadequate understanding of user information needs, and inconsistent standards throughout the organization.

Real-World Approach to Overcoming Analytics Adoption Challenges

An effective approach to overcoming analytics adoption challenges involves technology, user experience, and change management. Saurbh underscored that “Providing lots of BI and analytics tools is not a strategy. It’s about using those technologies effectively and making people aware of what is available. So, the disconnect between the strategic goal of using analytics and how people are trying to do so is the most critical part.” Nick and Saurbh discussed activities that are effective to improve analytics adoption:

ZenOptics and the Path to Greater Adoption of Analytics

ZenOptics provides a software platform that addresses the challenges of analytics adoption by emphasizing the power of simplicity. By unifying cross-platform analytics assets in a single user interface, ZenOptics’ analytics catalog simplifies the discovery and use of trusted analytics assets, unlocking the value of your investments and your people.

ZenOptics simplifies discovery and governance across your entire BI analytics ecosystem. It empowers teams to compose and collaborate around their analytics workflows, enabling you to assess and optimize the impact of analytics as usage scales. This is crucial in designing a beneficial experience that drives adoption and data culture.

Reflecting on the capabilities of ZenOptics and the insights from the webinar, several vital takeaways emerge for enterprises aiming to improve their analytics adoption:

1. Create an analytics environment that is easily accessible: Providing one-stop access to all analytics resources can significantly decrease the time spent on data search, thereby enhancing productivity. The unified interface of ZenOptics exemplifies this approach.

2. Foster and facilitate analytics governance: Mitigate chaos and establish trust by instituting a system that ensures analytics integrity and compliance. The governance capabilities of ZenOptics play a pivotal role in accomplishing this.

3. Encourage collaboration and knowledge sharing: A collaborative environment fosters knowledge sharing, leading to improved business productivity, reduced duplication, and a more engaged workforce. The combined features of ZenOptics embody this principle, enabling teams to work more effectively in unison.

These key takeaways offer viable steps for organizations to address their analytics adoption challenges, demonstrating how solutions like ZenOptics can be crucial to their journey. To foster a data-driven culture, concentrate on your goals, implement effective strategies, and leverage powerful tools like ZenOptics. Contact us for a tailored demonstration of how ZenOptics can accelerate your organization’s analytics adoption.

References:

1. https://www.wsj.com/articles/employees-are-accessing-more-and-more-business-apps-study-finds-11549580017

2. https://economictimes.indiatimes.com/jobs/employees-spend-more-than-25-of-their-time-searching-for-the-information-they-need-to-do-their-jobs-citrix/articleshow/69839496.cms