In episode #8 of ZenTalk series, Steve Dine, Head of Data Strategy at EXL, and Saurbh Khera, CEO of ZenOptics, discussed the evolving landscape of enterprise data management and analytics and shared their thoughts on making positive impacts.
For organizations, challenges arising from the proliferation of data sources and the integration of diverse analytics models. Steve highlighted the essential role of a unified marketplace for data and analytics assets as fundamental to addressing the complexity challenges and providing the ability to have informed enterprise decision-making.
“We are moving towards AI-driven analytics”, Saurbh added and pointed out the complexities and opportunities that advancements in artificial intelligence bring. He emphasized the necessity of merging disparate data sources into a single, cohesive platform that provides a reliable source of truth for all stakeholders.
The importance of user experience (UX) in enhancing productivity and fostering ownership among data users is the critical element to engagement. “We aim to provide a personalized marketplace experience,” Saurbh noted, underscoring the need to adapt analytics platforms to suit the varied requirements of strategic users, business analysts, and operational teams.
The crucial role of descriptive metadata and robust asset management in a data and analytics marketplace was addressed by Steve as well as advocating for a collaborative data governance approach that enables users to make well-informed decisions confidently.
Another important aspect for user engagement is the critical need to future-proof data and analytics infrastructures. “Flexibility and adaptability are essential,” Steve remarked, promoting agile development practices that can adapt to technological changes and incorporate new tools into existing systems smoothly.
ZenOptics’ Analytics Hub is a dynamic solution designed to adapt to organizational changes. “Our goal is to provide a continuity layer,” Saurbh stated, focusing on the platform’s ability to maintain operational efficiency amid technological advancements.
The importance of embracing change while ensuring continuity and trust within organizations was highlighted in ZenTalk 8. By prioritizing user experience, enhancing data governance, and future-proofing analytics systems, enterprises can confidently navigate the complexities of modern data and analytics management.
Listen to ZenTalk episode 8 with Steve Dine and Saurbh Khera here.
In the latest episode of our ZenTalk series, featured guest Stephen Dine (Steve), head of data strategy at EXL, and speaker Saurbh Khera, CEO of ZenOptics, delved into how organizations can effectively manage change, ensuring continuity and maintaining efficiency with their data and analytics programs.
The discussion began with an exploration of the dynamics of change in enterprise environments, particularly with data and analytics programs. Steve discussed the foundational role of architecture in managing change, from data structuring to managing transitions such as cloud migration. He highlighted the critical need for detailed planning to minimize disruption and prevent user dissatisfaction.
Steve also emphasized the importance of integrating effective change management strategies from the outset, focusing on helping users seamlessly adapt to new tools and processes. He argued that change management should be a core component of the development process, fostering a culture of continuous improvement and adaptability.
Saurbh built on this theme, emphasizing the need to be attentive to the user experience as a critical factor for sustained efficiency and productivity during organizational transitions.
The discussion also covered data and analytics governance and the importance of maintaining trust to ensure user confidence. Saurbh noted that disruptions or inconsistencies could lead users to seek alternative solutions, potentially derailing organizational objectives. He stressed the necessity of establishing a unified user experience and a continuity layer to build and sustain trust through collaboration and open communication.
Future-proofing was another key theme of the session. Steve and ZenTalk moderator Julie Langenkamp spoke about the importance of designing analytics systems and processes that are flexible enough to accommodate future changes, such as when adopting new tools or integrating AI-driven analytics. Ensuring systems are adaptable and can evolve with technological advances is crucial for maintaining continuity, trust, and facilitating user adoption.
ZenTalk 7 underscored the importance of embracing change while safeguarding continuity and trust within organizations. By weaving change management into data and analytics development processes, creating a cohesive user experience, and preparing for future advancements, organizations can successfully navigate transitions and achieve sustained success.
ZenOptics designed its Analytics Hub with these concepts in mind. ZenOptics establishes a centralized repository for end users to easily find and access analytics assets (such as reports, dashboards, spreadsheets, etc). This then becomes a “one-stop shop” for users – regardless of how often the applications or platforms may change behind the scenes with IT. The result is a continuity layer for end users to enjoy sustained productivity and efficiency. Learn more about the components of the Analytics Hub here.
Listen to the ZenTalk 7 discussion with Steve Dine and Saurbh Khera here.
While self-service BI tools have transformed data analysis by speeding up report generation, they also introduce complex issues: How can organizations manage the increasing volume of reports from various tools and maintain a unified source of truth? How do decision-makers integrate reports from different departments? These questions highlight underlying concerns related to BI governance, data security, and data ownership.
As Bernard Marr points out in his Forbes article, the lack of centralized oversight can lead to fragmented understandings and interpretations. This fragmentation from self-service analytics can cause organizations to overlook essential insights, misinterpret data, or make erroneous analyses.
To encourage meaningful discussions at leadership meetings, it is essential to standardize core management reports. According to a study published in the Journal of Big Data, organizations that operationalize data governance gain a competitive advantage. For instance, the collaborative project with the World Health Organization for managing and analyzing data about Neglected Tropical Diseases demonstrates the practical application of data governance.
Organizations need to adopt an integrated approach to report governance to fully capitalize on self-service analytics. A unified platform that allows both business users and decision-makers to access and organize content from various reporting and document management systems is essential. Such a platform should also provide customizable catalogs tailored to specific functional, business process, or organizational needs, enabling effortless access to actionable insights.
If your organization is overwhelmed by a flood of reports and struggles with governance and maintaining a cohesive reporting landscape, it is time to act. Many enterprises are successfully regaining control over their reporting landscapes without sacrificing the advantages of Self-Service BI.
Explore solutions that enable effective navigation of this report chaos, such as the ZenOptics platform, which allows business users and decision-makers to access and organize content from disparate reporting and document management systems into intuitive, personalized, and decision-focused views. It enables productivity, manageability, collaboration, and governance through a single interface and provides users with direct access to all analytics assets. These include reports, dashboards, spreadsheets, applications, and data.
You can learn more about this platform by contacting ZenOptics today.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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