ACTION RESEARCH FORUM: Emerging meta-dimensions harnessing of semantics the shelled data facts: quantified/ numeric/ textual/ graphical/ sound/ other measuring means of real world events.
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Source: https://www.ibm.com/information-technology/grow-your-own-citizen-data-scientists-these-5-tips?S_PKG=-&cm_mmc=Search_Bing-_-CIO_CIO+-+Leverage+Cloud+and+Analytics-_-WW_NA-_-data+scientist_Exact_-&cm_mmca1=000019RG&cm_mmca2=10004651&mkwid=f77b1e8c-6719-4453-8bf1-b23a8e579ee0|462|72937&cvosrc=ppc.bing.data%20scientist&cvo_campaign=000019RG&cvo_crid=81432585798985&Matchtype=e&cm_mmca8=kwd-81432595022440&cm_mmca9=f77b1e8c-6719-4453-8bf1-b23a8e579ee0&cm_mmca10=81432585798985&cm_mmca11=e
Enterprise BI Essentials: The Convergence of Self-Service Autonomy and Enterprise Scalability
Published: June 2016 Report Number: A0235 Analysts: James Haight, Principal Analyst; Hyoun Park, Chief Research Officer
What You Need To Know The resurgence of the self-service analytics paradigm has revolutionized the world of Business Intelligence and data analytics. However, as enterprises have developed solutions in response, Blue Hill believes that we are currently in a new generation of enterprise business intelligence (BI) and analytics that brings enterprise scope and self-service discovery together. Today, as end users begin to demand solutions that do not compromise on agility or scalability, a middle ground is developing in enterprise Business Intelligence offerings. This hybrid approach blends self-service autonomy with the governance, control, and collaboration required for true enterprise deployments. This paper examines the evolving perspective of BI tools and outlines a framework that decision makers should follow to invest in an enterprise-wide self-service BI solution that is right for their organization. Blind Spots Within the Self-Service Resurgence To understand how self-service discovery solutions have fallen short, consider the evolution of enterprise analytics. The success of early pioneers such as Cognos Powerplay and individual tools such as Microsoft Excel proved the value of on-demand access to insights. These tools set the stage for enterprise Business Intelligence solutions to rise to prominence. BI then moved to the cloud, which accelerated access to scale and performance. From here, a new wave of business analyst-centric innovations inspired a generation of self-service data discovery offerings that forced massive market disruption. In response, established enterprise players providing governance and deep functionality have been pushed to evolve, innovate, and launch their own product lines to provide an easy-to-use user experience in today’s world of enterprise BI.
The value proposition for self-service analytics and discovery is clear: allow organizations to democratize insights from data. Rather than wait for a monolithic IT department to process a deluge of requests, decision-makers can have on-demand access to the information they need at their fingertips. The enthusiasm for such innovations is rightfully deserved, but organizations cannot overlook inherent challenges that are incumbent to this wave of self-service innovation. The self-service resurgence successfully improved individual agility but, as a consequence, gave less consideration to areas of governance, collaboration, and scalability. This most recent self-service analytics revolution has already asserted itself and left an indelible mark on the software landscape as new entrants to the market were quick to characterize large incumbents as ‘legacy’ and ‘monolithic’. In recent times, these established vendors have all responded by releasing massive refreshes of their existing products, such as IBM Cognos Analytics and MicroStrategy 10, or new products, such as SAP Lumira and IBM Watson Analytics. Each has firmly responded to the changing market conditions and invested heavily in meeting this demand. There is no dispute that end-user agility through the self-service access is now table stakes in the enterprise analytics conversation. However, there are inherent tension points associated with this wave of the self-service revolution. In pursuit of individual agility, self-service solutions initially provided individualized workspaces through desktop applications. This helped to enable fast response times and a more accessible means for line-of-business analysts to directly interact with data (in a superior way than what they could achieve with Excel), but it did little to ensure scalability and governance. For instance, if individual analysts are each working on a desktop-based solution, they are very likely creating data extracts unique to their own analysis. Without shared business logic of how data are defined or a common environment in which the data are drawn from, each analyst would effectively be working from their own silo of data. As an example, consider an instance where two analysts are asked to investigate the impact of advertising on revenue. If one analyst uses ‘recognized revenue’ while the other uses ‘bookings,’ their findings may yield conflicting results because these analysts face the danger of not maintaining consistent business logic. Moreover, if each analyst is their own data silo, organizations are placed in a difficult situation if they wish to ensure that a new data source is effectively disseminated for use throughout the organization. Similar difficulties arise if organizations wish to avoid duplication of work by publishing applications, dashboards, or curated data sets to a broader team. Desktop-only solutions make such needs fundamentally difficult by making data governance effectively impossible to fully enforce and can negatively impact overall decision-making and alignment. Most important of all is that this onslaught of self-service data discovery solutions addressed a distinctly different challenge than the foundational core BI use cases of dashboards and reporting. The vast majority of the insights that organizations glean from data, whether it is the C-suite or department managers, come in the form of scheduled reports or dashboards. In contrast, the initial generation of self-service discovery solutions set out to solve a different challenge of data accessibility and individual discovery. As such, a clear divide developed in the marketplace; incumbent legacy providers were far better suited at building reports and dashboards at enterprise scales, while self-service discovery solutions poorly addressed these issues. They instead focused on agility and providing on-demand insights. The result was a marketplace in which an organization could not choose a single solution to address both their self-service discovery needs and still achieve enterprise scalability. However, this division of individual exploration vs. organizational consistency is no longer the case. Vendors from both sides of the equation have moved towards a hybrid approach. Leading vendors now balance self-service autonomy and flexibility with the required governance and collaboration capabilities to enable enterprise-wide support. A Third Way: The Best of Both Worlds Blue Hill Research observes that a new hybrid paradigm has emerged that blends the flexibility of self-service analytics with the robustness at scale of enterprise-grade BI solutions. Figure 1 details the changing paradigms within the broader BI marketplace. Blue Hill Research suggests that readers evaluate their current solutions and prospective solutions with the following table as a guideline. Table 1: The Emergence of a New Enterprise Analytics Paradigm Dimension Factors for Consideration Centralized IT Paradigm Self-Service Paradigm Hybrid Paradigm – Self-Service at Enterprise Scale Governance • Assurance of meeting data security and privacy protocols and legislation • Permission and accessibility of data to different segments of relevant stakeholders • Data consistency across all analytic applications Locked-down and monolithic Decentralized and inconsistent across user population Bi-directional governance allows leadership from centralized IT, but effectively adapts to decentralized input. Allows for a dynamic spectrum of access and permissions that evolves based on changing business requirements. Data Modeling • Efficiency and speed of analytic creation • Ability to re-use existing data for future workflows IT-driven models and data structures Analyst-built models Support for analyst-built models and IT-built models. Ease of leveraging models for future analysis across different teams. Copyright © 2016 Blue Hill Research Page 4 ANALYST INSIGHT Dimension Factors for Consideration Centralized IT Paradigm Sel
A Third Way: The Best of Both Worlds Blue Hill Research observes that a new hybrid paradigm has emerged that blends the flexibility of self-service analytics with the robustness at scale of enterprise-grade BI solutions. Figure 1 details the changing paradigms within the broader BI marketplace. Blue Hill Research suggests that readers evaluate their current solutions and prospective solutions with the following table as a guideline. Table 1: The Emergence of a New Enterprise Analytics Paradigm
Paradigm Hybrid Paradigm – Self-Service at Enterprise Scale Governance • Assurance of meeting data security and privacy protocols and legislation • Permission and accessibility of data to different segments of relevant stakeholders • Data consistency across all analytic applications Locked-down and monolithic Decentralized and inconsistent across user population Bi-directional governance allows leadership from centralized IT, but effectively adapts to decentralized input. Allows for a dynamic spectrum of access and permissions that evolves based on changing business requirements. Data Modeling • Efficiency and speed of analytic creation • Ability to re-use existing data for future workflows IT-driven models and data structures Analyst-built models Support for analyst-built models and IT-built models. Ease of leveraging models for future analysis across d
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Each day, 2.5 quintillion new bytes of data are created around the world. That’s a lot of data to manage, and it requires companies to invest in experts capable of analyzing it and using it to spur innovation.
A data scientist can be a valuable addition to any enterprise organization. In fact, there are over 2.5 million data scientist jobs in the US and this number grew by 12% from 2012 to 2016. The need for data scientists is so great, there has been a rise in what’s known as the “citizen data scientist.” This scientific role isn’t filled by someone who specializes in statistics and analytics — these scientists perform a different business function in an organization, with statistics and analysis taking a secondary role in their job obligations.
Citizen data experts aren’t meant to replace traditional data experts, but they serve a critical role by giving data-driven insights a business context. Though a master of analyzing data, a trained data scientist often doesn’t know the particular business complications that lie outside the information. And though a data scientist can optimize the international distribution strategy for a product, that scientist likely doesn’t know the complications that come with trade across certain borders or the unwieldy business partnerships that might obstruct these data-driven recommendations.
In this way, citizen data scientists provide a unique perspective and service to an enterprise. Here are five ways to grow your own personnel to fill this role:
1. Create and foster a business culture of self-service
A collaborative work environment is vital to encourage citizen data scientists to develop within your organization. Without interactions across departments, there is no reason for these professionals to acquire skills outside of the primary scope of their responsibilities. By encouraging work across departments, business and IT get to know one another better, and they can begin to benefit from their varied perspectives.
Ultimately, this encourages an agile environment, with business and IT communicating throughout development processes and with business professionals gaining a familiarity with how data can be leveraged to effect changes to the company’s operations. To maximize this self-service culture, professionals on both sides need access to a range of tools, including cloud-based solutions that can support their efforts to dig deeper into data science and apply their business knowledge to data analysis and inquiry.
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Published: June 2016 Report Number: A0235 Analysts: James Haight, Principal Analyst; Hyoun Park, Chief Research Officer
What You Need To Know The resurgence of the self-service analytics paradigm has revolutionized the world of Business Intelligence and data analytics. However, as enterprises have developed solutions in response, Blue Hill believes that we are currently in a new generation of enterprise business intelligence (BI) and analytics that brings enterprise scope and self-service discovery together. Today, as end users begin to demand solutions that do not compromise on agility or scalability, a middle ground is developing in enterprise Business Intelligence offerings. This hybrid approach blends self-service autonomy with the governance, control, and collaboration required for true enterprise deployments. This paper examines the evolving perspective of BI tools and outlines a framework that decision makers should follow to invest in an enterprise-wide self-service BI solution that is right for their organization. Blind Spots Within the Self-Service Resurgence To understand how self-service discovery solutions have fallen short, consider the evolution of enterprise analytics. The success of early pioneers such as Cognos Powerplay and individual tools such as Microsoft Excel proved the value of on-demand access to insights. These tools set the stage for enterprise Business Intelligence solutions to rise to prominence. BI then moved to the cloud, which accelerated access to scale and performance. From here, a new wave of business analyst-centric innovations inspired a generation of self-service data discovery offerings that forced massive market disruption. In response, established enterprise players providing governance and deep functionality have been pushed to evolve, innovate, and launch their own product lines to provide an easy-to-use user experience in today’s world of enterprise BI.
The value proposition for self-service analytics and discovery is clear: allow organizations to democratize insights from data. Rather than wait for a monolithic IT department to process a deluge of requests, decision-makers can have on-demand access to the information they need at their fingertips. The enthusiasm for such innovations is rightfully deserved, but organizations cannot overlook inherent challenges that are incumbent to this wave of self-service innovation. The self-service resurgence successfully improved individual agility but, as a consequence, gave less consideration to areas of governance, collaboration, and scalability. This most recent self-service analytics revolution has already asserted itself and left an indelible mark on the software landscape as new entrants to the market were quick to characterize large incumbents as ‘legacy’ and ‘monolithic’. In recent times, these established vendors have all responded by releasing massive refreshes of their existing products, such as IBM Cognos Analytics and MicroStrategy 10, or new products, such as SAP Lumira and IBM Watson Analytics. Each has firmly responded to the changing market conditions and invested heavily in meeting this demand. There is no dispute that end-user agility through the self-service access is now table stakes in the enterprise analytics conversation. However, there are inherent tension points associated with this wave of the self-service revolution. In pursuit of individual agility, self-service solutions initially provided individualized workspaces through desktop applications. This helped to enable fast response times and a more accessible means for line-of-business analysts to directly interact with data (in a superior way than what they could achieve with Excel), but it did little to ensure scalability and governance. For instance, if individual analysts are each working on a desktop-based solution, they are very likely creating data extracts unique to their own analysis. Without shared business logic of how data are defined or a common environment in which the data are drawn from, each analyst would effectively be working from their own silo of data. As an example, consider an instance where two analysts are asked to investigate the impact of advertising on revenue. If one analyst uses ‘recognized revenue’ while the other uses ‘bookings,’ their findings may yield conflicting results because these analysts face the danger of not maintaining consistent business logic. Moreover, if each analyst is their own data silo, organizations are placed in a difficult situation if they wish to ensure that a new data source is effectively disseminated for use throughout the organization. Similar difficulties arise if organizations wish to avoid duplication of work by publishing applications, dashboards, or curated data sets to a broader team. Desktop-only solutions make such needs fundamentally difficult by making data governance effectively impossible to fully enforce and can negatively impact overall decision-making and alignment. Most important of all is that this onslaught of self-service data discovery solutions addressed a distinctly different challenge than the foundational core BI use cases of dashboards and reporting. The vast majority of the insights that organizations glean from data, whether it is the C-suite or department managers, come in the form of scheduled reports or dashboards. In contrast, the initial generation of self-service discovery solutions set out to solve a different challenge of data accessibility and individual discovery. As such, a clear divide developed in the marketplace; incumbent legacy providers were far better suited at building reports and dashboards at enterprise scales, while self-service discovery solutions poorly addressed these issues. They instead focused on agility and providing on-demand insights. The result was a marketplace in which an organization could not choose a single solution to address both their self-service discovery needs and still achieve enterprise scalability. However, this division of individual exploration vs. organizational consistency is no longer the case. Vendors from both sides of the equation have moved towards a hybrid approach. Leading vendors now balance self-service autonomy and flexibility with the required governance and collaboration capabilities to enable enterprise-wide support. A Third Way: The Best of Both Worlds Blue Hill Research observes that a new hybrid paradigm has emerged that blends the flexibility of self-service analytics with the robustness at scale of enterprise-grade BI solutions. Figure 1 details the changing paradigms within the broader BI marketplace. Blue Hill Research suggests that readers evaluate their current solutions and prospective solutions with the following table as a guideline. Table 1: The Emergence of a New Enterprise Analytics Paradigm Dimension Factors for Consideration Centralized IT Paradigm Self-Service Paradigm Hybrid Paradigm – Self-Service at Enterprise Scale Governance • Assurance of meeting data security and privacy protocols and legislation • Permission and accessibility of data to different segments of relevant stakeholders • Data consistency across all analytic applications Locked-down and monolithic Decentralized and inconsistent across user population Bi-directional governance allows leadership from centralized IT, but effectively adapts to decentralized input. Allows for a dynamic spectrum of access and permissions that evolves based on changing business requirements. Data Modeling • Efficiency and speed of analytic creation • Ability to re-use existing data for future workflows IT-driven models and data structures Analyst-built models Support for analyst-built models and IT-built models. Ease of leveraging models for future analysis across different teams. Copyright © 2016 Blue Hill Research Page 4 ANALYST INSIGHT Dimension Factors for Consideration Centralized IT Paradigm Sel
A Third Way: The Best of Both Worlds Blue Hill Research observes that a new hybrid paradigm has emerged that blends the flexibility of self-service analytics with the robustness at scale of enterprise-grade BI solutions. Figure 1 details the changing paradigms within the broader BI marketplace. Blue Hill Research suggests that readers evaluate their current solutions and prospective solutions with the following table as a guideline. Table 1: The Emergence of a New Enterprise Analytics Paradigm
Paradigm Hybrid Paradigm – Self-Service at Enterprise Scale Governance • Assurance of meeting data security and privacy protocols and legislation • Permission and accessibility of data to different segments of relevant stakeholders • Data consistency across all analytic applications Locked-down and monolithic Decentralized and inconsistent across user population Bi-directional governance allows leadership from centralized IT, but effectively adapts to decentralized input. Allows for a dynamic spectrum of access and permissions that evolves based on changing business requirements. Data Modeling • Efficiency and speed of analytic creation • Ability to re-use existing data for future workflows IT-driven models and data structures Analyst-built models Support for analyst-built models and IT-built models. Ease of leveraging models for future analysis across d
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