So, You Think You Want to Implement Self-service BI: Is Your Organization Ready?
So, You Think You Want to Implement Self-service BI:? Is Your Organization Ready?
The article is from work during 2018, but still applies to today. The purpose of this article is to present a review of self-service BI (SSBI) technology for the purpose of informing the decision to adopt and of guiding its successful implementation throughout the organization.??The methodology of this research is as follows.??I first conducted a general survey of popular articles, blogs, summaries and reports with the purpose of “listening” to the current and relevant dialogue surrounding this technology.??Then, I categorically distilled the most informative of these articles into lists of (1) Benefits, (2) Barriers, (3) Enablers, (4) Organizational Factors, (5) Technological Factors, (6) Activities, (7) Objectives and (8) Impacts.??These categories ultimately inform the development of a program/action logic model as starting assumptions.??With this report, the decision-maker will have a holistic view of SSBI technologies to guide the decisions of whether to pursue adoption or wait, and?how?to prepare the organization for implementation.
Keywords:??Self-service Business Intelligence, Logic Model, Technology Implementation
Although Business Intelligence (BI) technologies have existed for more than twenty-years, the relatively recent emergence of self-service BI (SSBI) offerings promise much in the way of empowering business users and reducing both the dependence and demand loads on IT-departments. In the information era, organizations must balance the need to compete through faster and better-informed decisions with the need to adopt emerging information-enabling technologies prudently.??The following report presents a holistic overview of SSBI technologies, their risks and benefits, their organizational dependencies, and their capabilities.??The information is structured so as to inform the maturation of a program logic model for implementing SSBI within the organization, where these research data-points form the initial assumptions framing leadership’s decision to pursue adoption.??Further market research, cost-analysis and technology-readiness research is recommended for framing the program concept and for planning its implementation.
Introduction
Within our organization, there is a need not only to push services and product offerings to potential customers, but also to pull signals from both existing and potential customers into information shaping our strategic posture.??Although we acquire new work successfully, there is a need to improve sensitivity to customer satisfaction.??Further, as a growing corporation, our diversity of revenue streams and product/service offerings silo, rather than network, information.??These silos impose both short-term and long-term limits on the effective monitoring of activities, allocating of resources, and sensing of emerging problems before they escallate.??As a result, much of our leadership’s business-development activities are of the reactive “fire-fighting” variety.??
Self-service BI
To be competitive, our organization must improve proactive decisioning.??Self-service BI promises much in the way of both descriptive and predictive analytics supporting rapid, adaptive, data-driven decision-making.??By endorsing a program for SSBI implementation within our organization, leadership will be leveraging many of the un-tapped, yet built-in capabilities of its current cloud platforms.??Thus, without incurring additional technical costs up-front, an iterative SSBI program will support the ongoing cultivation of information to illuminate competitive blind-spots.??Further, because each functional-area manager is empowered as an analyst, this program supports our development as a data-driven organization.
Benefits of SSBI
In general, SSBI technologies offer several advantages to the organization’s people as well as its process performances.??First, SSBI empowers the autonomy of business users (McCartney, 2016; "Self Service BI vs. Traditional Business Intelligence," 2016; Sikes, 2017).??Second, SSBI unencumbers an organization’s IT-department (Hertz, 2018; McCartney, 2016; Sikes, 2017). Finally, SSBI enables agile decision-support (Hertz, 2018; McCartney, 2016;).??When implemented as an overall system of data-management technologies, SSBI is a valuable enabler of information cultivation across the enterprise. Together, this list of benefits frames the initial?assumptions?guiding our program logic model.
Business User Autonomy.
SSBI empowers business users in several ways.??First, because these technologies require no technical or programming skills, SSBI enables non-technical users to conduct their own data analyses (Hertz, 2018). Further, users can manipulate reports without the use of SQL aggregation or Excel Pivots ("Self Service BI vs. Traditional Business Intelligence," 2016).??Additionally, whereas traditional BI is managed exclusively through IT-departments, SSBI enables business users to connect directly to their data, granting them independence and autonomy from IT (McCartney, 2016; "Self Service BI vs. Traditional Business Intelligence," 2016).??With no backlogs, waiting or mismatches, SSBI lends business users enhanced control, agility and speed to generate the information they need (Sikes, 2017).??Finally, SSBI encourages business users to experiment ("Self Service BI vs. Traditional Business Intelligence," 2016).
IT-department Balance.
IT-departments, too, benefit from the organization’s implementation of SSBI technologies.??Freed from the backlog of data-requests, the IT-team can breathe and add value by focusing on more strategic IT initiatives (Hertz, 2018; McCartney, 2016).??Additionally, SSBI enables the IT-department to make quicker changes, deliver quicker support, and drill-down their focus on data and models (Sikes, 2017).??
Data-driven Decision Support.
All of these benefits to users ultimately correspond to an enhanced decision-support capability across the enterprise.??The principle benefit offered by SSBI technologies lies in its ability to empower business users to access and act upon intelligence quickly (McCartney, 2016).??Unlike traditional BI, SSBI enables on-the-fly decision-support (Hertz, 2018).??Faster, more agile, decisioning enables the organization to act upon data-driven strategies essential to maintaining one’s competitive advantage in an ever-changing environment. Further, because everybody uses the same version of data, decision-support information is trustworthy (Hertz, 2018).??Finally, SSBI technologies reduce significantly the burden of data extraction and processing, enabling quick and easy data discovery for ad-hoc questions ("Self Service BI vs. Traditional Business Intelligence," 2016).
Easy Implementation.
When compared with traditional BI implantations, SSBI implementations have a low initial cost as well as a low cost-of-ownership ("Self Service BI vs. Traditional Business Intelligence," 2016).??As a result of its rich interactive features, user adoption is typically greater in SSBI.??With a shorter development cycle than traditional BI, SSBI uses less IT resources to deploy across multiple platforms and devices.??Overall, SSBI places less load on enterprise servers, which reduces data complexity and storage needs, and increases system performances.??
Barriers and Enablers
Factors are resources and/or barriers which potentially enable or limit program effectiveness.??SSBI implementations face six classes of factors: (1) data governance; (2) business-user support; (3) leadership and commitment; (4) IT-department support; (5) cost and risk management; and (6) strategy.??The following sections consider each factor both as a barrier, and as an enabler of successful SSBI implementation.??Each of the identified barriers limits or diminishes the value and success of SSBI adoption within an organization, whereas each of the enablers negotiates or responds to a reciprocal class of barrier.??Together,?both Enablers and Barriers contribute as?inputs?within the program logic model.??
Data Governance
The need for data governance remains central to the development of actionable information from data.??Without the pre-or co-establishment of a data governance framework, SSBI initiatives struggle against the issues of data quality, shared metrics or veracity problems; due to the lack of information quality and consistency, the preponderance of time, cost and risk of BI initiatives comes from data integration (Gallo, 2018).??Along with the need to assure quality, organizations must continue to ensure security of its data, particularly of its datasets containing personal information (Underwood, 2016).??Other data issues that hinder the successful adoption of SSBI include the inaccessibility of data and the continued need for data in analysis-ready formats (Pilkington, 2016).?
Organizations with data and tool governance frameworks ready to implement or already in place are better enablers of successful SSBI implementation (McCartney, 2016; Zaino, 2017).??Additionally, an increasing number of SSBI tools integrate with self-service data-preparation solutions to help users prepare data for analysis in less time.
Business-user Support
Chief among the barriers to successful SSBI implementation is the risk that will not actually?use?the SSBI tools they are given (Zaino, 2017).??New business-users of SSBI tools may face difficulty switching from their old methods to incorporate the new analytics tool.??The tool itself might present ease-of-use issues.??Additionally, the risk that business-users are not completing or prioritizing their training poses a significant barrier (Brown, 2016).??Finally, because the business-users are not analysts, they lack both mind and skillsets to view data holistically, which can create redundancy and interpretation issues that impede SSBI success (Sikes, 2017).??
Training is an important enabler of SSBI that mitigates the risks imposed by business-user needs (Brown, 2016).??Additionally, change and project management tools such as strategic road-mapping and gap analyses are essential to both the training and the implementation of new SSBI technologies (Guillen, 2017).??Further, the use of a pre-implementation framework to identify future business-users and to map where everyone belongs along permission levels and data views is a key enabler of SSBI implementation success (Sikes, 2017).??
Leadership and Commitment
As with many implementation projects, the need for clear and persistent leadership and executive commitment is a major barrier to SSBI success across the enterprise.??From the lack of commitment springs an unwillingness to dismantle (harness?) shadow IT organizations (Gallo, 2018).??Additionally, a lack commitment from leadership limits both long-term investment and organizational commitment to the curation of trusted data stores (Gallo, 2018; McCartney, 2016).??Finally, when everyone is empowered as an analyst, there is a risk that no?one?is in charge of interpreting and managing information centrally.??The need for centralized information interpretation presents the barrier to the organization’s ability to see the big picture across functional-area views (Marr, 2016).
Adoption management is a strong enabler of successful SSBI implementation (Guillen, 2017).??The technical feature of auditability through usage metrics enables leaders to assess, monitor and act in ways demonstrating consistent commitment to SSBI as an enterprise strategy. To further enable SSBI success, organizations need to maintain a centralized analytic capability in addition to SSBI (Marr, 2016).
IT-department support
Although SSBI tools promise to reduce the workload of analytic requests dependent upon IT-departments, implementations of SSBI programs generate new demands on IT-departments, which must be managed.??In particular, there is the continued demand for time.??First is the factor of?speed-to-value time, which is the time it takes to create complete and consistent data sets (Gallo, 2018).??Second is the?morph speed, which is the time it takes for IT to add tables and columns to existing data stores and to modify semantic layers (Gallo, 2018).??Third, as SSBI is adopted, there is a need for IT to continuously educate users, and to deploy and maintain SSBI tools.??The unexpected dependence on IT to implement, manage and maintain SSBI capabilities is a potential barrier to SSBI success.
Two interesting enablers emerge from our survey that support the implementation of SSBI.??First, the selection of?embedded analytics?within existing or familiar platforms selected for SSBI implementation offer a compromise to both technical and non-technical users, which help to balance the ongoing need for IT support with the business-user’s need for IT-controlled data.??Additionally, by empowering IT users to drive IT governance, the use of SSBI within the IT-department to monitor networks, applications, and customer-support requests is an innovative enabler of SSBI (Zaino, 2017).
Cost and Risk Management
Whereas many vendors claim the benefit of low costs of implementation and ownership for SSBI products, enterprise-wide implementations of SSBI tools carry hidden costs which may act as barriers to successful implementation.??First, often not assessed, there is the cost of ongoing?maintenance?that should be assessed (Zaino, 2017).??Next, dataset redundancy, competing solutions, inaccurate report scope, ineffective report layouts, the lack of systematic testing, and an undefined development environment pose hidden?efficiency?costs (Guillen, 2017).??Hidden?operational?costs may arise from incorrect license allocation, lack of report ownership, and lack of tool usage (Guillen, 2017).??Further, hidden?opportunity?costs lie in wait of undefined prototyping and life-cycles.??A subset of hidden efficiency costs, the problem of run-away shadow reporting systems introduces additional risk and cost to information?security?and management – their dependence on a single person translates to a tendency to break and an inability to scale (Sikes, 2017).??Finally, SSBI systems are typically less scalable than traditional BI ("Self Service BI vs. Traditional Business Intelligence," 2016).?
????????????Key to managing these hidden costs is the use of an implementation lifecycle (Guillen, 2017).??Next, a “fit-for-purpose” paradigm enables the creation of use-case specific datastores that vary the trade-off between trust and agility (Gallo, 2018).??Additionally, data marketplace solutions may offer a middle-ground between shadow IT and IT.??Finally, clearly defined business processes, team structures, environment separations and service-level agreements help to enable successful SSBI implementation (Guillen, 2017).?
Unified Strategy
Related to the need for leadership commitment, the need for a unified strategy supporting SSBI integration and organization-wide analytics is a significant potential barrier to successful program implementation (Sikes, 2017).??The SSBI initiative’s need for strategy can result in the, what one author dubbed “data-extract hell”, where there is an unsustainable influx of data extracts to support certain self-service reporting tools (Underwood, 2016).??Further, the lack of strategic alignment produces uncoordinated efforts, which may result in duplicate, untrustworthy reports.??Both risks increase the hidden cost to efficiency.
To this barrier, several enablers emerge.??First, the strategic alignment of both IT and business-area users helps to reign-in rogue analytic power-users without compromising business-user autonomy (Zaino, 2017).??Related to this, a formalized process for business-user requirements discovery will help the organization to manage rapid, continuous prototyping of tactical and strategic analytics (Guillen, 2017).??Finally, the assignment of a BI/Analytics director will help to coordinate the creation and use of different data stores, to mediate the trust vs. agility trade-off for decision-support, to coordinate IT and business-user demands and support, to train, and to ensure data governance across the enterprise (Gallo, 2018).??
Resource Requirements
Certain resources are required to support the successful implementation of an SSBI program.??These may be grouped as components that the organization must contribute, or capabilities that the technology must contribute.??As with Barriers and Enablers, these resource requirements inform the?inputs?of our program logic model.
Organizational Factors
There are three primary organizational factors that inform the success of an SSBI implementation: (1) how well the organization disseminates information; (2) how well the organization collaborates between departments; (3) and the size of the organization.??
Networked Information.
As an enterprise grows, both the amount of data and the need for actionable information increases.??To support this need, an organization must disseminate information effectively and rapidly across the enterprise (McCartney, 2016). The success of an SSBI initiative not only depends upon this ability, but also necessitates a strategic approach to granting users access to a growing abundance of information, in real-time ("Self Service BI vs. Traditional Business Intelligence," 2016).?
Lateral Collaboration.
Several sources site the need for the IT-department to work closely with business-users, both in reviewing the potential technologies and in maintaining ongoing support (Gallo, 2018; Underwood, 2016).??The primary benefit of this collaboration is the continued assurance of security and governance.??As business-users from across the enterprise interact with SSBI-enabled data, all personnel begin to play an increasingly important role in maintaining enterprise data quality; IT-staff, business users and data stewards must all interact in an ongoing, coherent data governance effort (McCartney, 2016).
Size and Scale.
Finally, an organization’s size may warrant different approaches both to SSBI-technology selection and to the implementation strategy.??Large organizations may opt for a combination of both traditional BI and SSBI tools to meet varied use-cases; small and mid-sized organizations with more budget restriction and fewer use-cases may see the most cost efficacy in opting for SSBI over traditional BI ("Self Service BI vs. Traditional Business Intelligence," 2016).??SSBI systems are typically less scalable than traditional BI.??Additionally, with the rise of block-chain technologies, the days of moving data from external sources to specific data warehouses are numbered (McCartney, 2016).??The organization must consider together the growth plan of the enterprise, the flexibility of the technology to scale, and established industry best-practices along immediate, intermediate and long-term timelines.??
领英推荐
Technical Evaluation
There is a diversity of technical factors that must be assessed against user and enterprise needs when designing an SSBI program (Table 1).??To evaluate these features, author Jay Anantharaman recommends evaluation of these features along three levels of criteria (2018).??At the first tier, evaluate the functional performance across the BI process of data Acquisition, Preparation, Visualization, Exploration and Collaboration.??At the second tier, consider the needs of the enterprise.??For this, the enterprise must be very clear about its business-users’ requirements.??For instance, how important are offline capabilities, mobile access, and the ability to comment???What export formats are required vs. available???Third, the technology should be evaluated for its ability to connect to both an existing and future-state solution stack.??It should come as no surprise that there is no one-size-fits-all solution; organizations cannot afford to assume that what works for similar organizations will work for their needs.
Outcomes and Impact
According to the W.J. Kellogg Foundation’s?Logic Model Development Guide, most logic models lack specific short- and long-term outcomes that predict what a program will achieve several years down the road (2004).??It is therefore imperative for initiatives and projects to specify program milestones throughout the program’s design process.??Outcomes?are the specific changes that impact participants’ attitudes, behaviors, knowledge, skills, status or levels of functioning.??Outcomes may be short-term, attainable within 1-3 years, or longer term, achievable within 4-6 years.??Impacts?represent a broader, more long-ranging systemic changes to the organization within 7-10 years.??Together, Outcomes and Impacts help to inform the?Activities?that progress an initiative from design through implementation and sustainment.
Outcomes
Our survey of SSBI reviews indicate the prevalence of several outcomes to business-users.??First, Business users are able to filter, segment and analyze their data (Sikes, 2017).??Second, they are empowered to create their own reports to answer specific questions and analyze specific data (Zaino, 2017).??Next, users have the data they require to make informed decisions, instantly ("Self Service BI vs. Traditional Business Intelligence," 2016).???Everyone can build dashboards and reports, or run ad-hoc queries as needed (Maimone, 2018).
Impact
If users have the data and the tools they require to make informed decision, then we can expect the following changes to occur across the organization.??First, information is timely and trusted (McCartney, 2016).??Next, semantic layers provide business-friendly access to complex databases.??Data is extracted, organized and prepared to enable forecasting models on thousands of predictive variables measured over millions of data records (Maimone, 2018).?Finally, predictive analytics inform a better understanding of risks, and drive insights that capture opportunities based on customer behaviors (Maimone, 2018).?
Recommendation
It is recommended that leadership further pursue knowledge through the development of a concept proposal for the SSBI program implementation, as well as through a formal risk assessment, technology-readiness assessment, and business-case analysis of SSBI technologies.??Further, technical requirements must be formally captured and developed to inform the second tier of technical-selection criteria.??The continued development desired impacts and outcomes will help leadership to frame the activities supporting product implementation.??Finally, the identification of potential business-users and their data dependencies within an SSBI framework will help to inform the activities supporting both implementation and change management.??Together, his knowledge baseline will help to guide leadership’s decision of how best to leverage SSBI as a growing capability within our organization.
Objectives
The implementation of an SSBI program must satisfy the following objectives, which are prioritized by leadership:
Objective/Rationale
Increase Visibility
An effective SSBI implementation will centralize activities across operating departments within a transparent environment
Inform Strategies
An effective SSBI implementation will transform signals into operational, tactical and strategic-level decision support
Enable Growth
An effective SSBI implementation will support an executive-level view of the enterprise as a single operation, which is vital to investment and partnering certifications
Simplify Reporting
An effective SSBI implementation will simplify financial reporting by accommodating various formats across accounting and business-certification processes
Justification
The implementation of an SSBI program satisfies the following criteria, which are prioritized by leadership:
Justification/ Yes/No/ Details
This project is a revenue-making opportunity |Y| SSBI enables executive views supporting potential-partner decisions to invest
This project will reduce operating costs |Y| SSBI helps to reduce operating redundancy of efforts through information transparency
This project will increase quality of service |Y| SSBI integrates with data-sets to inform performance monitoring and improvement
This project will provide new capability |Y| SSBI enables an organization-wide analytic approach that we can bring to our customers
References
Anantharaman, J. (2018, February 13). Tableau vs power BI vs. SAP lumira vs. SAP analytics?????cloud; Which self-service BI tool is the best? Retrieved from????????????https://visualbi.com/blogs/business-intelligence/tableau-power-bi-sap-lumira-sap????analytics-cloud-detailed-comparison/?
Brown, M. S. (2016, December 30). Why self-service analytics won't replace data analytics?????????professionals; May help them. Retrieved from????????????https://www.forbes.com/sites/metabrown/2016/12/30/why-self-service-analytics-wont??????replace-data-analytics-professionals-may-help-them/#6fec71823d92
Gallo, J. (2018, April 24). Self-service BI: Barriers, benefits, and best practices. Retrieved from???https://tdwi.org/articles/2018/04/24/bi-all-self-service-bi-barriers-benefits-and-best??practices.aspx?
Guillen, J. (2017, September 12). The hidden costs of self-service BI initiatives. Retrieved from??https://www.blue-granite.com/blog/the-hidden-costs-of-self-service-bi-initiatives
Hertz, I. (2018, April 18). 5 advantages of self-service business intelligence. Retrieved from????????https://lab.getapp.com/author/ilanhertz/
Logic model development guide. (2004). Battle Creek, MI: W.K. Kellogg Foundation.
Maimone, B. (2018, April 30). A pure play on self-service big data prep and analytics: Wait for???smarter valuation entry point. Retrieved from https://seekingalpha.com/article/4167424??????pure-play-self-service-big-data-prep-analytics-wait-smarter-valuation-entry-point
Marr, B. (2016, October 25). Why we must rethink self-service BI, analytics and reporting.??????????Retrieved from https://www.forbes.com/sites/bernardmarr/2016/10/25/why-we-must??????????rethink-self-service-bi-analytics-and-reporting/#781d83172618
McCartney, A. (2016, August 04). Self-service BI success depends upon data quality and?governance. Retrieved from https://www.dataversity.net/self-service-bi-success-depends?upon-data-quality-governance/?
Pilkington, J. (2016, September 01). Dynamic duo: Self-service BI and data preparation.??Retrieved from https://www.dataversity.net/dynamic-duo-self-service-bi-data-preparation/
Self service BI vs. traditional business intelligence. (2016, September 23). Retrieved from????????????https://www.bistasolutions.com/resources/blogs/self-service-bi-vs-traditional-business??????intelligence/?
Sikes, N. (2017, February 6). An introduction to self-service business intelligence. Retrieved???????from https://www.domo.com/blog/introduction-to-self-service-business-intelligence/?
Underwood, J. (2016, December 12). Self-service BI governance and security risks. Retrieved?????from https://www.jenunderwood.com/2016/12/12/self-service-bi-governance-security?????risks/?
Zaino, J. (2017, January 02). Self-service business intelligence is big, but is it for everyone??????????Retrieved from https://www.dataversity.net/self-service-business-intelligence-big-may???????????not-everyone/
Technical Factors
Note:??This list represents a survey of the most desirable technical features that support both end-user and enterprise needs for SSBI.??An organization must evaluate all technical features based on three levels of criteria: (1) how well does the tool support each phase of the analytic process; (2) how does it support existing and anticipated business-user requirements; and (3) how well does it interconnect and scale with the existing technology stack??
Capabilities/Source