In-Depth
Self-Service Business Intelligence: Empowering Users to Generate Insights
Learn four key self-service BI objectives and which information workers will benefit most from such environments.
By Claudia Imhoff and Colin White
[Editor's Note: The authors are leading several sessions at the TDWI World Conference in Orlando (October 30 - September 4), including DIY BI: Self-Service Comes to Business Intelligence on November 2.]
In today’s economic environment, organizations must use business intelligence (BI) to make smarter, faster decisions. Access to BI is what gives companies their competitive edge and allows them to discover new business opportunities. Yet, in too many organizations, decisions are still not based on business intelligence because of the inability of many IT shops to keep up with demand for information and analytics. IT has been stripped down to the barest numbers, even while information workers are demanding more control and faster access to BI and business data.
To satisfy this demand and accelerate time to value, one approach involves setting up an environment in which the information workers can create and access specific sets of BI reports, queries, and analytics themselves -- with minimal IT intervention -- in a self-service BI (SS BI) environment. Self-service BI is defined as the facilities within the BI environment that enable BI users to become more self-reliant and less dependent on the IT organization. These facilities focus on four main objectives: easy access to source data for reporting and analysis, easy-to-use BI tools and improved support for data analysis, fast-to-deploy and easy-to-manage data warehouse options such as appliances and cloud computing, and simpler and customizable end-user interfaces.
This article is an excerpt from our TDWI Best Practices Report by the same title and briefly describes these four objectives (Figure 1) in more detail as well as the types of information workers who benefit most from this environment. Download the full report from the TDWI Web site.
Figure 1: The four objectives of self-service BI environments
Objective #1: Make BI Results Easy to Consume and Enhance
A fire hose of information makes it difficult for information workers to determine where things are going off-kilter, where exceptions occur, or even get a handle on critical situations. SS BI must be an environment in which it is easy to discover, access, and share information, reports, and analytics. Information workers need to personalize their dashboards or have automated BI capabilities so that the information becomes actionable for their particular situations. It must also be in an easy-to-use format and delivered to a device and user interface of their choosing. In addition, information workers increase knowledge content through interactions such as entering feedback on analytic results, models, and other BI results; adding business context on situations; and identifying related information such as external links, meteorological data, and other data that affects business activities.
Objective #2: Make BI Tools Easy to Use
Not only do BI results need to be easy to consume and enhance, but the tools generating the results must also be easy to use. BI technologies have focused on making these technologies easy to use for years and, for the most part, vendors have succeeded in making them straightforward and simple. These features may help even novice information workers select their own reports and create simple analyses.
It will certainly allow technologically savvy users get what they need when they need it. Support for sophisticated analyses, as well as making results easy to publish in the required format, greatly improves the productivity of a company’s information workers.
Objective #3: Make Data Warehouse Solutions Fast to Deploy and Easy to Manage
Self-service business intelligence may mean looking at alternative deployment mechanisms to reduce costs, improve time to value, and support increasingly extreme data processing. Key components of this objective include ensuring that the SS BI environment provides good performance and scalability for simple to complex analytical workloads and high data volumes.
In addition, SS BI must support easy administration and enhancement of the environment in a timely manner.
Objective #4: Make Data Sources Easy to Access
In our interviews, we heard a number of times that if you couldn’t access the data, then nothing else mattered -- whether it was traditional, IT-created BI components or SS BI. However, there is one significant difference with SS BI -- not all the data accessed needs to be stored in a data warehouse. Data external to the data warehouse such as operational and external but relevant data (e.g., weather information, geographic, demographic, or psychographic data) may need to be made available for access by the business community without IT assistance. If some of this data resides outside the warehouse, it means the BI environment must have some means or mechanism to federate data -- bringing it together virtually from different sources for analysis and access.
Information Worker Requirements
To create a sustainable and appropriate self-service BI environment, implementers must understand the information workers motivations, mode of working (e.g., mobile, geographically dispersed, virtual) and, of course, their technological skill sets. We have determined that there are four categories of information workers involved in the construction and use of SSBI environments including the implementers:
1. Information producers are also known as power business users. These users are capable of building BI templates and dashboards, customizing BI components, and publishing BI-related information for use by information consumers. They have a great interest in the self-service environment and explore, analyze, and produce actionable BI analytics for decision-making. Information producers can be business analysts, senior managers, or middle managers. Unfortunately, they are often frustrated by the IT-developed BI environment and may create their own solutions (e.g., using spreadsheets).
2. Information consumers are task-oriented business users who gather information to increase personal knowledge and make decisions. These information workers support day-to-day business operations and generally do not have the time, experience, and/or inclination to produce, analyze, or synthesize information for decision-making. They want to be able to easily consume BI analytics and make operational decisions based on the information presented to them. Examples of information consumers include the general public, customers, suppliers, call-center staff, store managers, hospital staff, and salespeople. These workers are, unfortunately, poorly supported today because corporate, IT-delivered BI analytics may be too complex for them, are not put into a business context, or can’t be made actionable within their workflows.
3. Information collaborators are the newest members of the information worker community. These people improve the knowledge content and expertise of an organization as well as other information workers (especially information consumers). Their knowledge is typically added using collaborative applications and usually involves some form of collaborative and social computing technologies to access and enhance BI analytics with related business knowledge. Examples of collaborative content include feedback and commentary, ratings, tags, sources of related information and expertise, and links or contact information for expert communities.
4. The BI/Data warehouse builder is traditionally responsible for constructing a data warehouse and/or BI solutions. Due to budget, resource, or priority issues, these personnel may be seen as the bottleneck in deploying BI. In an SS BI environment, the BI/DW builder is responsible for providing access to source data (ideally via common business views), developing customizable BI components for use by other information workers, and monitoring the use of SS BI solutions by other information workers. The BI/DW builder usually comes from central IT or the line-of-business IT group. They may also potentially come from the information producer community.
An interesting note about these four types of information workers is that they may not stay within these roles. Any self-service environment must allow these information workers to change roles and characteristics with ease.
Organizational Considerations
SS BI is a very different environment from traditional BI -- the business community takes over many of the roles that IT held. The information workers have their own techniques and, in many cases, their own technologies for accessing data, creating analytic results and reports, and distributing these throughout their groups. An important question becomes: “What procedures and policies are needed throughout the SS BI information supply chain to ensure that chaos will not occur?” There are several areas where new procedures or policies are needed to ensure that SS BI does not equate to anarchy!
Governance must still have a role. How does an organization set up governance in such a distributed environment, and who in the company is responsible for it? With SS BI, the number of moving parts increases significantly, and many may not be under any sort of central control. Despite this, governance does indeed have a role in each of the four objectives. It can be enforced by the use of prebuilt components such as report templates, customizable dashboards, and widgets. Governance should also identify the data sources that are governed versus those that are ungoverned.
The BI/DW builder should access sources of data through a unified business semantic layer to ensure standardization of access and an understanding of the governed versus ungoverned status of the data being accessed. Finally standard ETL processes can be used to control what data is used to populate these environments. There should be a mechanism for user-defined analytics to be brought back into the governed environment (that is, the data warehouse) for use by others once they are vetted and found to be of value.
IT must still have a monitoring and oversight responsibility. SS BI does not mean that IT no longer has a role to play. In fact, IT has the very important role of the BI/DW builder and of monitoring and maintaining oversight over the environment. Although SS BI may empower information workers to do their own analyses, the BI/DW builders must have the ability to administer and manage the infrastructure of the environment. They must have insight through monitoring and oversight capabilities into what the information workers are doing in the SS BI environment.
Monitoring means examining the performance of the environment, ensuring access to sources through the creation of unified business semantic layers, and determining whether sources can be certified as governed. Oversight entails deciding whether a source of data used in an analytic is governed. It also means that the BI/DW builder can see the usage of any BI component, can determine who published it, identify the data sources used, and see who else is using it. They can determine if a particular analytic is quite popular or is now mission-critical.
Summary
Self-service BI recognizes that there are two opposing forces at work -- the need for IT to control the creation and distribution of BI assets and the demand from information workers to have freedom and flexibility without requiring IT help. Companies seeking to implement self-service BI must reach a middle ground in which information workers have free access to data, analytics, and BI components while IT has oversight into the SS BI environment to observe its utilization. This gives the information workers the independence and self-determination they need to answer questions and make decisions while giving IT the ability to monitor the SS BI environment and apply governance and security measures where necessary.
For more information, please download our research paper at the TDWI Web site or attend our half-day course on self-service BI at the TDWI World Conference in Orlando on November 2, 2011.