Collaborative business intelligence is a hot topic on the list of enterprise imperatives, but the concept itself is not new. Information delivery coupled with improved analysis and sharing of information has been the proverbial holy grail of BI since the dawn of the Information Age.
Clearly, organizations that can utilize BI to make information accessible and actionable among employees at all levels of the organization are in the best position to achieve improved performance. In a February 2009 Aberdeen Group report, Senior Research Analyst Michael Lock uncovered the substantial performance benefits tied to fostering and implementing a strategy around collaborative BI. The report, “Collaborative Business Intelligence: Three Steps Toward Superior Customer Responsiveness,” cited that the most successful companies experienced a 44 percent improvement in customer responsiveness, a 42 percent improvement in employee productivity and a 30 percent improvement in process efficiency. This data suggests that collaborative BI is certainly beneficial. So, why are so many organizations still struggling to achieve it?
In many ways, traditional BI has failed the enterprise. Why else would we still be discussing strategies and best practices around getting “the right information into the hands of the right people at the right time”? Many organizations still believe there is a single tool out there, a magic bullet, that will help solve the age-old problems of data discovery, data integrity, proactive information delivery and information sharing. But, there is no quick fix, and the issue isn’t simply solved with technology alone.
Collaborative BI is a people problem; user adoption is the single biggest obstacle to enablement. Dashboards and reporting and analytics packages have indeed improved to make BI more accessible to the masses. Users now have richer data visualization options and more intuitive interface designs to entice them. However, organizations that follow the classic paradigm where IT predominantly drives the establishment of a single solution for all BI-related needs will find that this model hinders their ability to achieve true collaboration. Historically, BI adoption rates of IT-approved tools have been low, with many users opting to navigate around them to find their own solution to information delivery and analysis. Organizations must recognize that there is a unique human factor and cultural acceptance element in the collaborative BI implementation process that cannot be ignored.
Strategies that Enable Collaborative BI
To truly enable collaborative BI, information delivery and the tools used to visualize, analyze and share corporate data need to be developed with the end user and his or her personal workflow processes and communication inclinations in mind from the start. To do this, organizations need to think strategically and tactically about both the implementation and features that would facilitate collaborative BI in their organization. While every approach should be tailored to unique organization needs, commonalities exist that can assist in shaping any collaborative BI strategy. Some key initial strategies include:
Unite IT and business users. Believe it or not, IT and business users are on the same side; they just happen to view the world a little differently. It is essential to bridge the gap between these two groups in order to foster an environment conducive to collaboration. In some cases, this may require the involvement of senior leadership or the realignment of goals to reward managers for supporting the creation of an information-driven culture and prioritizing the importance of data-driven decision-making.
Bring users into the fold. To improve BI user adoption rates, enterprises must take into account user-defined requirements during the BI tool selection and evaluation process. It is essential to obtain an in-depth understanding of the type of functionality required in a BI solution for employees to do their jobs. The key to success begins with user engagement; interview users about their needs in order to gain insight into what types of tools they need to be more efficient. Identify a pilot group of employees knowledgeable about the business issues they encounter and who possess social capital within their domains that can act as evangelists for the BI initiative. This will minimize the proliferation of “spreadmarts” and increase the chances that employees will actually use the BI tools that have been put in place.
Better decision-making with a cross-functional approach. If organizations don’t want their data in a silo, then the same logic should hold true when it comes to their decision-makers. Decisions impact more than one area of the business, so it would seem appropriate that the decision-making process should reach outside the domain of a single department or even have the ability to span across multiple levels of the organization. More often than not, users may have the ability to see trends across multiple areas of the organization, but analysis remains compartmentalized. While many organizations have embraced a cross-functional approach to project management, they have not embraced the same methodology when it comes to data visualization, analysis and decision-making.
Features that Support Collaborative BI
Business users and other knowledge workers are the intended mass consumers of BI technology. As such, a collaborative solution strategy must emphasize identifying tools with features that tie back to solving user pain points in order to successfully overcome the BI adoption obstacle. The ultimate goal of BI is to increase efficiency, speed the decision-making process and provide employees with a platform to make data-driven versus gut-feel decisions. A number of features present in BI tools can alleviate common user pain points, but organizations must prioritize features based on direct input from their user community. A short-list of critical features include:
Rich data visualization capable of engaging the user. User engagement is one way to address the issue of low BI adoption. From a BI perspective, users are engaged when they are able to freely interact with data that is relevant to their role in an interface that matches their skill set. Rich data visualization options such as compound indicators, bullet charts, scatter plots and trend lines provide users with a better summary of performance while immediately calling attention to exceptions that may have gone unnoticed with traditional, less dynamic information views. Interactive data visualization capabilities need to be embedded into an organization’s BI environment to enable employees to spend less time collecting and assembling data and more time performing critical analysis that supports a culture of data-driven decision-making.
Analysis tools that provide self-service functionality. Better data visualization will naturally lead to an increase in the number of users that want to perform in-depth analysis. Traditionally, the analysis function has been left to a small number of database-savvy power users that embarked on a solo mission to find answers using query-based tools that were not known for being user-friendly. As a result, business users were left to their own devices, frequently relying on spreadsheets to perform analysis because it provided them with self-service capabilities. This compartmentalization does not support a collaborative environment. Business users need to be able to ask and answer their own questions of the data without requiring outside intervention or a technical skill set. Analysis tools need to be integrated with data visualization elements in a BI environment so business users can transition seamlessly from detecting a pattern visually in a dashboard environment to investigating the root cause of the same
data point in an analytics environment.
Integrating enterprise applications for centralized information delivery. With business users more involved in BI purchasing decisions, the odds of a company having more than one (or even several) discrete BI tools in house is becoming a more common occurrence. Couple this with the growing number of enterprise applications, and this makes integrated, centralized information delivery a challenge for today’s enterprise. Organizations need to assess their BI strategy to ensure a healthy balance between providing users with BI tools that meet their specific needs and facilitating an environment that cuts down of the amount of time employees spend searching for data that requires the merging of metrics from multiple BI tools and disparate enterprise applications.
Next Stop: Collaboration
By definition, collaboration means working together, and that is the ultimate goal of collaborative BI – enabling people to work together and share information to make timelier decisions more effectively and accurately. To do this, BI platforms need to center on proactive information delivery in a way meaningful to the user community at large. The key to collaborative BI is a solution that not only satisfies all types of users – regardless of technical aptitude – but also has the flexibility to facilitate productivity and sharing of information through features that solve the day-to-day process and communication barriers experienced by the end users. By creating a collaborative BI strategy that focuses on both cultural and technology-based elements, organizations can overcome the user adoption obstacle that has stymied many companies in the past and established a foundation upon which they can reap the rewards of improved employee productivity, process efficiency and customer responsiveness for the future.
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