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Implementing Self-Service Business Intelligence

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SSBI x300by Jelani Harper

Self-Service Business Intelligence (SSBI) puts the power of analytics in the hands of end users to create their own reports and analysis of the data sets they want, on an as needed basis. The goal is to utilize Data Discovery and other BI tools to reduce IT’s involvement and expedite information to business users by delivering what Gartner refers to as “faster, more user-friendly and more relevant BI.”

However, a plethora of concerns must be addressed before business professionals with a pedestrian knowledge of analytics can readily take advantage of the bevy of dashboards, interactive visualizations, in-memory, and mashup tools that are contributing to the movement towards the simplification of BI.

The successful implementation of SSBI requires accounting for issues of governance, disparities in user ability and need, organizational structure, and hybridization of a centralized and decentralized approach. The role of IT is pivotal for nearly all of these concerns and while ultimately reducing its involvement in provisioning data, is just as valuable in its role as facilitating it for the business.

Enforcing Governance

With end users largely able to create their own reports and access disparate data sources, SSBI can potentially complicate issues of Data Governance. Unlike merely accessing data in a conventional warehouse, self-service users can determine their own data sets outside of the warehouse and exercise high levels of autonomy defining and manipulating data, while seemingly bypassing conventional governance measures. Fairly common governance complications of self-service BI and ways to ameliorate them include:

  • Poor Data Quality: Inconsistent definitions are one of the principle reasons for poor data quality with self-service tools, since respective sections of an organization have different definitions for the same term (such as customer). This issue can be avoided by creating definitions based on key performance metrics at the policy level that are consistent on an organization-wide basis. Doing so provides a necessary limitation on user autonomy while reinforcing proper governance as well as conventional self-service benefits.
  • Data Integration: Integration problems arise when data is not properly formatted before users attempt to aggregate or share data. This occurrence can be lessened by configuring self-service tools with predetermined analytics requirements, report templates, and data schema (largely based on business input), enabling users to pick the most appropriate one for their particular needs. Many BI vendors also offer governance software to account for integration and data quality issues.
  • Logical Errors: An extension of the data quality issue, logical errors occur when individuals make decisions (and share data between users and departments) based on erroneous data taken out of context. Governance policies should be established to stratify information at the individual, exchangeable, and publishable levels, all of which require increasing scrutiny and data cleansing before creating action from it. IT monitoring of these tools is recommended to provide an additional level of vigilance.

User Differentiation

Self-service implementation of BI must account for the wide variety of skill levels and requirements of various users, since it implies users will employ these tools with greater autonomy. Whereas preconfigured templates and schema may prove useful for end users attempting to identify trends to impact business decisions, such limitations may prove too restricting for advanced analysts (data scientists) engaged in complex data exploration. Also, more casual users may require substantial training to avoid relying on IT to issue queries and reports. According to industry authority Wayne Eckerson, “The biggest mistake most BI teams make is buying a single self-service BI tool and give everyone in the company access to it.”

Oftentimes, most business user requirements are satisfied with basic reporting and dashboard options, which they can master without extensive training (although some is necessary). Data discovery platforms (typically involving a combination of in-memory analytics, data mashup, and visualization tools) can meet the needs of heavier analytical users as well as those in the business who have a specific requirement for tailored OLAP or ad-hoc querying, providing a happy medium between extremes. According to a report from Gartner:

“Data discovery tools such as those of QlikTech (QlikView), Tableau and Tibco  (Spotfire) do not require a well-modeled semantic layer and are therefore more conducive to rapid prototyping of analytical content that has not already been modeled. Moreover, the lack of a semantic layer, coupled with the built-in performance layer, enables the unfettered drilling that power users want in order to explore detailed data.”

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IT’s Evolution

One of the misperceptions about self-service BI is that by empowering end users, these tools reduce the need for IT. In reality, implementing self-service tools transforms the role of IT from that of formally serving the needs of the business (in which the latter is frequently waiting on data from the former) to offering varied – and significantly prompter – support. In addition to reinforcing governance policies by monitoring self-service tools to ensure users aren’t deriving action from inconsistent data,  IT is responsible for calibrating tools and conducting training for those in need. IT’s evolving role also includes:

  • Organizational Restructuring: Although there is usually a clear (and quite considerable) divide between the business and IT, implementing self-service tools requires a synthesized effort on the part of both departments. Organizations often need to make fundamental, physical changes to their company structure to enable IT to support the business by becoming more involved in its daily use of BI. Restructuring may include positioning representatives from each department in the same physical proximity to underscore their involvement with one another, as well as aligning departmental goals and incentives so that IT has a vested interest in the business achieving its objectives, and vice versa.
  • Decentralizing BI: Traditional centralized BI is largely responsible for the development of self-service tools, since it required a heavy reliance on IT and lengthy waiting times for reporting about historical trends. Implementing self-service BI necessitates balancing this extreme with its opposite, a completely decentralized approach in which each department (or user) has complete autonomy and operates as a silo. IT facilitates such balance by providing the infrastructure in which data is accessed and exchanged between departments, while tempering enterprise-wide (centralized) concerns with local (decentralized) ones. Commonalities – such as governance, security, privacy, etc. – are issued from a centralized perspective but may be adapted to suit the specific needs of local applications. The degree of adaptation should be determined by upper level management and business executives, but is ultimately implemented by IT.
  • Beneficent Vigilance: It is critical to emphasize to end users that IT’s vigilance and monitoring should not be misconstrued as mere governance voyeurism. In addition to providing the infrastructure whereby data is accessed and exchanged, IT’s job in monitoring self-service tools is to ensure optimization and to issue support for analytics, reporting and publishing needs as they arise. These benefits, as well as aiding the accuracy of self-service reporting, reinforce the notion that IT is central to the facilitation of self-service BI and empowering the business.

And Don’t Forget

In addition to reinforcing governance, structuring user autonomy, accounting for user differentiation, and transforming IT’s role from serving business to offering cross-functional support, it is important to realize that self-service BI should not be considered a replacement for traditional BI tools and warehousing. By utilizing a hybrid approach of centralized and decentralized models and restructuring the organization accordingly, self-service BI functions best as a supplement to the conventional methods in which data is accessed more expediently and put in the hands of those who need it most.

In fact, one of the prime benefits of self-service BI is that it allows users to access data outside of the warehouse that is relevant to their needs – which is one of the boons of merging a localized and centralized approach. Meteorological data relevant to marketing, for example, need not be warehoused and shared with other departments with no use for them. Access to different sources is one of the key facets of personalization and automation of processes that IT should supply at the local level, enabling the business to get the relevant data it needs.

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