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Nearly a year ago, an article written for DATAVERSITY™ on Business Intelligence (BI) stated “at some point in the near future BI analysis will simply be another routine aspect of the job for business professionals looking to maximize the use of what will be viewed as a standard resource.”
This prediction was presaged in the context of the increasing assertion of two “diametrically opposed” movements related to Business Intelligence – the prevalence of formal education in this field and the simplification of BI tools that are drastically assisting business users in daily operations.
The reality is that the reliance on data is growing nearly exponentially; virtually any application of data requires analytics or BI to extract meaning and subsequent action. What once appeared as two opposing trends that could potentially cancel each other out has merged into a synthesis in which formal education in the field – in addition to developments in automation, Data Discovery tools, Cloud computing and Big Data – is readily used to simplify the process of leveraging BI for laymen.
BI’s crossroads has become a solitary path paved by the consumerization of (and burgeoning familiarity and comfort with) IT, which Gartner describes as “how enterprises will be affected, and how they can take advantage of new technologies and models that originate and develop in the consumer space, rather than in the enterprise IT sector.”
Education Induces Simplification
BI education includes vendor and organization-wide training from consultants, professional certification, and graduate and undergraduate degrees (including majors and minors). The first two of these three options directly assists end users by providing training with commonly used tools and functions that effectively demystify the process of BI and analytics – oftentimes, certification and consultant training explicitly target the business enabling it to derive greater value from these technologies.
Despite the fact that formal degrees in BI are generally designed for individuals working in IT departments, IT’s role has gradually shifted to offer substantially more assistance (within the requested timeframes) of business users. This shift includes aligning IT objectives (complete with incentives) with those of the business, repositioning departmental locations to facilitate more convenient accessibility for end users, and provisioning mobile devices with the most beneficial applications so that IT has greater contact with and a vested interest in the business’s frequent interactions with BI tools.
Traditional BI utilized a centralized approach in which end users routinely waited for IT to obtain reports of historic data. Current advancements in workload automation and discovery tools, however, have contributed to a decline of the centralized environment in favor of a distributed or hybridized approach which grants users more autonomy and decreased time to insight. Subsequently, IT is no longer at the center of numerous BI requests and instead functions as a facilitator for BI on various desktop, mobile, and Cloud applications that are much more accessible and celeritous for the business, increasing BI’s overall usage and business value.
Sophisticated automation tools are at the forefront of technological advances that are paving the way for the simplification of BI and play a considerable role in sustaining hybrid or centralized BI architecture. Formerly, lengthy querying and reporting jobs which involved time-consuming scripts are now able to be expedited due to self-service and scriptless automation job scheduling. With the proper IT configuration, end users are able to issue on-demand queries in close to real time with some of the most viable applications for BI such as Big Data and data from conventional sources. These queries greatly simplify the integration of BI with other platforms.
The movement towards the simplification of BI is substantially enhanced by the triad of technologies associated with Data Discovery tools, mobile devices, and the Cloud. This combination has spurred BI’s departure from the backrooms of IT departments and placed its capabilities in the front offices and pockets of end users, providing an unmatched potential to access insights on-demand from anywhere.
- Data Discovery: Discovery tools have lessened the need for conventionally lengthy (and time-consuming) BI reports, and significantly augmented them in cases in which they’re essential. Dashboards and interactive visualizations graphically represent data and results from BI in ways in which trends are readily discernible, data mashups and in-memory analytics enable users to quickly query a variety of disparate sources, and search tools offer text-derived analysis of either structured or unstructured quantitative and qualitative data.
- Mobile: Mobile technologies are extremely complementary to discovery tools; several dashboards and visualizations were specifically designed for or are easily modified for tablets and smart phones. Mobile devices bolster user familiarity – particularly due to the widespread Bring Your Own Device (BYOD) policy of many organizations – and provide a degree of accessibility that desktop applications can’t match. Recent increases in the adoption rates of mobile BI mimic the overall trend of embracing mobile technologies for accessing IT.
- The Cloud: Cloud computing plays a major role in the simplification of BI. The Cloud is projected to be the de facto source for Big Data analytics in the coming years, while its low cost and extreme scalability is ideal for accessing BI from mobile devices. Cloud-based BI and cloud applications in general are partly responsible for the consumerization of IT by rendering these services outside of the specific realm of IT departments and making them accessible to laymen.
Big Data as a Driver
Big Data’s impact on the simplification of BI is multifaceted. On the one hand, the sheer amount and variation of Big Data is responsible for the growing numbers of formal education programs in Data Science, underscoring a degree of specialization that is far from the self-servicing ends of discovery and mobile technologies. Nonetheless, Big Data is one of the main drivers of the trend towards accessing analytics and BI through the Cloud.
The number of service providers issuing Cloud-based BI and analytics is increasing due to the realization that it is frequently more cost-effective and expeditious to obtain these insights from third-parties than to procure and maintain the hardware and trained professionals required to do so in-house. These on-demand analytics providers are accessible at the pace of the business and reduce the need for time-consuming, in-house delays traditionally associated with BI.
Realizing the Future
The movement towards the simplification of BI encompasses some of the most pivotal aspects of enterprise architecture:
- Training: The abundance of options for formal education in this field enable professional’s greater access to the skills required to manipulate these tools for themselves.
- Organization: Organizational restructuring so that IT departments are more aligned with the business and its objectives for data use leverages the training of the former where companies need it most – to produce business value via analytics and BI.
- Workload Management: Automation tools with scriptless and self-service capabilities can greatly increase the rate at which end users can derive insights from BI on their own.
- Technological Advances: The consumerization of IT is largely facilitated by the ease of use and convenience of Data Discovery, mobile and Cloud technologies, each of which offers greater autonomy and flexibility for the end user and his or her ability to manipulate BI.
- Big Data: Big Data’s impact on Data Management places a greater premium on analytics and BI, while reinforcing the swift, relatively ease of use of third-party sources for analytics.
Thus, a convergence of important factors related to the enterprise is functioning as the collective impetus for the consumerization of both IT and BI. These factors are also contributing to the growing climate in Data Management in which data is readily used in the decision-making process for everyday activities and to further business objectives.