The transformation of Business Intelligence (BI) from an IT-centric, centralized process to a self-service, decentralized process is clear:
- According to Gartner, “By 2017, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis” (Parenteau et al, 2014).
- Forrester’s top prediction for BI is, “Managed BI Self-Service Will Continue to Close the Business and Technology Gap.”
- Forbes reported, “Visual data-discovery, an important enabler of end user self-service, will grow 2.5 x faster than the rest of the market, becoming by 2018 a requirement for all enterprises.”
What is surprising is the form that self-service BI in the hands of end users will ultimately take. Traditionally, self-service analytics options were based on Data Discovery tools: search, visualizations, dashboards, data mashups, etc.
Advancements in vendors and analytics offerings, however (spanning everything from Machine Learning predictive analytics to conventional BI) are significantly broadening the scope of self-service BI to include structured and unstructured data, data preparation (including ETL), data-centric storytelling, and a host of other capabilities.
Consequently, self-service BI will account for seamless and expedient data integration between any number of data assets within and outside of the enterprise, granting the business user more control over BI than ever before.
Traditionally, the ETL process of transforming and loading data into a warehouse for Business Intelligence was one of the most time consuming aspects of issuing reports. These complications are exacerbated by the size and velocity of Big Data; although it is possible to stream data into platforms with such as Hadoop for BI purposes, such options still do not account for the agility and variation of data types a business user requires for ad-hoc exploration for real-time decision making.
More vendors, however, are facilitating self-service options for such situations by including loading and transformation functionality as part of overall data preparation capabilities. Forrester indicates that such capabilities include data wrangling, a “lightweight set of data transformation, integration and cleansing capabilities built right into the BI tool which are ‘just right’ for the business users. These tools are not designed to replace enterprise ETL platforms, but rather complement them for those occasions when the business just can’t wait.”
The projected impact of these tools is highly significant, giving the business user access to different types of data and integration options while expediting one of the most important aspects of Business Intelligence.
Enhanced Data Discovery
Vendors have equipped Data Discovery tools with a host of new functionality that transcends the traditional capabilities associated with these options, which are available either as components in BI platforms or as standalone alone solutions. In addition to assisting with ETL processes, such tools also come with data lineage and hierarchy generation, simplified user interfaces, automated visual data flow building, data blending, textual representations, semantic auto discovery features, and more. This added functionality helps laymen users successfully prepare data for integration with disparate sources (including those outside the enterprise) and types (including unstructured data).
Although they require the addition of some technical skills, these options provide a degree of automation with the data preparation process and represent the nexus of advanced analytics, Natural Language Processing based querying, visualizations, and user interactivity. These enhanced discovery tools are provisioned by vendors such as Beyond Core, IBM’s Watson Analytics and others, and uncover inconspicuous data patterns. Gartner noted that “Smart pattern discovery facilitates discovery of hidden patterns in large, complex and increasingly, multi-structured data sets, without building models or writing algorithms or queries” (Parenteau et al, 2014).
The efficacy of discovery tools such as interactive visualizations and dashboards has helped to shift the focus of self-service BI from numeric representations to graphic images. 2015 will see the natural progression of this shift from images to data-driven narratives that explicate analytics results. Doing so requires a greater amount of specificity of visualizations and their settings, as well as the inclusion of textual accompaniments of analytics results. Some vendors, for instance, offer explanations of the results of queries. Others include story boards which incorporate narratives to help put the results of analytics in an overall business context to aid in their conviction.
Look for vendors to add to these capabilities in the coming year, while emphasizing an ease of use, specificity, and incorporation of external sources accessed through the Cloud. Storyboard functionality complements the integration process of discovery tools by enabling users to quickly incorporate different sources to get increasingly comprehensive views of their data and its meaning.
Automated Event Triggers
The semi-automated aspects of the data preparation of certain Data Discovery platforms is indicative of the growing trend towards automation in which analytics is utilized to effect action. In these cases—the most powerful of which is evinced through the numerous applications found in the Internet of Things (IoT)—the results of analytics triggers an event that can aid in the business process. Such automation can be as simple as issuing an alert via text or email that requires an employee to do something, or actually implementing that action electronically such as ceasing production on a cell phone tower when there is a default detected with it. In these cases, analytics takes a proactive approach that will increasingly bring value to the enterprise in the coming years.
The IoT will influence the analytics landscape in several ways. Although its presence is currently most prominent in the management of equipment assets via the Industrial Internet, the aforementioned automation capabilities it facilitates place special demand on analytics to account for its enormous quantities of data via real time streaming. Architectural concerns include performing analytics near the source of the data, which enables the results of those analytics—and not the huge quantities of data—to go back to the enterprise or to a Cloud platform, as well as utilizing solutions that prioritize speed. The number of analytics solutions created expressly for the IoT and real-time analytics will continue to grow in 2015; a number of them will utilize the scalability of the Cloud.
The newfound integration prowess of self-service BI options will likely hinge upon the Cloud in several ways. Analytics pertaining to Big Data and the IoT will typically revolve around Cloud access in the coming year. One of the most discernible trends relating to BI and the Cloud is the increasing frequency at which the enterprise will utilize the Cloud for the analysis of data residing on premise. According to Tableau, “In 2015 companies will begin to choose the cloud when it makes sense for their business case, not only because the data is there.” One of the most salient reasons for doing so is to integrate on-premise data with those in the Cloud. Additionally, the Cloud provides an accessible, expedient way to aggregate data from external sources (such as news, competitor announcements, etc.) and to integrate them with that from internal sources. The transition to self-service BI will help the business user to do so with relative ease, granting him or her greater autonomy and utility from a widening body of data.
From IT to Self-Service
The drivers for self-service BI are numerous and include the paucity of Data Scientists, the skills shortages around Big Data platforms such as Hadoop, the consumerizaion of IT engendered by the BYOD trend, and the overall ubiquity of analytics in general. The many advances in Data Discovery tools noted above are not only useful because they provide a greater degree of automation and insight for ad hoc purposes, but ultimately because they help the end user integrate data between sources and locations.
The level of empowerment achieved by such integration (in considerably less time than it takes when it is done with considerable quantities of code and the involvement of IT personnel) is something that business users have not previously experienced, and will bring a renewed emphasis on governance and its accounting for the broad potential of access and manipulation of data that users will soon have. Not all integration tools that expedite the data preparation process accompany Business Intelligence offerings; there are a number of standalone options as well. However, 2015 will definitely see the assertion of self-service BI and the gradual receding of centralized, IT department based models.