Traditional business intelligence (BI) and analytics models are being disrupted as the balance of power shifts from IT to the business, according to Gartner. The rise of data discovery, access to multi-structured data, data preparation tools and smart capabilities will further democratise access to analytics and stress the need for governance. Gartner predicts that by 2017, most business users and analysts in organisations will have access to self-service tools to prepare data for analysis.
“Data preparation is one of most difficult and time-consuming challenges facing business users of BI and data discovery tools, as well as advanced analytics platforms,” said Rita Sallam, research vice president at Gartner. “However, data preparation capabilities are emerging that will provide business users and analysts the ability to extend the scope of self-service to include information management, and extract, transform and load functions, enabling them to access, profile, prepare, integrate, curate, model and enrich data for analysis and consumption by BI and analytics platforms.”
“Self-service data integration will do for traditional IT-centric data integration what data discovery platforms have done for traditional IT-centric BI: reduce the significant time and complexity users face in preparing their data for analysis and shift much of the activity from IT to the business user to better support governed data discovery,” said Ms Sallam. “However, specific skills are required. Self-service data integration requires that users master both the technical aspects and the business requirements of joining data together.”
Gartner expects basic business user data mashup capabilities to become mainstream as part of data discovery tools in the near future. Data discovery and traditional BI vendors are likely to respond to this demand and opportunity to add value by extending their own business user data mashup capabilities to include more-advanced data preparation features.
Gartner made a number of further predictions about BI, including:
By 2017, most data discovery tools will have incorporated smart data discovery capabilities to expand the reach of interactive analysis.
As data discovery capabilities are becoming smarter to streamline pattern detection in data discovery, self-service data preparation capabilities are evolving and becoming more capable of semi-automating and enhancing the data preparation activity of data discovery, and making it accessible to a business analyst. The two advances in combination will create a next-generation data discovery user experience that makes advanced types of analysis accessible to a broader range of users.
“Smart data discovery has the potential to expand access to sophisticated interactive analysis and insights to business consumers and non-traditional BI users — the approximately 70 per cent of users in organisations that currently do not use BI tools or have statistical backgrounds,” said Ms Sallam. “New approaches have the potential to transform how and which users can derive insights from data discovery tools. The potential business benefit will lead to a shift resulting in smart data discovery becoming standard features of most data discovery platforms.”
Moreover, Ms Sallam said that this evolution will likely facilitate accelerated growth of the citizen data and make new sources of information accessible, consumable and meaningful to organisations of all sizes, even ones that don’t have extensive advanced analytics skills or in-house resources.
Through 2016, less than 10 per cent of self-service BI initiatives will be governed sufficiently to prevent inconsistencies that adversely affect the business.
End-user clamour for access to business data, combined with IT’s inability to satisfy this need, has manifested in self-service BI initiatives in many organisations. The growing increase in data volume, velocity and, especially, variety has further fuelled this trend. Vendors have responded with mass consumable, broadly deployable, easy-to-use and, often, cloud-based technologies for basic query, analysis and reporting.
Often, these solutions are implemented by business units that have circumvented IT and as a result, they are disposed to analytic sprawl — an inconsistent or incomplete use of data, capricious development of metrics and formulae, and either too-restrained or unrestrained sharing of results.
“As a result of the limited governance of self-service BI implementations, we see few examples of those that are materially successful — other than in satisfying end-user urges for data access,” said Doug Laney, research vice president at Gartner. “This, combined with increasing examples of data privacy and security breaches, along with anticipated instances of public disclosure inconsistencies, will temper businesses leaders’ enthusiasm for self-service BI. From unfortunate occurrences like these, we expect resulting investor and customer blowback for organisations with ungoverned, or loosely governed, BI initiatives.”
To counter these adverse effects, a return to more controlled enterprise BI implementations is expected, or the deployment of self-service BI technologies within a better governed, IT-led project environment. On the technology front, vendors will to continue to play both sides, but more conscientiously — selling simple data discovery technologies broadly throughout their prospects’ businesses, while reemphasising the advantages of controlled, centralised and more-robust enterprise BI technologies.