Business intelligence (BI) continues to rank high on the priority list of most organisations, but “analytics” has the greater star power. BI, which typically revolves around querying and reporting, is beginning to be taken for granted as part of the information infrastructure. Analytics, on the other hand, is perceived as having higher impact:Aanalytical processes are often focused on uncovering data insights that can deliver immediate competitive advantages through smarter customer interactions, more targeted marketing campaigns, and less wasteful operations, among other benefits. Business management guru Thomas Davenport described it well when he said “Organisations are competing on analytics not just because they can -- business today is awash in data crunchers -- but also because they should.”
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Growing interest in analytics is driving implementation of newer technologies, such as Hadoop and MapReduce, that allow deeper discovery of large volumes of raw data, including semi-structured and unstructured information. Standard BI and data warehousing technologies have proven highly proficient for structured data, but less straightforward for information and analysis requirements that fall outside of expected data types and use patterns.
Putting further stress on highly controlled BI and data warehousing environments is the popularity of data discovery and visual analysis tools that give non-technical users capabilities for performing what-if analysis and creating visualizations on their own -- without direction from IT -- while reducing the need for power users versed in online analytical processing (OLAP) techniques.
The new technologies have many CIOs and C-level business executives taking a long look at their investments in BI and data warehousing and wondering whether, given the explosion of interest in analytics, Hadoop and related technologies should take their place.
Adding to the lure is the notion that Hadoop, based on open source, is cheaper -- at least until the reality of development and maintenance costs sets in. However, replacement could be a risky strategy. Without essentially reinventing technology wheels already performing well in traditional BI and data warehousing environments, Hadoop and related technologies are likely to fall short.
Complement rather than compete
Instead of replacing BI and data warehousing, organisations should pursue a complementary strategy. BI needs analytics, and analytics needs BI. Although OLAP capabilities provide some analytics functionality, BI/OLAP systems do not deliver the deeper, more exploratory perspective that advanced, predictive analytics such as data mining provides. Such analytics could help BI users explore the “why” questions surrounding query results and metrics they see in the dashboards and reports provided by their BI systems.
On the other hand, the results of analytics are often hard for users to consume without proper visualization and appropriate context. BI systems’ dashboards and performance metrics can help users understand the significance of analytics for their roles, responsibilities and decisions.
Don’t ignore business/IT tensions
The complementary approach ought not to stop with technology implementation. Organisations need to address people and organisational differences. Analytics often causes tension between IT and business units. IT is used to owning all development and data access and gathering users’ requirements all at once. This doesn’t work when business users and data scientists performing analytics need to test hypotheses and explore data before knowing exactly what they need.
By choosing a complementary approach, organisations will gain the best of both worlds with BI and analytics.TDWI (The Data Warehousing Institute), in partnership with IRM UK, will present the TDWI BI Symposium at the Radisson Blu Portman, in London, 10-12 September, 2012. SearchDataManagement.co.UK is a media sponsor.
This was first published in August 2012