Importance of big data analytics for Business Intelligence

Business intelligence (BI) remains to rank high on the priority list of most organisations, but “analytics” has the greater star power. BI, which normally revolves around inquiring and reporting, is starting to be taken for given as part of the information facilities. Analytics, on the other hand, is viewed as having higher impact: Aanalytical procedures are typically concentrated on revealing data insights that can provide instant competitive advantages through smarter customer interactions, more targeted marketing campaigns, and less wasteful operations, among other advantages. Business management expert Thomas Davenport described it well when he said “Organisations are competing on analytics not simply since they can– business today is awash in data crunchers– however also because they should.”.

Growing interest in analytics is driving application of newer technologies, such as Hadoop and MapReduce, that permit deeper discovery of huge volumes of raw data, including semi-structured and unstructured information. Standard BI and data warehousing technologies have shown extremely skilled for structured data, however less simple for information and analysis demands that fall outside of anticipated data types and use patterns.

Putting additional stress on extremely managed Business intelligence and data warehousing environments is the popularity of data discovery and visual analysis tools that provide non-technical users capabilities for performing what-if analysis and producing visualizations by themselves– without direction from IT– while reducing the need for power users versed in online analytical processing (OLAP) techniques.

The new technologies have numerous CIOs and C-level business executives taking a long look at their investments in BI and data warehousing and questioning whether, provided the surge of interest in analytics, Hadoop and relevant technologies must take their place.

Contributing to the lure is the concept that Hadoop, based upon open source, is less expensive– at least till the reality of development and maintenance expenses embeddeds in. However, replacement could be a risky strategy. Without essentially reinventing technology wheels already carrying out well in conventional BI and data warehousing environments, Hadoop and relevant technologies are likely to fall short.

Complement rather than contend

Instead of changing Business intelligence and data warehousing, organisations need to pursue a complementary strategy. BI needs analytics, and analytics requires BI. Although OLAP capabilities offer some analytics capability, BI/OLAP systems do not deliver the deeper, more exploratory viewpoint that advanced, predictive analytics such as data mining supplies. Such analytics might assist Business intelligence users explore the “why” concerns surrounding query outcomes and metrics they see in the dashboards and reports provided by their BI systems.

On the other hand, the outcomes of analytics are often hard for users to consume without proper visualization and suitable context. BI systems’ dashboards and performance metrics can assist users comprehend the importance of analytics for their roles, responsibilities and choices.

Do not overlook business/IT stress

The complementary method ought not to stop with technology execution. Organisations have to resolve people and organisational differences. Analytics often triggers tension between IT and business units. IT is used to owning all development and data access and gathering users’ demands at one time. This doesn’t work when business users and data researchers carrying out analytics need to test hypotheses and explore data before knowing precisely what they need.

Organisations should bring together leaders from IT and business devices, specifically marketing, to enhance understanding and cultivate better collaboration.

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