BI packs a punch

by | Nov 4, 2016

BI packs a punch

by | Nov 4, 2016 | In The Media | 0 comments

Edging towards the end of yet another year, the Christmas paraphernalia in commercial districts leaves one in shocked silence at the thought that another year has come and gone. Those who have experimented in agile development practices cannot resist the prompting that the end of a cycle calls for a moment of retrospection.

Meditating on the last year of the business intelligence (BI) industry trends ironically enough finds ‘agile’ thinking has become far more prevalent in BI circles. Companies are starting to realise that traditional BI development life cycles of three to six months are no longer good enough. Information of high quality has become a key ingredient to business success, and therefore, the demand for faster delivery is becoming more pervasive.

However, the insight of those who experimented with agile development practices in BI has started to mature, with the realisation that BI is typically far more complex than application development. Therefore, more disciplined approaches to agile are required in order to effectively scale, and enable shorter delivery cycles to meet the needs of decision-makers for ad hoc decisions. Not only does BI delivery become faster, but disciplined agile BI delivery enables agile business with improved responsiveness to new opportunities or changing circumstances.

Meet and greet

These agile practices make the BI delivery team more user-centric, in a weird way mimicking the realisation of a common business strategic ambition to become more customer-centric. It is this strategic objective that is driving the second observed trend of an increased take-up of big data. The ambition of customer-centricity has been evolving over the last couple of years in SA, but the realisation is starting to hit home that a company can only truly be customer-centric if it know its customer. Multitudes of sources containing fragments of knowledge about customers can be consolidated in innovative ways using big data technology to generate insights never thought possible before. Investment in big data technology exploration or proof-of-concepts has increased significantly this year, as companies are preparing the way for the realisation of their customer-centricity ambition in the next two to three years.

BI is typically far more complex than application development.

Upstream, in the data provisioning value chain, there has also been an increased investment in more mature information management capabilities. Companies are investing in robust master data management capabilities that enable more robust data integrations from fragmented sources. The quality of data upstream is also being measured and monitored intentionally, in order to proactively ensure the high quality of the information downstream provided into analytics for decision-making. All of this is being managed more intentionally, as companies, especially in the financial services industries, invest a focused effort on maturing their information governance practices. Concepts like data compliance, data owners, and data stewards are slowly becoming part of the mainstream business management glossaries.

Downstream, analytics has also seen significant increase in maturity over the last year. BI strategic focuses are moving away from the masses using BI for standard management information reporting or dashboards, and the attention becomes concentrated on key power-users who require high-powered and advanced analytics. They go by various names, such as quantitative analysts, statisticians, actuaries, management accountants, etc. However, these are all just various stage names for the user profile known as data scientists.

These are the users who approach the BI team with new query or view requests almost on a daily basis, and the query result or view is probably only required for once-off use. These are also the users who typically benefit from self-service capability to source their own information, manipulate it according to the business problem they are faced with, generate various analytic views through a large variety of diverse visualisation features in their analytic tools, or even employ some highly sophisticated predictive and prescriptive analytical capability.

Cognitive computing

These users are also the front-runners to lead BI into the reality of true cognitive computing in the near future. Although there hasn’t been real progress in the space of cognitive computing in the last year, curiosity is starting to rise and questions are being asked more often.

All in all, 2016 has been a year of rapid increase in maturity for management of information, as information is becoming ‘big’ – big on the strategic agenda of any company that wants to establish and maintain a competitive advantage in the new digital age, as well as big, as in big data technology.

As far as the delivery of BI capabilities goes, the need for agility rings as true as the sleigh bells of Christmas 2016 that loom ahead. BI is no longer a standalone capability that merely spits out beautiful reports. It is effectively becoming embedded into the competitive DNA of the company, and therefore, agile delivery will become a critical success factor to keep up with the proverbial Jones’s.

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