Business Intelligence vs. Analytics?

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Enterprise Business Intelligence Strategy

Almost all organisations struggle to turn an Enterprise Business Intelligence strategy into a workable, cohesive operating model for the business stakeholders.

Lack of investment, indifference to data quality and the sheer effort required to make it work mean that many fall by the wayside.

Data Quality?

Good data quality costs money, requires strong discipline and management controls – attributes that most organisations have little appetite for.

Many organisations are lured away from climbing the peak of Enterprise Business Intelligence into “Big Data” and “Analytics” eager to try the brave new world of Data Science where back office statisticians in darkened rooms conjure up algorithms that may solve many of the organisational challenges in a fast paced, ever-changing environment.

Data Science

C level executives fresh from the latest research conference brimming with buzzwords such as “Big Data”, “Predictive Analytics”, “Machine Learning” and Artificial Intelligence call a meeting to announce that the organisation needs to embrace this new and agile way of working.

Teams are setup and architects busily start white-boarding, data scientists are recruited, fresh and eager straight from University.

Never mind the Big Data many companies struggle to get their small data right and stumble at the first hurdle finding that the same issues applicable to Enterprise Business Intelligence are inherent in analytics;

  • poor data quality, governance and control
  • lack of investment
  • core resources stretched on other critical strategic projects
  • expensive outsourced infrastructure suppliers that hide behind their SLA’s
  • IT architects promoting blue sky solutions that don’t actually work in the real world and with obscure acronyms that confuse non technical business users.

Agile – what does it mean?

Many business users are eager to stake their claim to “their” data and wrest control of it from perceived monolithic IT departments only to realise that they lack the skills and knowledge to actually make use or sense of the data.

IT is then re-introduced to the strategy.

Tensions then often arise from the more structured, assured IT approach vs. the “agile” way put forward by the emboldened business teams with little understanding of what Agile actually means (it’s an organisational change management approach and to work best requires onshore collaborative, holistic teams – often disregarding the fact that 75% of core IT resources and skills were off-shored in the last big re-organisation).

The Result

The result can often be $$m’s in wasted platforms, resources and supplier costs. So many organisations embark upon these often complicated and vast projects without having any sort of strategy – a strategy which is often quite straightforward to conceive but the leaders responsible lack the courage to promote it for fear of failure.

The lessons learned are similar across many different types of projects;

  • develop, communicate and agree a clear strategy
  • build a business case to support the investment
  • ensure sponsorship
  • employ resources with expert domain knowledge rather than “data scientists”

The rewards are immense for certain organisations that do use analytics successfully but there are also costs of failure for those that ignore the lessons from past BI and Data Warehousing initiatives.

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