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Here is what I noted from the article “Making data analytics work for you – instead of the other way around” by Helen Mayhew, Tamim Saleh and Simon Williams:

[…] Advanced data analytics is a means to an end. It’s a discriminating tool to identify, and then implement, a value-driving answer. And you’re much likelier to land on a meaningful one if you’re clear on the purpose of your data […] and the uses you’ll be putting your data to […]

[…] the insights unleashed by analytics should be at the core of your organisation’s approach to define and improve performance continually as competitive dynamics evolve […]

[…] Ask the right questions. Clarity is essential and so is focus.

[…] Think really small … and very big. […] Identify small points of difference to amplify and exploit. The impact of “big data” analytics is often manifested by thousands – or more – of incrementally small improvements […]

[…] Embrace taboos […] useful data points come in different shapes and sizes […] Too frequently, however, quantitative teams disregard inputs because the quality is poor, inconsistent, or dated and dismiss imperfect information because it doesn’t feel like “data” […] Recording the quality of data – and methodologies used to determine it – is not only a matter of transparency but also a form of risk management […]

[…] Connect the dots […] Too often, organizations drill down on a single data set in isolation but fail to consider what different data sets convey in conjunction […]

[…] Run loops, not lines […] Best-in-class organizations continually test their assumptions, processing new information more accurately and reacting to situations more quickly […] OODA loop – Observe, Orient, Decide, Act […] OODA plus data amplify the effect and accelerate the cycle time.

[…] Make your output usable – and beautiful […] Analytics should be consumable […] organization will respond better to interfaces that make key findings clear and draw users in.

[…] Build a multiskilled team […] Key team members include data scientists […] engineers […] cloud and data architects […] user interface developers […] You also need “translators” – men and women who connect the disciplines of IT and data analytics with business decisions and management.

[…] Make adoption your deliverable […] the best day-one indicator for a successful data-analytics program is not the quality of data at hand, or even the skill-level of personnel in-house, but the commitment of company leadership.

Article provides a nice overview of how-to with regards to organizational and organized approach to big data analytics with some good examples. Italics mean direct quotes from the article. Reach for it.

 

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