Guiding principles for data driven organizations

Big data has received no shortage of hype in the past few years, but successful implementations are relatively thin on the ground. This post will aim to provide you with a few tips to help you get started, and ensure you set off in the right direction, courtesy of a couple of guides on how to use data effectively.

The first comes from a recent paper published by British data science company Tessella. The paper provides 5 key tips to help you get the most out of analytics:

  1. Focus on your business outcomes – Successful analytics programmes start by identifying what the business is trying to achieve and what decisions must be taken to reach those goals. Only then do they assess what data and technology are needed to inform those decisions.
  2. Think long-term, but focus on quick wins – Many data projects fail because they are too big and take too long to deliver value, leading senior teams to lose interest. Data projects must have a pragmatic execution plan, with milestones designed to demonstrate early success. The first data project plans should focus on multiple, smaller projects, run with agility, to deliver the fast actionable results and rapid-fire value that will win over senior teams.
  3. Who, when and how -Data success requires an understanding of who will use the data, when the information is needed and how they engage with the insights being provided. By doing so, the resulting insights are presented in an appropriate manner for the decision maker.Project outputs need to be used by all sorts of people: it may be a data visualization for an expert in drug chemistry or oil well drilling, or it may be a mobile app which presents complex analytics of multiple health metrics as a simple text recommendation. Getting it wrong may mean missed opportunities, lost customers and disillusioned staff.
  4. Silos out, collaboration in -True business transformational data projects transcend traditional organisational boundaries. Companies need to adopt newly evolved structures, creating a culture where data scientists are in direct contact with the business functions, the IT departments and the communities to which they are providing insights.Teams must be led by someone with a strong understanding of the both business context and technical challenges, these are the vital ‘translators’ who can speak the language of the business and data scientist.
  5. Take a scientific approach – Many analytics strategies fail because they put technology first; Invest into an analytics platform, a black box, which may rapidly identify trends in their data sets. However, these correlations may not be meaningful in a business context. To deliver the effective insights, the reasons for these correlations need to be fully understood.

Original source: The Horizons Tracker

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