Using data analytics in lean and agile ecosystems

Dilbert Big Data

Dilbert and Data Analytics – we hope to remove some of the gobbledygook!

We will be giving a workshop at the  Agile in the City conference in Bristol this week, on using data analytics in lean and agile ecosystems. Lean and Agile are often used almost as synonyms, but they are not – they are attempts to optimise value flow according to different criteria, that can be in competition with each other:

  • Lean = Removing waste – but that can remove redundancy and thus flexibility
  • Agile = Increasing flexibility and speed – but that can increase waste and cost

The optimum of course is to try to be both Lean and Agile, but that in essence means pushing any process to the edges of its performance envelope. Analytics have been used to help for some time already, but as computers and data networks get more powerful and cheaper, they are getting more useful in this regard.

In the workshop we will give an overview of the difference between optimising systems for Lean or Agile systems, and how the emerging analytics options can make them more effective, and interwork more easily with each other.

The session has three parts, firstly to look at the difference between Lean and Agile systems, and how each is optimised, and what the tradeoffs are.

Secondly, to understand the main trends in Analytics and what are useful tools for the Lean and Agile approaches:

  • “Big Data” (or more accurately, what does analytics do with a lot of data vs. what was possible before)
  • Social media analytics (finding the signals in the noise – it’s amazing what you can see)
  • Behavioural analytics (engaging people has been a good idea for centuries, but “how” has always been non trivial)
  • Sensors everywhere – The ”Internet of Things” – between the hype and the practical
  • Emerging technologies – machine learning, predictive analytics, AI etc – what are they, what do they do?.

Secondly, to look at how these can be applied in the end to end value chain, using case studies:

  • Innovation & Product Development (Competitor analysis, Innovation optimisation and Value engineering)
  • Sales and Marketing (Social media analytics, CRM and data mining)
  • Operations (Lean vs Agile vs the real world – Tradeoffs, Shifting Optima and Dynamic analytics)
  • Logistics (if it moves, track it – and what happens as costs plummet)
  • Customer service (Predicting customer defection and the game theory of service)
  • Support functions

By the end, participants will be equipped with an understanding of why, where, and how modern analytics can help them, some idea of what tools are possible, and knowing where to get more information.