Why incremental delivery is better than ‘big bang’ for your analytics platform

Anthony Doig | Director of Innovation at Modis Australia Posted 22 July 2020

In this digital age, investing in data and analytics is essential. Regardless of their size or industry, businesses that aren’t using data to underpin their decision-making, customer relationships, operations, and employee experience, are missing significant opportunities to improve and get ahead.

An energy utility that lacks in-depth understanding of its customers’ needs and asset operations may miss new product opportunities, continue inefficient manual processes, and compromise the experience it offers customers.

A not-for-profit that doesn’t measure outcomes of its activities may find it hard to continue to secure sufficient government funding.

A large consulting firm that doesn’t track or measure its staff satisfaction levels may end up with high turnover rates and a de-motivated, unproductive workforce.

However, despite the vital importance of investing in an adaptable, scalable and advanced analytics capability, we typically recommend businesses opt for an ‘iterate and grow’ approach over a ‘big bang’ analytics strategy and program.

This article was originally published by Anthony Doig on Linkedin.

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There are some key reasons for this:

  1. Small wins generate momentum.
    As we’ve mentioned here and here, by starting small and creating quantifiable business value, teams can get some solid ‘lighthouse’ projects under their belt, which they can use to showcase the benefits of analytics to the wider business.
  2. Early exploration and business context is essential.
    The nature of data and analytics, particularly with hard problems, requires an experimental approach up-front to create good solutions. Working side-by-side with business SMEs when exploring and experimenting can be a massive boost to your chances of delivering great analytics outcomes. Your solution and approach may change along the way, but an incremental and experimental approach will help you either deliver what the business actually needs, or identify initiatives that need to stop.
  3. Change is hard. Change with data and analytics can be harder.
    Like we said in this blog, changing how people work using data and analytics is hard. Giving employees the chance to familiarise themselves with analytics, in their own context and language, allows them to see how it can improve their day-to-day work. Senior stakeholders will likely be skeptical about a solution that threatens their team’s years of experience. However, when they can see the ‘art of the possible’ and new opportunities for productivity, safety and risk, which they can actually drive, they will start to realise the benefits.
  4. Analytics technology moves quickly – only build what you need.
    Advanced analytics technology is essential for success, and changes rapidly. Some organisations fall into the trap of building out large analytics technology stacks before any outcomes are delivered (see this blog). As the technology improves quickly, the plumbing you built six months ago will likely be out of date, slowing progress. We recommend you only build the bits in your analytics platform that you need right now to solve business problems. Start small, have a feature roadmap, and build out your platform over time – based on what you need at the time, and what’s working for your particular business.

At Modis, when we work with our clients to deliver analytics solutions, we help them define their destination and goals, plan the path to get there, and start delivering small, rapid wins that bring the organisation on the journey. If you’re interested in learning more about data and analytics and the path that is right for your business, please don’t hesitate to get in touch.

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