Data and Analytical Strategies: Why Your Data and Analytics Strategy Sucks

Data and Analytical Strategies: Why Your Data and Analytic Strategy Sucks

I recently hosted a fascinating webinar with James Lawther, and wow, did he bring some eye-opening insights! He tackled the common belief that "data is the new oil" and explained why many data and analytical strategies tend to fall flat. Here are the key takeaways that really stood out to me:

1. Understanding the Value of Data

James kicked things off by challenging the notion that data is automatically valuable. Just like oil, raw data isn't worth much on its own; it needs to be processed and transformed into something useful. The good news? Unlike oil, data isn't running out—it’s actually exploding in volume! From just 2 zettabytes in 2010 to a staggering 97 zettabytes in 2022, the growth is incredible, and it's expected to double in the next five years.

2. Challenges in Managing Data

James pointed out some major hurdles organisations face when it comes to data management:

• Complexity: With so much data, figuring out what to do with it can feel overwhelming. Take a supermarket with 40,000 products—it has to juggle pricing, promotions, and even weather conditions! That’s a lot to keep track of.
• Presentation: How data is presented is crucial. James reminded us of the Three Mile Island nuclear incident, where operators were bombarded with 1,900 pieces of information, contributing to a near-disaster. This highlights the need for clear and concise data presentation.
• Timeliness: The speed of implementing data strategies is vital. James shared a thought-provoking story about Albert Einstein, illustrating that as the world changes, so do the answers we can derive from data. If a strategy takes too long to roll out, it might become irrelevant before it’s even implemented.

3. Effective Data Strategies

One of James’s biggest critiques was of the traditional “solution-first” approach, where companies dive into complex models before fully understanding the business problem. Instead, he suggested a more iterative, experimental method:

• Scientific Method: Think of it like running an experiment. Start with a hypothesis, test it out, see what happens, and learn from the results. This cycle of "plan, do, check, act" helps organisations improve continuously.
• OODA Loop: This approach, taken from the US Air Force, stands for Observe, Orient, Decide, Act. It emphasises the need for quick decision-making and adaptability. By moving fast and iterating, organisations can stay one step ahead of the competition.

4. Learning from Evolution

James shared a fascinating story from his time at Unilever that really resonated. Two teams were tasked with improving a soap powder nozzle. One team took the traditional route, using standard engineering methods. The other team? They were evolutionary biologists who embraced a more iterative approach, making small tweaks and testing them out. Guess who ended up with the better nozzle? That’s right—the team that mimicked natural selection. This really shows the power of rapid iteration and learning from small changes.

5. Focusing on Business Problems

Ultimately, James emphasised that the goal of any data strategy should be to solve real business problems. Organisations should start by pinpointing specific issues, gathering relevant data, experimenting, and iterating quickly. This way, data initiatives can directly impact business outcomes and avoid those "white elephants"—large, costly projects that never quite deliver.

James's webinar was a treasure chest of insights on the pitfalls of data and analytical strategies. By focusing on iterative learning, presenting data clearly, and addressing genuine business challenges, organisations can craft more effective and agile data strategies.

So, what steps will you take to ensure your organisation gets the most out of its data? I’d love to hear your thoughts!

You can watch the full webinar here: Why Your Data and Analytic's Strategy Sucks