Dayo Aderibigbe, Product Operations Manager at Google, tells us about the use of Data for Strategic Decision Making in Business. Here’s a summary of the latest Google MasterClass on Thursday, December 10!
Dayo Aderibigbe, Product Operations Manager at Google, is currently working on Google One, a cloud storage subscription product, and Google Play Products. He focuses on Strategy, Operations, Customer Experience, Partnerships Operations, and Launch Management. Then, his job is the perfect link between Data Analyst and Executive.
How to use Data in Decision Making?
First of all, an efficient use of Data in Business is implying the consideration of the four following characteristics:
- Facts versus Feelings: Making a good business strategy relies fundamentally on being objective and fact-based, and avoiding recommendations based on our own feelings;
- Storytelling: A huge part of a good business strategy is influenced by storytelling: How do you tell a story regarding the data? What is the narrative scope and objective?;
- Reveal insights: As new opportunities always pop up, you should find the right way to transform an insight into an action;
- Make concrete decisions: Leaning on the facts is essential to build good hypotheses that will be tested in the process. If they are turning wrong, they can always be updated and refined, to come up with a more relevant hypothesis. One thing to remember is that decision making, based on the hypotheses made, is always impacting business outcomes.
What should we consider when using Data in Decision Making?
Even though analyzing data is important, their synthesis and use are critical. On the one hand, you should keep in mind that you should always seek to formulate the right question, according to the available data. On the other hand, you should think about how you are going to synthesize all of this information to identify the source of the problem and find a proper solution.
Creating an argument and an effective story using data are also a big part of a good business strategy. There are multiple ways to scan data, to build an approach and to sell a story or an idea.
Laying out options and sizing the opportunity, impact and tradeoffs are steps that cannot be underestimated -- because sometimes, after testing the first option, we can realize that it is not as impactful and relevant as expected. Thus, it is really important to come up with different options and consider them before jumping into data analysis.
How do we efficiently evaluate problems?
First step: Understanding the problem
Understanding the problem actually takes 40% of your time. A proper understanding will avoid you spending too much time on solving a misunderstood problem, doing the wrong research and analyzing more or less irrelevant data.
Besides the data-driven strategy, the best way to understand a problem is talking to people, sharing with your surrounding, and asking yourself:
- Who and where are the right resources in my organisation which deal with this problem?
- Who are my consumers?
- What are they facing?
- How are they going through their user journey?
- What is the relevant data to analyze to capture the problem?
- How do I know that these data are correct?
Second step: Framing the goal
A goal should be quantifiable and specific, with supporting figures. You should ensure focusing on the best possible options and build an overall problem statement that should be solved.
Third step: Structure the analysis
Through the available data, you should be able to:
- Validate the data: Are the data accurate? How can we validate?
- Understand the available data sources: Which data are available to better understand the problem statement? Are the data labeled?
- Set a timing: What time period should we look at? Do we settle a time frame according to a daily, weekly, monthly, yearly or seasonal basis?
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See data nuances: What are the differences between each set of analyzed data?
Fourth step: Synthesize the data and make recommendations
Besides the current situation, you should lay out the different solutions and analyze them to choose the ones with greatest opportunities.
Here are the main steps of recommendation making:
- Policy lever: Define the decision to be made;
- Description: Write details about the decision;
- Financial impact: Compute the costs involved;
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Non financial considerations: Consider brand image, customer experience, and other non financial aspects.
Most of the time, non financial considerations should not be underestimated because they can lead to financial impact in the future.
Fifth step: Implement and measure results
Finally, once the whole strategy is meticulously settled, it is time to implement it and measure results!
Few tips to be an effective Business Strategist
Last but not least, here’s a quick list of the main characteristics you should consider to become an effective Business Strategist:
- Learn how to work through people, collaboration is key;
- Ensure that your project is linked to your organization/company objective to ensure momentum;
- Be flexible and be ready to pivot based on new information.
If this article made you want to discover the Data field, here’s our dedicated Data Analyst prep course!
