Driving decision making

According to Tableau, data-driven decision-making(DDDM) is using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives and initiatives. A culture that encourages critical thinking is needed to enable DDDM. Access to the correct level of data, balanced with security and governance, is required for effective DDDM. The correct level of skills coupled with training and development opportunities for employees to foster data skills are essential.

Both quantitative and qualitative analysis play key roles in DDDM.

• Qualitative analysis uses data which is not in the form of numbers, such as videos, interviews and anecdotes. It’s based on observation rather than measurement.
• Quantitative analysis focuses on numbers and statistics. This type of analysis is measured rather than observed.

Let’s look at a few examples of how organisations are benefiting from DDDM.

Lufthansa Group

The Lufthansa Group competes in the aviation industry. It didn’t have standardised analytics reporting across its 550-plus subsidiaries. By using one analytics platform, it managed to increase efficiency by 30%, improve flexibility in decision making, and increase departmental autonomy. “We’re in a stronger position to create and design our analyses independently and a lot of people now understand the central importance of data for the success of Lufthansa,” said Heiko Merten, Head of BI Applications in Sales.[1]

Walmart

Walmart used a process to predict emergency merchandise in preparation for Hurricane Frances in 2004. Executives wanted to know the types of merchandise they should stock before the storm. Their analysts mined records of past purchases from other Walmart stores under similar conditions, sorting a terabyte of customer history to decide which goods to send to Florida (quantitative data). They discovered that during natural disasters, Americans turn to strawberry Pop-Tarts and beer.

Linda M Dillon, Walmart’s CIO at the time, explained:

By predicting what’s going to happen, instead of waiting for it to happen … trucks filled with toaster pastries and six-packs were soon speeding down Interstate 95 toward Walmart’s in the path of Frances.[2]

Walmart’s analysts created profits by anticipating demand since most of the products sold out quickly.

Critical success factors for DDDM

The Lufthansa Group and Walmart are great examples of how DDDM can benefit a business. What lesson can we learn from them?

Let’s consider some of the critical success factors for DDDM in business.

• Define clear objectives. Define strategy, set clear objectives and key performance indicators (KPIs) to focus on business priorities. You should avoid following industry hype or the next best trend.
• Gather data now. Gathering the right data is crucial for DDDM. Implementing a business dashboard culture in your company is a key critical success factor to capitalise on data you collect.
• Find unresolved questions. To achieve the right goals and objectives, you will need to ask the correct data analysis questions. It will enable you to focus on the data you really need, rather than collecting anything and everything.
• Identify the data needed to solve questions. Identify the ideal data. Ensure such data is accurate and is relevant to address the business problem. Determine if this data can be obtained internally or both internally and externally.
• Analyse the data. Read through the data and analyse it so that you can extract meaningful insights. Next, isolate actionable insights.
• Revisit and re-evaluate data. Have you focussed on the correct category or subclass? Should the data be sliced and diced in a different way to answer the business questions? These are some of the questions you should address.
• Present the data in a meaningful way. Use data dashboards and reports to present your findings in a meaningful way.
• Set measurable goals for decision making. Now you can use your data analysis and findings to answer business questions. Ensure that your decisions are aligned with the organisation’s visions, strategy and objectives.
• Continue to evolve your data driven business decisions.

One final factor is this: Guard against biases. Running your decisions by a team of experts or specialists who don’t share the same biases as you is important. A McKinsey study of more than 1,000 major business investments indicated that when organisations reduced the effect of bias in their decision-making processes, they achieved 7% more returns than usual.[3]

Here are some tips to overcome biases:

• Improve awareness. By just being aware that a bias exists can help limit its impact.
• Collaboration. Collaborate to spot biases in each other’s work. Also, share ideas on how you could overcome biases individually as well as a team.
• Seeking out conflicting information. Ask the right questions to seek out and remove biases.

And remember: You should never stop examining, analysing, and questioning data-driven decisions.

Now that we have looked at DDDM in some detail and noted that it is useful for businesses to use facts, metrics, and data to guide strategic business decisions. You are encouraged to reflect on a time or experience where you have previously used data to inform a critical decision you’ve made? In hindsight, is there a decision you arrived at that you feel would have substantially benefited if you had used DDDM?

Please post your responses in the comments section below. Next, you will consolidate your learning through a short knowledge check.

References

1. Tableau [Internet]. Lufthansa increases efficiency by 30%, gains flexibility and departmental autonomy. Available from https://www.tableau.com/solutions/customer/lufthansa-saves-30-time-gains-decision-flexibility-and-departmental-autonomy
2. Durcevic S. [Internet]. Why Data Driven Decision Making is Your Path To Business Success. Available from: https://www.datapine.com/blog/data-driven-decision-making-in-businesses/
3. McKinsey [Internet]. A case study in combating bias. Available from: https://www.mckinsey.com/business-functions/organization/our-insights/a-case-study-in-combating-bias