Deliberate and Decide with Data

Deliberate and Decide with Data

Do you have an experience where you search for a shoe online, and next you see related shoe advertisements popping up even after you exit the search? Or when shopping online, you get a helpful message that ‘you might be interested in’ along with similar or associated products/services?

How do businesses arrive at such suggestions?

The Vs of Data

Every business collects data in significant volumes, with great variety and high velocity, to earn more insights about their customers. However, the veracity of the data is essential to add value to the analytics.

With the availability of large amounts of data in a short time and with data that has the power to drive economics, innovations in ‘big data’ are gaining importance.

Data Products

Any digital product or feature can be considered a ‘data product’ if it uses data to facilitate a goal. Data products are born when algorithms are applied to the data to analyze, infer or predict. In his article ‘Designing Data Products’, Simon O’Regan lists clear examples of data products by type: raw data, derived data, algorithms, decision support, and automated decision-making.

Making data available in the form they are collected or by modeling it with some algorithms requires you to make the decisions and actions. Evolving further, dashboards and visualizations aid decision-making by presenting to you relevant data in a readable form.

For example, the home page of a digital newspaper is a data product if the news items featured on the homepage you see are dynamically selected based on your previous navigation data.

Further, prescriptive analytics combines data with AI/ML that automates decision-making. Here’s another example: the ‘Faster route available now’ message from Google maps is also a data product. It applies your current location and traffic information on your destination details entered earlier and helps you to decide on the fly.

Data-driven decision

More and more businesses are increasingly investing in collecting data, creating data products that add value, and making decisions based on the data. Data analysis powered by modeling algorithms and

AI/ML significantly impacts decision-making.

Data-driven decision-making (DDDM) is the process of using data to make informed and verified decisions. When data is available in the form of dashboards and recommendations, companies can use the data to make less-biased decisions, more aligned to business needs to drive greater business agility, thereby saving costs. Ongoing use of data to make decisions propels more decisions to be data-driven and, in turn, initiates more data product creation to drive further decisions — all in a cyclical manner.

After hundreds of Starbucks locations were closed in 2008, then-CEO Howard Schultz decided to take a more analytical approach to identify future store locations. Starbucks now partners with a location-analytics company to pinpoint ideal store locations using data like demographics and traffic patterns. Starbucks uses this data to determine the likelihood of success for a particular location before taking on a new investment. The organisation also considers input from its regional teams before making decisions.

According to S&P Global Intelligence, more than 44% of individuals across industries say strategic decisions in the company are data- driven. Google’s ‘people analytics’ mines data of thousands of employees and their human interactions to create and develop training programs for their leadership teams. Amazon’s sales drives are driven by data analytics of their customer’s previous purchases and sales behaviors.

To reap the benefits of data analytics, it is vital to look for patterns, visualize the data, and, more importantly, tie the results back into the data. This will ensure more and more serious decisions are backed by data and allow business leaders free to focus on furthering business development.

Good data doesn’t guarantee good decisions

While all the data, algorithms, AI/ML, and tools can provide detailed insights and recommendations for making decisions, it is important to discern that the analysis is as good as the data served. When the data is biased, the inferences will lean in that direction.

Basing employee promotions on data analytics after mining previous human decisions, which might have a personal bias, will lead to generating analytics that may not be accurate. In an ideal case, it is good to strengthen one’s decision — an investment decision or the employee promotion decision or buying real estate, or any other — with the data analysis and use one’s conviction to take further actions.


Data is the new oil. Mining data and creating data products will give businesses the freedom to take action faster, with confidence, and more proactively.

While data-driven decision-making has many benefits, it is always good to start small and build the data products to fuel more significant decisions. Hilary Mason, founder of Fast Forward Labs says, “Data is a tool for enhancing intuition”.

Power your decisions with data. Enhance your decisions with deliberation.

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