Use data to make the right decisions

Use data to make the right decisions


Jikku Joseph

Change is inevitable – in every industry. It’s how businesses adapt to change that leads to success or failure.

But adapting means rethinking how decisions are made, and anyone in business knows just how hard it is to consistently make the right decisions: You can’t hide your head in the sand like an ostrich, but you also can’t take a radical turn and risk everything on a whim.

So, how do you make informed decisions that will create or capitalise on new opportunities, while still allowing the business to benefit from existing operations? How do you predict and plan for future trends – real trends, not pie-in-the-sky theories?

The answer is data – harnessing bits and bytes to give us information that we didn’t know about, or hadn’t thought about in a particular way. In the past, this was easier said than done: Collecting, formatting and analysing data used to be an arduous and time-consuming task.

Thankfully, advancements in technology have made this process easier and more powerful than ever before.

Here are four ways that data can be used to make your business more agile.

You’ll know more about your customers. How much are your customers spending? Where are they spending? Harnessing transactional data can answer those questions. Locally, banks and fintechs are using artificial intelligence (AI) to make sense of consumer spending data.

A business could, for example, see where else its customers (as a group) are shopping or where competitors’ successful stores are located.

You’ll prevent unconscious bias (if you’re careful). We’re all guilty of making decisions based on personal beliefs and prejudices – something that psychologists call ‘unconscious bias’. Unfortunately AI systems are trained on historic data which could include social inequalities or biased human decisions.

This can be highly problematic in an organisation where one uses AI systems to make decisions. One way we can prevent biased algorithms is to involve more individuals in the algorithm development process. To do this, it is important to present the training data in a way that any person in any department can interpret and understand, whether they’re a data analyst or not.

This allows more people in more areas of your business to contribute to ensuring that the predictive algorithms are built to be predictively powerful, while accounting for known biases.

You’ll be more cost-efficient. How do you decide which areas of your business might benefit from better marketing or other interventions, or which areas might need to be reined in or even discontinued? Good data can help.

It is important to find those measures that will be early indicators of success or failure, called lead indicators. For example, a company selling a product online could use the number of site visits coming from organic search engine traffic to determine whether there is demand for what it’s selling.

Objective information like this can be tremendously useful when you’re making tough decisions that affect costs within your business.

You’ll know what’s coming. Businesses that monitor consumer trends will be ahead of their competition. With useful data and/or predictive AI systems at your disposal, you can better track trends, spot opportunities and create meaningful customer experiences.

Companies like Facebook’s $1 billion acquisition of Instagram in 2012 was made based on Facebook’s own data that showed an explosion of Instagram users who were sharing images with their Facebook accounts.

Netflix uses predictive models not only to recommend personalised choice but also to determine the new content that it creates that we will want to watch.

There’s a saying that the only thing constant in life is change. The Covid pandemic has proved just how true this is. If you can harness all the data at your disposal to inform your business decisions, you will be far better positioned to cope with an uncertain future.

Jikku Joseph is managing director at 22seven.

*The views expressed here are not necessarily those of IOL or of title sites.


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