Member Article

Make better business decisions with publicly available online data

By Or Lenchner, CEO at Bright Data (formerly Luminati Networks)

Or Lenchner, CEO at Bright Data (formerly Luminati Networks), explores how data-driven decision-making (DDDM) can help you make better judgments and unlock growth in your business.

The quantity of data gathered globally is exploding and is predicted to reach 163 zettabytes per year by 2025. In our online interactions, there is no way of getting by without exchanging some form of information. However, the Internet, the world’s largest database – founded on the principles of openness and transparency – is not accessible to all in the same way. Instead, we all see limited, tailored views of it that are based on behavioural, demographic or even anonymous user data.

Thanks to technology, it’s now much easier for organisations of all sizes to bypass these limitations and obtain publicly available online data with which to inform their business decisions. In a survey of more than 1,000 senior executives, PwC found that highly data-driven organisations were three times more likely to report significant improvements in decision-making compared to those who relied less on data.

Keen to implement the principles of DDDM in your business? Here’s where to start: The fantastic foursome of data analytics

For business leaders looking to improve decision-making, the following four types of data analytics practices provide a helpful framework for understanding how data can be used to drive change.

• Descriptive Perhaps the most used form of data analytics in the business world, descriptive data analytics involves using raw data to track performance, see supply and demand and understand who your customer is. This includes monthly sales, conversion rates and customer demographics. It also usually involves data mining and visualisation techniques.

• Diagnostic Slightly more advanced, diagnostic data analytics seeks to find the ‘why’ by identifying patterns and analysing data to comprehend the reasons behind trends identified in the previous step. Business Intelligence (BI) dashboards use this technique to understand the root cause of organisational issues.

• Predictive Unfortunately, no one can see into the future, but one can get close to it. How? Through data analytics, you can equip yourself with the tools to make solid predictions around future sales, revenue and market changes. For this type of analytics, data scientists use data modelling and machine learning.

• Prescriptive Last but not least, this technique involves using findings from the previous three steps to deliver value through problem solving. For example, prescriptive analysis is what your mobile GPS application uses to suggest the best route to reach your destination. Managing risk with data-driven decision-making

Only by implementing DDDM – rather than by relying on intuition or incomplete information – can you fully understand the risks and benefits of each business decision you make and remove a lot of the guesswork. Imagine you’re launching a new product and planning the marketing campaign. Instead of basing your strategy entirely on your current market research, you can use analysis and insights either from previous product launches or from similar product launches that target a similar customer base to inform your decisions. Doing so will enable you to reach smarter conclusions within a shorter period of time.

Beware of bias

To get the best possible results from DDDM, business leaders should always look out for bias – one of the greatest challenges in data analytics. Often, we see what we want to see rather than what the real-time data story actually is. So, beware of confusing correlation with causation or mistaking blips with trends. Businesses should also prioritise data quality and not draw conclusions from limited data sets. We live in a real-time economy, and real-time data sets are essential to ensure that you are advancing on the right course of action.

You can go a long way towards eliminating biases by cross-referencing data from different sources. It’s also worth looking at what data you collect, ensuring it comes from a wide variety of sources and geographies – as long as this doesn’t conflict with your goals.

Prioritising DDDM now

Most companies wait until they find the perfect business idea or product before strategising about data collection. However, it’s never too soon to get started. For example, a start-up that begins its product-to-market strategy with accurate research to determine audience interests and competitor offerings will be in a better position to understand if there is a market fit for their product than a start-up that relies on a ‘gut feeling’ about potential customers out there.

Looking forwards, it’s clear that being data-driven has become non-negotiable for businesses that want to succeed. If your competition is using data effectively, and you aren’t, you simply will not be able to keep up. For this reason, now is the time to apply the principles of DDDM to create a data-driven culture in your organisation.

This was posted in Bdaily's Members' News section by Bright Data .