Public online data can help business leaders make better decisions - The EE

Public online data can help business leaders make better decisions

Or Lenchner of Bright Data

Or Lenchner, CEO at Bright Data (formerly Luminati Networks) explores the ins and outs of data-driven decision-making (DDDM) and tips that business leaders can use to improve their business.

We live in a world that is overrun with data. From what we read, to what we watch and listen to online, there is no getting by without exchanging some form of information. However, the internet is not accessible to all in the same way. We all see tailored views of it, reducing the transparency and open market principles upon which it was founded.

Now, thanks to ever more sophisticated technology, it is much easier for businesses and organisations of all sizes to obtain and analyse openly available online data to inform their business decisions. What was previously a concept of intuition and romanticised as a ‘gut-feeling’ for where a market or sector is heading, is now grounded in more scientific terms and processes.

In fact, a previous PwC survey of more than 1,000 senior executives, found that highly data-driven organisations are three times more likely to report significant improvements in decision-making than those who rely less on data. Although intuition can be a good tool, it would be a mistake to not consider data as a key driver of decision making in business. And here’s why business leaders can now make better and more informed decisions using publicly available online data.

What is data-driven decision making?

It’s a strategic approach that uses data as a base to make informed business decisions. This approach is also referred to as DDDM or data-driven decision making. It involves collecting data based on measurable goals and/or KPIs (key performance indicators). The data is then analysed in order to identify patterns and generate useful insights.

Companies leverage those insights to develop strategies for business growth making data-driven decisions to achieve goals instead of intuition. According to research, leveraging data analytics for decision-making offers several benefits, including: Better strategic decisions (69%); More control over the operational processes (54%); Improved understanding of customers (52%); Cost reductions (47%).

Which types of data analytics are used for decision making?

When it comes to data analytics, there are only four types out there that matter to business leaders looking to improve their business decisions.

  • Descriptive 

Means using raw data to describe a given/current situation. For example, monthly sales or conversion rates for a specific period. Or a demographic analysis of customers. Data mining and visualisation are some techniques used for this type of data analytics practice.

  • Diagnostic 

Seeks to find out the “why”, identifying patterns, and analysing data to understand why something discovered in the previous step is happening. Business Intelligence [BI] dashboards use this technique to understand the root cause of issues in one’s organisation.

  • Predictive 

Analyses past and present data in order to forecast what is to come. It allows companies to predict future sales, revenue, and market changes. For this type of analytics, data scientists use data modelling and machine learning.

  • Prescriptive 

This technique involves taking the findings of the previous three and using it to deliver value, by determining the possible solution to a problem. For example, prescriptive analysis is what your mobile GPS application uses to suggest the best route to reach your destination.

What are the benefits of DDDM?

First of all, the information provided is accurate. Instead of poorly sourced information and typical ‘gut feeling’, leaders can now use real, actionable data to guide their decisions.

Another key benefit is the mitigation of risk. When you base your decisions on cold hard data, you put yourself in the driver’s seat in terms of the risks vs. benefits of each decision you make. For example, imagine you are launching a new product and planning the marketing campaign. Instead of basing your strategy entirely on your current market research, you can collect data on and look at what previously worked with similar product launches. This will enable you to reach smarter conclusions within a shorter period of time.

However, a big challenge in adopting a DDDM is in navigating biases. We’re only humans, and as it often happens, we see the things we want to see rather than what is factually presented. It’s one of the greatest data analytics challenges. But, adopting a data-driven culture means that data is accessible to the right people enabling them to make better-informed decisions. You can eliminate biases by cross-referencing data from different sources or by collecting data via a wide variety of peers, and devices as well as from different GEOs when that does not conflict with your goals.

What’s next for DDDM?

Creating a data-driven company culture has its challenges. Most companies wait until they find the perfect business idea or product before strategising about data collection. However, they need to reap the benefits of data collection early on. For example, a start-up that begins its product-to-market strategy using data will be able to find a product-market-fit by first conducting accurate market research using data, discovering where their audiences’ interests lie or what their competitors are offering.

However, building a company that places data at the core of operations and becomes an integral part of decision-making is key to helping business leader make better informed decisions. And over the coming years, publicly available online data will be an imperative, that will allow businesses of all sizes to take market advantage and make that one-step ahead of the competition.

The author is Or Lenchner, CEO at Bright Data (formerly Luminati Networks).

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