Today, data is like air – it’s everywhere. In fact, big data is such an abundant reality, that the measure of success lies in what a business does with it. Some of the biggest companies in the world today have enabled themselves into becoming data enterprises and have consistently made business decisions that result in their growth. Companies like IBM, Amazon, Facebook, Google etc., have built a premise for gathering, analyzing and providing data solutions through multiple ways on the internet– such as providing customer solutions, creating data directories or using data to strengthen natural language processing (NLP) so technology can serve people better online. Such is the power of Data-Driven Decision making, or DDDM.
When it comes to decision-making in business, there have been two persistent paths that leaders have walked on: One of Intuitive decision making, and the other; Data-Driven decision making. There used to be a time where the free-flow of data or access to it, was both very limited and time-consuming. The channels for data mining were incredibly expensive and difficult for everyone to attain. Business leaders often had to depend on instinct for decision making. While many speculate over the reliability and authenticity of that “gut” feeling, it has undoubtedly been the preferred method of decision making for some of the greatest entrepreneurs today. Intuitive decision making famously contributed to Google’s Sebastian Thurn, when he thought of self-driving cars in a time where there wasn’t enough data, maps or infrastructure available to particularly back the idea. In fact, a KPMG study conducted in 2016 revealed that of 2,200 CEOs surveyed, only one-third of them trusted data analytics to make critical decisions, as they believed that the kind of analysis, they required was more of a human judgement than machine algorithms.
Instinct, without a doubt can be a powerful influencer in successful decision making, however, businesses today survive on quick decision making, an ability that is strongly promoted through data powered insights. In the past two decades, people have constantly added to chatter on social media, and brands can quickly tap into this data to validate their ideas and support their instincts. Howard Schulz for example, the founder of Starbucks, relies on both predictive data and financial analysis as fundamental deciders of business growth, he has also admitted to leaning on instinct when it comes to building stores, product innovation and brand culture. The availability of smart technologies have further helped process data into meaningful insights in a timely and cost-effective manner. Therefore, data has helped businesses evolve and look at decision making very differently.
What DDDM does, is places data at the center of decision-making, as it provides hard facts, metrics and quantifiable insights that eliminate biases and inconsistencies when making a critical business decision. Data empowers brand departments with accuracy, helps them compete with other brands in the space and reduces business costs. In fact, many a time, data sits on dashboards and is underutilized or revisited. If historic data is sought after correctly, brands can tell what has worked for them in the past, observe trends, sentiments, journeys and behaviour, and apply those insights for future business decisions and operational processes. DDDM eliminates a large margin of “risk” that intuitive decision making can’t particularly validate or quantify without the backing of verified data.
We live in trying times, where customer-centricity is hard to truly define without the focus and cushioning of adequate data analytics. When working with DDDM, brands can leverage insights that can help understand what works with customers, what doesn’t and why. Brands can then evaluate the performance of the campaigns, products or services, sales and strategize winning opportunities, but also, prepare for sudden shifts in the market, prepare for threats and even direct crisis management efforts. The first step would obviously be to invest in robust data analytics software that helps prioritize data that resides in silos. If quipped with powerful artificial intelligence and machine learning capabilities, analytics technologies work to churn data into valuable insights for brands to consume, evaluate and execute stable business opportunities. When it comes to data-driven decision making, real-time actionable insights are the key to success.
One of the biggest reasons for investment in big data analytics other than building customer relations, is the reduction of operational costs. A 2017 survey conducted for the Harvard Business Review studied 1000 corporate executives who chose to incorporate DDDM to reduce expenses. 49% of the executives saw monetary value in incorporating data-led processes. The firm that conducted the survey deduced from their study that it is now a norm to make use of big data to improve operational efficiency, and real-time data analytics to make informed decisions rapidly. Today, that norm has evolved into an ecosystem where data is utilized to strengthen AI-technologies and machine learning abilities to build smarter architecture for brands and their customers.
For instance, Amazon uses machine learning and data analytics to build its ‘product recommendation’ engine. This engine uses data to make product recommendations to customers based on their purchase history and search patterns. According to a 2017 McKinsey study of the effects of this engine, it was discovered that 35% of the purchases made by Amazon customers were sourced back to the recommendation engine.
The perks of DDDM are that brands can spot patterns which are linked to accountable data, which in turn are used to make steady brand strategies. It also improves the speed of decision making in an organization and its multiple functions quite rapidly as well as leave a lot of room for improvement. It is also crucial to remember that data is – and should be consumed by all functional departments in an organization, because data-driven decision adds an intrinsic value for organizational growth. It also creates a unified premise for strategic thinking that can draw out the value of data. When every aspect of the organization is making decisions based on data, it eventually leads to a brand’s success and growth.
