Business leaders are required to make dozens of decisions every day that can impact the company and its key interested parties. While one decision can translate into millions of dollars, another can simply rupture the brand value overnight. However, thanks to technology, firms worldwide are now swimming in vast amounts of data. By employing accurate statistical and analytical techniques, this data trove can produce insights that can help executives drive better and well-informed decisions, thereby making their organizations to emerge as agile, competitive, and successful.
There are primarily three types of methodologies of using data for decision making – descriptive, predictive, and prescriptive. All these data analytic techniques are being widely used by corporate houses across the globe to devise better plans and strategies.
Here’s a quick snapshot of how these tools help in transforming data into prudent evidence-based insights.
This is primarily a post mortem analysis technique. It produces insights on the reasons behind the occurrence of a past event. It mines huge chunks of data into smaller sets, thereby generating a sound understanding of the cause and effect relationship. It throws light on ways of achieving operational efficiencies and optimizing outcomes across all functions.
This stream of analytics utilizes modeling, machine learning, data mining, and allied techniques to analyze the facts for making estimates about the future. By building a predictive model, organizations can determine the probable outcome of an activity or even the likelihood of the occurrence of an event. The greatest advantage lies in the fact that the model can extend itself to areas where currently there is no data available. It is particularly useful in aspects where there is a high level of uncertainty. Firms use this data analytic tool for predicting customer behavior towards a new product, estimating the success of marketing campaigns, or anticipating sales trends.
The prescriptive model is all about rendering advice on possible outcomes. It not only helps in predicting what will happen, but goes one step further, in recommending one or more viable courses of action. It applies a combination of sophisticated techniques, including, robust algorithms, machine learning, and modeling procedures on various data sets. If implemented properly, it can vastly influence an organization’s bottom line.
It is important to note that none of these data analytic techniques are better than the other. In fact, they complement each other. They propel the organization to make decisions that would help in accomplishing their business objectives as well as maintain their edge. Be it predicting the competitive landscape, lowering operating costs, enhancing customer service, or increasing ROI, the ultimate goal of analytics, is to guide the decision maker to formulate decisions that are agile, effective, and productive.
Acer Innovation is an advanced analytics firm providing consulting services across a diverse range of industries and horizontals. For more information on their technology and services provided, visit http://www.acerinnovation.com