The Digital Transformation of Decision-Making

Content By Devops .com

The ability to make effective business decisions has historically relied on factors such as experience, qualifications, talent and, in many cases, luck. Strategic and tactical choices are also informed by ‘borrowing’ the insight and knowledge of others. In many situations, ‘follow the leader’ can be an effective approach, and one need only study any sector of industry or commerce to see organizations everywhere mimicking each other, with competitive advantage based on relatively small acts of incremental innovation.

Today’s ambitious businesses have many more options at their disposal to inform decision-making than previous generations. The extent, for instance, to which businesses can now collect, store and analyze data has been transformed by improvements in technology. The result is many businesses are now sitting on enough data for advanced analytics to inform smarter, more efficient decisions.

The Role of Advanced Analytics

Stepping back for a moment, advanced analytics is a set of tools and techniques that use existing data to develop insights to inform business decision-making – in particular, improving the accuracy of predicting future events based on certain actions. In practical terms, it is particularly useful when organizations are planning to implement a new business activity, where it is employed ahead of legacy processes that simply can’t deliver the same levels of insight.

Online shopping is a prime example. When a consumer clicks on various items on a website, they will often be given other suggestions – sites often will present this information with the words ‘Frequently bought together,’ for example. This is based on what has already been viewed and uses advanced analytics to predict what else the shopper might be interested in. It’s just one of many ways retailers now rely on advanced analytics to intelligently cross-sell, and as many people will know, it’s very effective.

In a wider sense, advanced analytics can be used in a huge variety of scenarios, irrespective of industry, market or type of customer. In doing so, businesses stand to reap a number of potential benefits, including:

  • Smarter decision making. By generating data based on strong logic and reliable information, advanced analytics can draw on both past and present data to make more reliable, more confident choices about what to do in the future.
  • Focused knowledge. By understanding the factors that improve business outcomes alongside those that are negligible, leaders can focus their attention and investment on what is more likely to deliver the desired impact.
  • Risk management. In an era of heightened risk, and the need for greater risk management, advanced analytics can identify risk based on available data, helping to inform business models that minimize risk, or help businesses make decisions based on insight rather than hope. This can also help improve governance and mitigate regulatory and compliance risk.
  • Strategy automation. Because advanced analytics takes care of analyzing data, teams have greater flexibility and time to focus on wider strategic considerations, which can help make the business better.

Advanced Analytics in Action

Advanced analytics employs various techniques across an ever-increasing range of use cases. For example, data mining is applied to ensure organizations make use of the most relevant data, extracting what is useful from the raw data. This data can then be subjected to further analysis for a multitude of objectives, such as understanding customers better or building more effective strategies.

In addition, ‘big data’ analytics can be applied to both structured and unstructured data, and can be particularly helpful when the data generated isn’t in a proper format, or for organizations with massive data volumes to process.

Then there’s predictive analysis, where machine learning algorithms are applied to current and past data to identify relationships and patterns. The algorithms then build a prediction model that can inform decision making. In general, the more data that is available, the more accurate predictions will be. It can’t, however, deliver 100% accuracy, and random, unpredictable factors such as a mass healthcare crisis illustrate the obvious limitations on predictive analysis.

Taking this a step further, prescriptive analysis helps determine the best way to implement a business decision. Even when a decision is close to being made, there can be more than one possible implementation path to follow. Prescriptive analysis uses current and past data to suggest possible outcomes before actions are set in stone, recommending the best implementation options.

Advanced analytics is a rapidly growing niche of the technology industry, regularly described by industry experts and commentators as among the most important, transformational IT trends. Given the pivotal role of decision-making across every organization, this perhaps shouldn’t come as a surprise, and for businesses everywhere, it’s now only a question of when they apply it to improve their fortunes, not if.

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