In the past, analysts considered the 3R’s – the right data, at the right time, for the right people – as core principles driving enterprise business intelligence. However, this approach is no longer sufficient, as it only considers the information companies can gather themselves – internal and historical data they cannot change.
To compete in today’s fast-paced world, executives need to shift their thinking. With increased competition and greater noise due to the vast amounts of data now available, companies need to discern what internal and external data adheres to the new 3R’s for long-term success:
- Real Time
Relevant data means companies must look beyond their historical sales, CRM pipeline, pricing and promotions to consider external factors that impact demand, revenue and profit. In this sense, newer companies with digital business models have a giant leg up over traditional companies that do not see the whole consumer picture. Through technology, Amazon, Microsoft and Google, for example, know exactly who their customers are and actively track their wants and needs. This depth of knowledge provides a measurable advantage over traditional businesses that don’t have full visibility into end consumers or have the ability to discern repeatable patterns in their behaviors.
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How can traditional companies catch up?
First, businesses need to find out what leads consumers to spend. Converting sales and analytics to digital can provide valuable insights, but there are other factors to consider. Companies that collect external data like employment and wage growth information, for example, will be able to see how these trends lead to activity – both online and in store.
Second, and more importantly, companies should use big data and machine learning to identify other relevant leading indicators that impact consumer purchases. Potential clues can be found at the macro economic and micro economic levels, including industry trends, consumer behavior patterns and the changing climate.
Real Time means the time when real decisions are made and the ability to watch daily events that will cause future change. For example, big swings in gas prices may impact next week’s, next month’s, or even the next few quarters’ performance. Businesses should collect relevant external leading indicators at least daily, and stop waiting weeks or months to re-forecast and adjust inventory, staff, price and operational activity. Companies that build daily-updating predictive forecast models, using their unique set of relevant leading indicators, will be the winners in this new economy.
However, none of this matters for long-term growth if it is not repeatable. All too often, companies manually try to identify relevant and timely data, only to realize that they will need to do it again in the future.These companies have failed at making a truly repeatable process. For repeatable models and analytics, companies need to look for solutions that can consistently provide relevant data in an automated, systematic manner. This is not an easy task when necessary data and data sources change frequently, making technologies dedicated to providing the analysis, insight and information needed a valuable resource. To find repeatable patterns that might not be readily apparent, the best solution is one that leverages advanced machine learning, using a mix of internal and external data, to see the whole picture ahead.
It is a new day in big data, and the original 3Rs – right data, right time, right people – are no longer sufficient to succeed. Companies that move past legacy processes, data collection and analysis will be able to compete using the right technologies. By identifying the most relevant data, no matter where it exists, in real time every day in a repeatable manner, companies will be able to compete and win.