How Companies are Reimagining Business Processes With Algorithms
More changes in today’s business are being driven by computers- not through useful programs written by humans, but processes re-engineered by data analytics. This branch of computing relies on complex mathematical algorithms that today represent machine learning – the ability to learn, adapt, and evolve better machine intelligence. Algorithms are in effect a step-by-step process of problem-solving.
Data and Algorithms
The purpose of these algorithms is to re-engineer business processes as humans have always done. Business process re-engineering (BPR) was limited by human speed and understanding. Managers reviewed collected data to make note of trends, influences, and rewards, which were then codified by developers into business intelligence systems. This could be in part a waste of time and resources as business technologies and marketplace strategies changed. However, modern machines can constantly re-evaluate environments and processes through current data. Every transaction is digitally recorded and fed to data marts. Internet tools can gather data in real-time by user clicks and searchers, while a new generation of appliances – known as the Internet of Things – are becoming more intelligent and WiFi connected. The IoT is expected to exceed 28 billion devices by 2020. An incredible flood of data is out there waiting to be used.
BPR garnered some negative reactions when over-eager businesses began transforming too many processes at the same time. Readiness to broadly discard working methods, even flawed ones, resulted in human confusion and squandering of resources that did more damage than good to business operations. But machine-reengineering is off on a different track with a focus on redesigning one process at a time, particularly core processes. This make it much easier to quantify outcomes. However, algorithms can be developed for specific business needs. PNMsoft defines case management software as technology for real-time work allocation in operations centers. Algorithms are ideally suited to augment such practices. Still, only 15% of global corporations are using big data in BPR.
One specific area of applied analytics is in marketing, where predictive analytics fits well with sales and advertising numbers, but with advanced algorithms, other important factors such as demographics and language detection come into play.
Analytics is chiefly focused on three basic, attainable goals: cost-cutting, customer behavior, and revenue. While these are not always related or specifically required outcomes, machine algorithms more easily and reliably generate process options than humans searching through trends and perspectives for productivity solutions.
The question is whether these algorithms are creating more business gains than traditional approaches. The answer seems to be a definite ‘YES’. Many of the corporations pioneering analytics have reported significant gains in the above-mentioned areas. This is in part due to automated communication of results to front-line sales, allowing them to adjust strategies according to current customer activity.
For instance, CRM (Customer Relationship Management) databases can be quickly prioritized into the most promising leads per product per region. Analytics becomes in essence a revenue machine directing data-driven efforts, while traditional business analysts can focus on other tasks besides crunching numbers and running reports.
Machine-learning has also led to recommendations for improved processes that lead to better interactions in customer service interaction with clients. The prior trend in customer service involved introducing automation to shorten wait times and resolve common issues. It took machine-learning to drive home the point that human customers prefer human engagement.
Machine-learning presents a new model for designing and suggesting solutions that improve workflow. It empowers ideas that spring from evaluating data too complex and too vast for human analysts to grasp. In some cases these ideas represent patterns that lie outside human experience and recognition. Improved machine-learning algorithms make evaluating business operations of all kinds more efficient and more flexible. Analytics and forecasting are making the transition to an evolving technology that provides more rewards, and faster.
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