Businesses have been leveraging the power of analytics and business intelligence for some time. These analytics gave business leaders insight and reporting analysis into the dynamics of sales, inventory, logistics and virtually every system of the business. These analytics drew on historical data, found patterns, discovered anomalies, trends, forecasts and performed other slice and dice analyses. They could, at best, describe the current dynamics of systems or diagnose i.e. explain why something had happened the way it had.
However, there are two trends that are outstripping the capability of these analytics to leverage the data that is available for decision making. These are the emergence of Big Data and the Internet of Things (IoT). Big Data is the vast amount of unstructured and structured data emerging from human interactions online, as well as the data generated in real-time, by machines connected to systems and the Internet.
Earlier, it’s not that machines were not connected to the Internet, but the data that was generated was available only within a few siloed systems; sales data lay with sales, inventory data lay with inventory, etc. Also, there are now enterprise application integration solutions that have brought data together, from different and disparate systems, to provide enterprise level insight and analytics, such as production capacity, returns from assets, cost of holding current inventory levels, etc.
When it comes to data generated in real time, from IoT for example, traditional analytics are unable to deal with the complexity of the unstructured, non-relational data. This is because the data is not standardized and is in different data types.
An emerging trend of analytics, using sophisticated correlation, clustering and modelling algorithms is now giving businesses unparalleled capability to access, make sense of and deploy real-time data for decision-making, in response to real and critical events that are happening. This data could be related to production processes, inventory movement, or embedded inside the sales or customer journey.
This revolutionary trend in analytics is giving business an unprecedented access of data inside work flows, enable mining of real time data, and generating insights and predictions of future events.
From the descriptive and diagnostic capabilities of previous generation analytics, this emerging trend of analytics delivers predictions based on what will happen as well as prescriptions i.e. what can be done about it.
Added to this are machine learning algorithms and cognitive technology, which increase the depth and accuracy of insights and predictions as well as the generated recommendations.
Basically, cognitive and predictive analytics has given businesses boost of “brain” power to access, process and make sense of real time and dynamic data, to get better views and insight into business dynamics in order to take swift and impactful action.
Some of the significant benefits to businesses include:
- Nearly 15% gains in operating margins and revenues
- Improving risk profiles
- Modelling future requirements delegated to computers so executives can focus on high-value projects
- Managers can more optimize resources quickly and easily to increase production and improve efficiency
There are also certain business areas where analytics and predictability find particular powerful relevance:
Customers leave a trail of digital data, which if mined and analyzed, can reveal a lot about their decision- making processes, buying habits, preferences, etc. With sophisticated modelling and analytical tools, businesses can even predict with reasonable accuracy what customers might be interested in next. With this knowledge, they can preempt their purchasing events or even prompt them at the right time, with offers that would suit their preferences and interests. They can also reduce customer churn, predict the next buying cycle of customers, the price that would be most attractive to customers, and offer many more powerful insights.
Usually it takes a long drawn out post sales analysis to reveal the factors underlying a successful sale or an unsuccessful conversion. It takes even longer to match deals with certain buyer profiles. Sales and marketing can use Big Data and predictive analytics to understand the real factors that drive sale success and increase lead conversion.
Deciding how to price a product can mean the difference between success and failure for businesses. Pricing items too high and no one will buy, too low and they’ll struggle or even lose money. Pricing strategies involve a multitude of dynamic factors including cost, mark-up, buyer psychology and many others. With sophisticated modelling and analytical tools businesses can predict prices that will suit their customers.
Inventory tracking and forecasting
With Big Data and cognitive analytics, operations managers can have access to real-time inventory metrics. This can empower then to take corrective action to remove bottlenecks and improve efficiency. Having access to real-time customer demand pattern data can help inventory managers accurately match inventory levels with customer orders and improve customer service satisfaction. Big Data analysis can also predict seasonal spikes and troughs in customer demand to ensure optimum levels of inventory, help reduce costs and ensure tighter margins.
As more and more devices are interfacing with businesses, businesses need to enforce safeguards and security controls to protect confidential and sensitive information. Cognitive and predictive analysis technology can process Big Data, including image processing, behavioral analysis, event analysis and breach analysis. Cognitive technology can build profile and event models and put security systems through simulations to develop a strong security strategy to withstand the most serious threats.
Speech and Voice Services
Cognitive and machine learning technology can slice through mountains of speech data, pick-up patterns, draw correlations between patterns with user profiles, help detect voices, and interpret messages.
Cognitive and predictive platforms can process both historical as well as real-time data from devices and detect signals from hardware to provide alerts for likely failures or predict when the next maintenance activity needs to be undertaken.
Cognitive and predictive analysis technology can sift through voluminous historical as well as real-time, downtime or fault data, service level agreement violations, along with customer interface and interaction data, to understand customer service satisfaction drivers, analyze and co-relate dissatisfaction with events, and create a service quality strategy that delights customers.