Integrating Predictive Analytics
As the name implies, “predictive analytics” is a type of machine learning where models and algorithms work together to offer predictions of how a future scenario might unfold. It harnesses all of the data found in a system (whether it’s a logistics operation or a power plant) and then is able to provide estimates on when or where certain parts of that system will transpire, fail, or change. Companies that are already deep into using Big Data and analytics are incorporating predictive logic to work more efficiently, reduce costs, and ultimately provide better services to customers. The market for this type of analytics is expected to surge to more than $12 billion by 2022, and its growth will go hand-in-hand with IoT and exponential growth in the sheer number of data points available to businesses.
Integration with Solutions Such as SharePoint
SharePoint is a solution that can provide additional value through predictive analytics. The platform enables enterprise content management for the storage, archiving, tracking and reporting of various documents and records. This provides a legal and process requirement framework for businesses. SharePoint can also function as an intranet portal and social network to improve employee engagement and centralize processes. And SharePoint encourages collaboration, by improving team communication with shared mailboxes, project-centric content organization, and other tools.
Microsoft has improved SharePoint with multiple predictive capabilities, including a refined search function. For example, it is using predictive logic to help with queries into a SharePoint library that might pull up thousands of records. This feature will streamline searches, making them more efficient and able to pull up more relevant results for the user.
Predictive analytics are increasingly found within integrations to other platforms because they provide the end user with an improved way of conducting business, whether they’re producing energy or selling goods on the retail shelf.
Industry Use Cases
In the logistics sector, companies are using predictive analytics to streamline their operations and proactively manage potential problems. Companies in this sector have for some time used sensors to track where packages or other items are located within the supply chain, but they have not always been able to provide accurate estimates for when items will arrive. A predictive analytics engine that receives data from sensors and other means can gauge multiple scenarios, such as the expected time materials will arrive at a warehouse/plant, when materials will arrive that are sent from a plant to a distribution center, and when the product will go from the distribution center to the eventual customer.
The benefits of knowing “when” become a greater efficiency for the teams that are tracking shipments and those waiting on their arrival. If a production team knows exactly when a shipment will arrive, they can have the group ready for offloading and can forecast the next steps in the process. If the analytics indicate the shipment will be late, then the team could focus on another part of the process, or management could ask some staff to come into work later to avoid wasted hours.
Predictive analytics can also be used as a modeling tool to judge the impact of seasonal movements in demand or the launch of new products. This can aid retail companies that need to optimize both their workers and other resources in order to capture the most sales while keeping expenses lean. The predictive analytics can reduce waste and help managers in a retail environment make better decisions in terms of inventory levels, rearranging floor space, and when to launch sales promotions.
In the energy sector, predictive analytics are being used to improve efficiency and avoid costly repairs.
Consider this example from a wind power firm operating a turbine in Iowa. The power company implemented predictive analytics that was monitoring wind turbine information, and its modeling predicted a problem in the gearbox that turns the machine. The analytics sent a message to the maintenance team to conduct a minor $5,000 repair, which if done promptly, would avert a massive failure that would have cost $250,000 and had the turbine offline for several days. This example is a good illustration of how inexpensive cloud storage and improvements in sensors provide the base for all of the data that comes together to make predictive analytics possible.
Improving the utilization rates of a turbine (by avoiding downtime) is not only cost effective, but it’s more sustainable, as energy produced in this fashion will not be produced using coal or other fossil fuel-centric means. Predictive analytics tied into weather forecasting will also allow maintenance to occur during periods of very low winds, so the company does not miss out on peak production times.
Predictive analytics also has a role in risk measurement and mitigation. Consider an insurance or financial services firm that can use predictive data to develop an accurate risk profile of a potential borrower, and they can adjust their product offering to that person accordingly. These analytics tools are also used to spot fraud problems, by identifying anomalies of events that might have just recently occurred, or point to incidents that are very likely going to occur in the future.
Implementing Predictive Analytics
Moving too quickly from a “gathering data” stage to predictive analytics can result in a knowledge gap, where a firm’s current staff is not equipped to find the right predictive insights from the data. Firms looking to harness the most value from these tools should consider an implementation technology partner who can guide them on how to train personnel on using predictions and what expectations should be set.
A consultancy can also help frame the questions about the exact challenges that predictive analytics will be looking to solve. For example, does an energy producer lack the ability to match peak demand and wants to better use analytics to provide more accurate predictions? Does a manufacturing firm spend too much time replacing broken machines instead of understanding when to proactively take a machine out of the line for service?
Businesses must be able to describe the problems in their business, have in place the right data points/sensors/tracking to look at the data, and then integrate predictive logic, which can address the problems in a completely new way.
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