5 ways industry is leveraging predictive analytics

The explosion of data and its significance in various sectors have called for the need for predictive analytics in order to try and predict future trends in the sectors. For starters, predictive analytics encompasses the extraction of information from an existing data set so as to forecast what might happen in the future. One might be in the position to see and determine patterns with the aim of predicting trends and outcomes through predictive analytics.

Predictive analytics goes hand in hand with big data analysis. Big data can be defined as voluminous data sets that can be computationally analysed to determine patterns and trends relating to human behaviour. With big data analysis, one can have insights that may eventually lead to better decision making and strategic moves in business which in turn brings maximum profits to the business.

In light of this, there are five major ways the industry is leveraging predictive analytics.

1. Customer behaviour analytics. Customer behaviour analytics involves qualitative and quantitative observation of customers based on common characteristics. The customers’ interactions with a given business are analysed and grouped to determine a common future trend. Furthermore, this analysis gives one a customer’s motives as well priorities when it comes to a particular commodity or service. Customers’ needs are given priority in any business since the customer always comes first and is commonly referred to as ‘the boss’. This information can enable organizations analyse different trends and come up with a conclusive solution to better their services and products thus reducing future loses by coming up with risk assessments.

2. Internet of Things (IOT) Analytics. IoT analytics refers to the application of data analysis tools and procedures with the aim of realizing value from voluminous data generated by connected internet devices. By the use of IoT, industries are in the position to collect and analyse data from sensors on manufacturing of their produce. On the other hand, data centres, as well as healthcare applications, can use IoT to make their services better and future oriented. In relation to IoT, big data predictive analytics is used to give current, consistent, and correct information for business reporting and analysis enabling industries realize a future trend. In conclusion, by the use of IoT, industries are able to predict maintenance, manufacture more intelligently, improve customer satisfaction as well as create new business models entirely in relation to the customer priorities.

3. Security analytics. This is a tech approach to cybersecurity which focuses on protecting data of a given industry. In this approach, data is analysed in order to produce security measures which are proactive. As a matter of fact, it is very difficult to predict a future security threat in any business but then if one deploys effective security analytics tools, they may be in the position to detect a threat beforehand and take measures to ensure that it doesn’t come into fruition. Through this, industries can achieve holistic protection in cyber domains thus ensuring that their data and any sensitive information is safe from hackers now and in the future.

4. Cognitive search and knowledge discovery. This encompasses extracting the most relevant information from large sets of data which is paramount in a given organization. In industries today, cognitive search and knowledge discovering is used in improving employees’ work efficiency by discovering and accessing information that they need to do their work. To add to this, when big data predictive analytics is applied in any platform that is enabled with abilities of cognitive computing, it can be used in interacting with users in a manner that makes them feel free and comfortable in relation to the topic of interactions. Through cognitive search and knowledge discovery, industries may make better decisions with Artificial Intelligence powered analytics hence enabling content sensitive and unified search and knowledge. This helps them in establishing links between related data which may be relevant in a chosen field.

5. Operations analytics.  This is an approach in business which focuses on improving already existing operations in a business. It involves the use of different data mining tools by an organization so as to get information which is more transparent for business planning. The planning is necessary in such a way that all factors are put in place beforehand to avoid any inconveniences that may arise as a result of lack of planning. Several companies have bought the idea of operations analytics for analytical intelligence operations with the aim of increasing their revenues and reducing operations costs. This has been effective for a long period of time in the various industries thus ensuring that their business is always on the go. Operations analytics can be used to forecast a future trend in the market thus enabling industries commit in light with what the future holds.

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