For years, predictive analytics has dominated the spotlight for analytics, overshadowing prescriptive analytics. However, that is starting to change. Today, adoption for prescriptive analytics is at an all-time high, with Gartner predicting that the market will reach $1.1 billion by 2019.
Currently, 10 percent of organizations use some form of prescriptive analytics. This will grow to 35 percent by 2020, as per Gartner. Part of this growth is attributed to the growth of the Internet of Things (IoT).
One of the greatest IoT market challenges for companies is how to manage, track, and analyze all of their data. ABI Research forecast that global revenues from the integration, storage, analysis, and presentation of IoT data will triple over the next five years, and surpass $30 billion in 2021 with a 29.4 percent CAGR. Data analysis from ABI Research suggests that early adoption of predictive and prescriptive analytics is occurring in more developed, mature Machine-to-Machine (MTM)/IoT verticals. Growth is especially high in asset-intensive industries where machinery cost is high, such as industrial, manufacturing, oil, and gas sectors.
The Difference between Predictive and Prescriptive Analytics
Predictive analytics is often defined as the practice of extracting information from existing data sets to forecast future possibilities. It indicates what might happen in the future with an acceptable level of reliability, including some alternative scenarios and risk assessment. In terms of business application, predictive analytics is used to analyze current data and historical facts to better understand customers, partners, and products, and to identify potential opportunities and risks.
So what is prescriptive analytics? Prescriptive analytics goes a step further. It examines data or content to determine what decisions should be made and what steps should be taken to achieve an intended goal. The key difference is that prescriptive analysis looks at current pattern sets and provides actionable outcomes. Prescriptive analytics allows for a tailored approach for understanding both user behaviors and unlikely patterns that could cause organizations to waste resources. While both predictive and prescriptive analytics rely upon big data for gathering and understanding information, the main difference between them is that a prescriptive approach recommends an actionable plan to fix the issues.
Descriptive vs. Diagnostic vs. Predictive vs. Prescriptive Analytics
Prescriptive analytics solutions take predictive to the next level by providing a desired outcome. Rather than relying solely on predictions based on educated guesses and past results, prescriptive analytics provide pattern-seeking machine algorithms that provide resolution. Often, it is characterized by techniques such as graph analysis, simulation, neural networks, recommendation engines, and machine learning.
One of the best examples of prescriptive analytics is Google’s self-driving car, which makes decisions based on various predictions and future outcomes. It needs to anticipate what’s coming and what the effect of a possible decision will be before it makes the decision in order to prevent an accident. The goal of prescriptive analytics is to see what the effect of future decisions will be, which helps to adjust decisions before they are actually made.
The Different Types of Analytics
Applying Prescriptive Analytics to Workforce Planning and Management
Currently, 96 percent of organizations use descriptive or diagnostic analytics; however, just four percent use predictive or prescriptive analytics. One of the reasons for this big disparity is that the vast majority of companies use multiple, unrelated tools to manage their talent, which makes it difficult for predictive and prescriptive tools to glean accurate data. Recently, attitudes have begun to change. A recent PwC survey found that 86 percent of organizations said that creating or improving people analytics is a strategic priority over the next three years.
While the most common application examples of prescriptive analytics revolve around logistics or retail, there is a benefit for hiring managers and HR executives. Prescriptive analytics can be valuable when it comes to workforce planning and workforce management.
For example, if a company knows that they have a 27-year-old who has been employed for just over two years, prescriptive analytics could be used to suggest the likelihood of that worker leaving within the next year or the likelihood of that person becoming a high performer in the future. The prescriptive analytics solution could then notify the manager of these likelihoods, and offer a series of pre-determined actions (based upon this employee’s exact profile). Thus, the manager is presented with data-driven guidance to help him or her understand the most viable next step to retain and develop a possibly high-potential employee.
Implementing and utilizing tools with prescriptive analytics can help companies gain a competitive edge in making critical workforce decisions. And prescriptive analytics also presents HR departments with a great avenue to move on from their traditional tactical role to provide the strategic guidance that organizations need.
“Descriptive analytics currently generate more than 75% of IoT analytics revenue. But over the next five years, rapid uptake of advanced analytics will overtake descriptive analytics’ share of revenue to the extent that predictive and prescriptive analytics will account for more than 60% of IoT analytics revenue by 2021.” ~Ryan Martin, Senior Analyst at ABI Research