For years, there was only the lonely world of descriptive analytics. You were able to pull up some data, look at it and tell your supervisors what happened. But you couldn’t say why it happened, and you sure couldn’t predict what could come next or take the step even further to try to make something happen, as with prescriptive analytics.
Then slowly, as information became more plentiful, you were able to move into the next to realms of diagnostic (the “whys”) and predictive (the “what will come nexts”). But as we zoom toward the future, more and more organization are insisting on prescriptive analytics (the “how can we make it happens”).
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% of organizations use some form of prescriptive analytics. This will grow to 35% by 2020, according to 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 forecasts 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% Compound Average Growth Rate (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.
Predictive vs. 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 as well as 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.
The next level
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’s characterized by techniques such as graph analysis, simulation, neural networks, recommendation engines and machine learning. Smart Track uses prescriptive analytics with its Match Index Intelligence to prescribe, or offer likely matches, for contingent workers, which helps determine the best candidate for your requisition without the human bias inherent in anything.
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.
However, while algorithms might do a better job of removing biases, it’s unable to understand people in the same way a human would. We, as humans, have cognitive intelligence – we can read a book or a paper and understand what it says. We are able to relate relevant concepts or themes based on what we already know. But our understanding is colored and influenced by environmental factors like time of day, mood or other factors such as comments from a colleague.
At DCR, we understand this dilemma between human intuition and machine-based objectivity. Through our Match Index Intelligence capabilities, we provide a solution. Smart Track, now offers complex cognitive intelligence to help hiring managers match candidates to requisitions that goes far beyond a simple word search.
Here’s an easy, visual way of understanding it:
Having foresight with your workforce
Currently, 96% of organizations use descriptive or diagnostic analytics; however, just a paltry 4% use predictive or prescriptive analytics. One of the reasons for this disparity is that the vast majority of companies use multiple, unrelated tools to manage their talent, which makes it difficult for predictive and prescriptive solutions to glean accurate data.
Also, sadly, it hadn’t really been a priority. However, more recently attitudes have begun to change. A recent PwC survey found that 86% 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 and procurement departments with a great avenue to move on from their traditional tactical role to provide the strategic guidance that organizations need, in line with the total workforce management concept.