By Greg Silverman, Oct 26, 2020
There’s no denying the value prescriptive analytics brings to an organization, but doing so correctly is also difficult. Not only do companies need a background with other business intelligence tools and have the technology to implement it, but internal teams must be ready to turn the insights into results.
Prescriptive decision-making is the final step in the business analytics journey that drives revenue. It involves taking the recommended steps the process provides and turning them into strategic action. Today, we are highlighting three specific prescriptive analytics methods that help businesses choose the best course of action to optimize their strategies.
After businesses have mastered diagnostic analytics, descriptive and predictive models, it’s time to perfect prescriptions. There are a few methods data scientists and decision makers use to drive the results organizations need:
With technology continuing to advance at a rapid pace, ML has been a major buzzword for businesses wanting to benefit from new developments. Forbes explains that machine learning essentially helps an artificial intelligence system teach itself, so business leaders are freed up to focus on other core tasks.
This method is used for both predictive and prescriptive analysis because of its ability to learn from mistakes and achievements to improve forecasting abilities. For predictions, machine learning takes historical data to achieve more accurate projections of the future. Prescription uses that forecast and ML incorporates insight about human behavior and market conditions to make better decisions based on the future outcomes.
In the modern business landscape, organizations must be the first mover in the market to gain traction. Machine learning helps companies use their existing data science and analytics techniques to learn new things about the market, competitors and consumer behavior to stay ahead of the curve. Of course, this is just one benefit of ML; Forbes outlines a few more, which include:
The flexibility and scalability of machine learning helps organizations simultaneously enhance their strategy with accurate predictions while adapting to changing markets and consumer preferences.
Gartner predicts that by 2021, 80% of emerging technologies will have artificial intelligence as their foundation. It’s no surprise why. AI is capable of creating a system so advanced and adaptive that it runs without human intervention. These technologies have already been applied to automate business operations across a variety of industries, but especially in manufacturing.
Artificial intelligence is also applied in both narrow (ANI) and general (AGI) circumstances. For instance, ANI is used to mimic very limited aspects of human intelligence, like reading X-rays and other niche applications. On the other hand, AGI is broad and is used to help businesses achieve an overarching goal like reaching a revenue target.
Essentially, AI simplifies business decision making by doing some of the heavy lifting for advanced analytics. In tandem with ML, the system learns about trends and market patterns to automate processes and deliver accurate recommendations that help organizations adapt to the future.
With all of this being said, while AI is one of the most helpful prescriptive analytics methods, it never replaces the human touch. Businesses still need a team to take the results of the analysis and turn them into action that drives results.
The final statistical method as it applies to this broader topic is actually a subset of machine learning, and drives many AI applications, per IBM. Deep learning is akin to the human brain, as it involves many layers of neural networks that learn from data mining. The ability to ingest information from multiple sources and analyze it in real time helps businesses gradually improve the accuracy of their prescriptive models as the market changes.
Without deep learning, AI would not be as accurate or scalable as it is today. Just think about how good Netflix or Spotify are at curating playlists and recommending shows you might like — that's deep learning making your life easier. This technology is also used for more serious purposes, like identifying credit fraud.
While all three of these prescriptive analytics methods deliver undeniable results, together they create the most agile, effective models to help businesses reach their revenue target.
The best part about these methods is that they leverage existing tools and information to succeed. No matter if your organization is entering a new market with limited information or you have a wealth of knowledge and experience with big data analytics; prescriptive analytics methods are able to handle it all.
This is especially helpful for companies with novel data or those trying to compete in a market during uncertain times. Storing all of these analytics tools on one platform provides an organization with a complete view of the market, including consumers and competitors. Information, ideas, and technology are no longer siloed between departments. In one place, they provide businesses with the most valuable information and functionality.
From this analysis, business leaders across the organization — from CEOs and finance managers to marketing officers and supply chain organizers — gain valuable insights that help them optimize their strategy. However, there are more benefits to unifying prescriptive analytics methods, they include:
The Concentric model utilizes these prescriptive analytics methods to help businesses learn about the best investment strategies and how to reach their revenue targets. Contact our team today to learn more about incorporating this technology into your business intelligence stack.