It’s safe to say we’re experiencing an analytics explosion at this moment in time. Today’s future-oriented businesses have unprecedented access to:
- Predictive Analytics: A process of analyzing information that leverages historical data to forecast future outcomes
- Prescriptive Analytics: This approach focuses on generating actionable recommendations to overcome future issues and help clients optimize everything from revenue operations to marketing to recruiting and remote work.
But we’re also in the middle of a shake-up: The unrivaled proliferation of tech solutions has made employee productivity and security a bigger challenge for hybrid and dispersed teams. Let’s face it: Many organizational leaders lack the processes and experience they need to handle these completely new stumbling blocks.
That’s why we’ll focus this article on four core areas where predictive and prescriptive analytics are sorely needed and can have a major impact: productivity, process optimization, cybersecurity, and user behavior. We’ll also outline our best practice roadmap for leveraging analytics within your business.
Understanding Predictive & Prescriptive Analytics
Predictive and prescriptive analytics aren’t completely removed from each other in data analytics, but they have slightly different applications. Let’s look at both closely before we understand how they can work together to drive better business decisions.
What is Predictive Analytics?
Predictive analytics are metrics that rely on historical data to more accurately forecast how markets, customer behavior, and employees behave due to external factors or pressures. Think of the conventional wisdom that as interest rates rise, stock market prices fall. That’s one way we see predictive analytics in action in our everyday lives.
Common techniques and algorithms
To make more accurate predictions, analysts use:
- Machine learning techniques: For example, stock market analysts will use a machine learning algorithm like the Long Term Short Memory (LTSM) to build a model for how they will analyze the stock market. Security analysts get actionable insights with threat detection algorithms to predict cybersecurity risks and more easily detect anomalous behavior internally.
- Data mining: Analysts also use AI and programming languages like SQL and SAS to synthesize and study large data sets. This lets them discover patterns and implement statistical algorithms to create more accurate business forecasts.
- Statistical modeling: Statistical models are mathematical representations of real-world scenarios. They then allow analysts to run simulations of possible future events to better inform any kind of business strategy. For example, real estate analysts might use statistical models like regression and neural networks to valuate real estate properties more accurately.
What is Prescriptive Analytics?
Prescriptive analytics builds on predictive analytics as if to say, “We know what might happen in the future, so what do we do about it? Think about how today’s marketing teams might use AI and machine learning to target customer segments more accurately rather than guessing why a certain campaign performed well. When used strategically, it’s a powerful resource that allows you to see what will happen and predict future scenarios.
Common techniques and algorithms
- Machine learning: Prescriptive algorithms and machine learning help analysts in the venture capital space decide where to invest. And prescriptive marketing analytics helps social media platforms curate more engaging content for users.
- Statistical modeling: Algorithms based on statistical models help sales professionals with lead scoring so they can determine which leads to prioritize.
The Complementary Nature of Predictive and Prescriptive Analytics
There’s a lot of confusion about the role descriptive analytics, predictive analytics, and prescriptive analytics can and should play in business strategies. But these types of analytics all work together to help analysts and leadership learn from the past and predict what business outcomes will occur when specific external forces are introduced.
Let’s look at key steps in the analytics journey to see how predictive and prescriptive analytics work together.
- Data collection and preparation: Analysts use programming languages and software to pull related data sets and prepare them so they’re ready for predictive analysis.
- Predictive modeling and validation: Techniques like clustering, time series analysis, decision trees, and neural network algorithms help analysts determine potential outcomes.
- Prescriptive modeling and optimization: Analysts may utilize statistical models to run various business scenarios. For example, logistics analysts for UPS will determine how factors like traffic, weather, and package weight could effect deliveries, allowing them to optimize their routes.
- Deployment and monitoring: Comprehensive data in hand, analysts will work together with their cross-functional colleagues to develop strategies and processes that will help them meet their business goals, customer demand, and identify future trends.
Driving Productivity with Analytics
Analytics can play a variety of roles in driving productivity, helping companies identify where their workflows and everyday processes need refinement. Let’s look at a few specific examples.
The role of advanced analytics in productivity improvement
The apps, websites, and tools teams use today are meant to make their lives easier, but they can also lead to extra work, distraction, and even burnout. Analytics help identify what work and productivity tools might hinder efficiency and drain business resources.
Predictive analytics use cases
In client-facing roles like sales and customer success, every interaction matters. For example, sales representatives are always looking for ways to predict which leads are most likely to buy and what kinds of content and conversation touchpoints are most likely to persuade them to buy. Thankfully, sales intelligence tools now use AI and machine learning to help sales teams predict what leads and persuasion tactics they should prioritize.
Workforce intelligence platforms like Teramind also help sales and customer success teams pinpoint and predict how specific processes and workflows—like following up with a lead or guiding customers through troubleshooting—might be interfering with the entire sales lifecycle so they can make changes. Improving the customer experience with data-driven decisions is key to growing your business.
Prescriptive analytics use cases
When teams use prescriptive analysis to improve productivity, they might test how some external factors might set them back. For example, a call center might use a predictive modeling program to simulate what would happen if there was a sudden and unforeseen surge in call volume. They can then use prescriptive analytics to create a contingency plan to handle that kind of scenario.
Strengthening Cybersecurity with Analytics
Cybersecurity isn’t just about considering external threats. Security issues can also arise from within, which matters if you’re in health, financial, or government services and need to protect your client and your employee data.
The growing importance of analytics in cybersecurity
Companies that want to grow need to be tech-enabled but also smart about how they use that tech, especially as cybercrime becomes more sophisticated. That’s likely what’s led organizations like the NSA to adopt and promote the zero-trust security model, a philosophy encouraging organizations “not to trust anyone by default.”
Predictive analytics use cases
Sophisticated insider threat prevention and user activity monitoring platforms like Teramind allow security-minded organizations to identify potential threats and vulnerabilities, like how their processes for handling and sharing files internally and externally might be putting them at risk for data leaks.
This kind of software can also help you detect signals of anomalous behavior in real-time, like someone logging into their work email outside of working hours or emailing someone in the finance department with an unauthorized invoice.
Prescriptive analytics use cases
Today’s AI-powered cybersecurity platforms can also make recommendations based on identified vulnerabilities. For example, Teramind uses dynamic risk scoring to help organizations identify which issues to prioritize when strengthening their security posture. They can also alert you immediately when they identify an issue and even block files or information sharing based on the business rules you set.
Enhancing User Behavior with Analytics
User and entity behavior analytics (UEBA) is a more focused area of cybersecurity that uses machine learning to identify abnormal behavior. Let’s look at analytics’ role in better predicting and prescribing solutions to threats from users and entities within your organization’s network.
The role of analytics in understanding and influencing user behavior
With the right application of predictive and prescriptive user behavior analytics, organizations can better protect their assets, customers, and reputations.
Predictive analytics use cases
UEBA uses AI and machine learning programs to draw from a vast breadth of threat data so organizations can establish a baseline for “safe” user behavior. For example, customer service interactions in the finance industry may need to establish extra parameters around file sharing so that customers never get access to sensitive information about other clients or employees.
From a business growth perspective, predictive analytics can also help software companies with valuable insights like identifying high-value user segments — those that get the most long-term value from their services and products. Doing so allows them to target more potential clients with similar traits to high-value users.
Prescriptive analytics use cases
Building on their predictive user analytics, software companies can offer more personalized user experiences based on their clients’ preferred services and features. That allows them to improve user engagement and retention. And for more security-focused businesses, the user behavior they gather from their customers and employees allows them to offer more advanced threat protection.
Implementing Analytics: A Roadmap
Before identifying how you can apply more predictive and prescriptive analysis to your own business processes, it’s essential to assess the state of your current safety mechanisms and operational efficiency. Let’s walk through the steps you should follow to make sure your analytics have the most impact.
Assessing organizational readiness and maturity
The fundamental questions to ask here are, “Where are we now? And where do we want to go? And how will analytics help us get there?” However, fully fleshing out these answers should be a project that involves cross-functional leaders, including your CISO, COO, and CFO. For instance, a deep dive into the current state of your company’s security could reveal you have improvements to make. The goal, then, is to determine:
- Whether you have the right tools, resources, and expertise to make those security and productivity improvements
- How long it will reasonably take to make those improvements
Defining business objectives and key performance indicators (KPIs)
You should then turn these discussions into clear business objectives tied to specific numbers and tactics.
Your objectives should follow the SMART framework—specific, measurable, attainable, relevant, and timebound—so they’re easier to break down and cascade throughout your company and are applicable to different teams.
For example, let’s say you want to increase your overall threat preparedness level:
We want to improve our Mean-Time metrics by 20% within the next six months. We’ll regularly monitor KPIs like Mean-time-to-detect and Mean-time-to-resolve and create more detailed action plans to overcome intrusion and hacking attempts.
Building a data-driven culture and mindset
If your organization follows the zero-trust model, ensure all employees know that as soon as they apply to your job posting and have a first interview. And if you use employee monitoring software, make sure they understand that the cybersecurity and user behavior analytics you gather from the software aren’t meant to hold them accountable to impossible productivity standards, but just to keep everyone protected and working as efficiently as they can.
Selecting the right tools and technologies
Multi-functional software like Teramind can help organizations solve their security, productivity, and user behavior issues within a single platform. However, depending on your needs, there are other kinds of cybersecurity software designed to protect organizations from threats posed by mobile devices, Internet of Things (IoT), and malware.
Developing analytics skills and capabilities
While you likely have capable business and security analysts working hard to keep your organization safe and growing, the world of technology is changing fast. That means your analytics team should always be on top of the latest research and news so they know what emerging threats and tech could impact your organization. They should also be working to upskill themselves on the innovations in AI and machine learning most relevant to your industry.
Measuring and communicating the impact and ROI
If you’ve been tracking the right metrics and making the right tweaks along the way, measuring the impact of implementing analytics shouldn’t be difficult. But if your analytics program doesn’t produce the hoped-for return on investment, communicate that with leadership. Work together with your security, operations, and analytics to determine why you weren’t able to meet your goals, and what adjustments you need to make moving forward.
The Future of Predictive and Prescriptive Analytics
According to an IBM survey, 42% of enterprise-level businesses have integrated AI into their operations, 40% are considering AI, and 38% use generative AI within their workflows.
We will likely see a rise in customer-facing teams using deep-learning AI chatbots and assistants to handle customer queries more speedily and thoroughly. But we’ll also see an increase in job disruptions, regulation shifts, and the spread of misinformation and deep fakes. Think of the way threat actors can now use AI to impersonate just about anyone on the phone or in writing.
If your organization’s eyes are trained on future outcomes, the biggest concerns organizations should have center around ethics and security. Be aware that we are likely to see more multi-vector Gen V, supply chain, ransomware, and phishing attacks due to the unique ways AI can make companies vulnerable.
Be open to creating more ethical frameworks that account for AI’s disruptive nature, such as how human biases have shaped AI or how the AI skills gap could impact people from underrepresented backgrounds within your company.
FAQs
What is the difference between prescriptive and predictive analysis?
Prescriptive analytics goes beyond predictive analytics by predicting future outcomes and recommending specific actions to optimize those outcomes. While predictive analytics uses historical data and statistical models to forecast future events, prescriptive analytics makes use of data-driven insights and optimization algorithms to provide actionable recommendations for decision-making.
What is the main difference between perspective and predictive analytics?
The main difference between prescriptive and predictive analytics is that while predictive analytics focuses on forecasting future outcomes based on historical data, prescriptive analytics goes a step further by recommending specific actions to optimize those outcomes. Predictive analytics uses statistical models, while prescriptive analytics utilizes data-driven insights and optimization algorithms for decision-making.
Is AI predictive or prescriptive analytics?
AI can be used for both predictive and prescriptive analytics. Predictive analytics uses AI algorithms to analyze historical data and forecast future outcomes, while prescriptive analytics takes it a step further by recommending specific actions to optimize those outcomes using AI-driven insights and optimization algorithms.
What is an example of prescriptive analytics?
An example of prescriptive analytics is a recommendation engine used by streaming platforms like Netflix. It not only predicts what shows or movies a user might like based on their viewing history and preferences but also prescribes specific recommendations to optimize their viewing experience.
Conclusion
Prescriptive and predictive analytics are more important in cybersecurity and productivity than most business leaders understand. When you consider the turbulent nature of AI’s past and future, these types of analytics will only become more important when implementing analytics within your team.
That’s why it’s important to have powerful platforms like Teramind on your side. With dedicated tools to detect and prevent threat incidents, you can rest assured that you’ll overcome unprecedented security issues and keep your business moving forward, no matter what the future holds.
