Data-driven decision-making is a necessity in today’s competitive business landscape. Companies collect data for myriad purposes, but if you aren’t using it to make business decisions, you’re failing to optimize that data. One powerful tool that can help you harness the potential of your data is predictive analytics. It can provide you with a stronger sense of future performance and a competitive advantage, empowering you to make informed decisions with confidence.
In this comprehensive guide, we delve into the concept of predictive analytics, its applications in various business domains, and how to identify opportunities and implement predictive modeling for your organization. By the end, you’ll have a solid understanding of how to leverage predictive analytics for data-driven decision-making in your workplace.
What is Predictive Analytics?
Predictive analytics applies machine learning technologies to company data to create predictive models. Predictive analytics models use artificial intelligence and deep learning techniques to predict potential business outcomes based on historical sales and other data. They can be used for a variety of business applications, like predicting future sales or sales opportunities, how certain changes will impact production output, customer service improvements, and much more.
Identifying Opportunities for Predictive Analytics in Your Organization
Predictive analytics can be applied in many ways, depending on your organizational needs. Below are some of the most common applications.
Human Resources
Predictive analytics can help your organization plan for employee turnover, respond to staffing needs, and assess the potential for improved productivity. As you make changes to your organization, like adding a department or introducing a new product or service, predictive models can use existing company data to assist in talent acquisition, retention, and other data-driven discovery of HR opportunities.
They can also help identify potential employee opportunities to change teams or rise into new roles to optimize your workforce.
Models can also assist in predicting an employee’s future performance and development, making it easier to keep track of high-performing employees you want to retain or employees with potential that you want to incentivize.
Marketing and Sales
A very common business application of predictive analytics is financial forecasting. However, it’s also useful in developing marketing strategies, identifying sales opportunities, and avoiding potential pitfalls. Machine learning can help marketing teams effectively target the customer profiles for your product or service and create the right customer segmentation for different marketing campaigns, messaging, and offers.
For sales teams, predictive analytics can use existing sales and customer data to score leads more efficiently and help salespeople prioritize the ones most likely to convert to customers. It can also provide actionable predictions that lead to sales opportunities.
On the other hand, models use data-driven discovery to identify common risk factors for customer churn by drawing valuable insights into customer behavior. Statistical models can help develop customer insights to improve customer experiences and hone thoughtful sales strategies.
Operations and Finance
Continuous improvement is a hallmark of the most efficient organizations. Predictive analytics is key in optimizing operational efficiency by using historical trends to optimize processes, remove potential roadblocks, and reduce operational costs.
Any project or innovation carries risk, and predictive analytics can provide risk assessments to determine what you can do to minimize risks or which projects may not be worth the risk. From production output and cost forecasts to insider fraud detection and risk reduction, predictive analytics has a wide range of applications for operations and finance teams, from future cash flows and additional costs to sales forecasting and more.
Building a Predictive Analytics Roadmap
Incorporating predictive analytics into your organization’s planning and strategy requires taking a step back and getting a bird’s eye view. You must understand the key details of your company’s operations and processes and verify its data maturity and readiness. Some organizations simply won’t have enough data to create robust predictive analytics models, so they must first implement strategies and technology to prioritize data collection and cleaning.
When your company has the data to create predictive analytics models, you must determine how to use that data. What business processes do you want to improve? What business outcomes do you want to predict?
Setting clear goals and objectives will help you properly implement predictive analytics and gauge the success of your models. Identifying key stakeholders, like executives and team leads, and securing their buy-in will help focus your goals and ensure team members understand how to use analytics to pursue specific future outcomes.
Key stakeholders can also assist in prioritization. Pursuing a dozen disparate, unrelated goals is inefficient and wasteful. Yes, you may want to triple sales next year, but that may not be realistic. Prioritizing initiatives based on impact and feasibility helps your organization make incremental progress that allows employees to celebrate their wins and feel like the company is making meaningful improvements with the help of predictive analytics. When it comes to improving operational efficiency, data is your best friend.
Assembling Your Predictive Analytics Team
To perform effective predictive analysis, you need a strong predictive analytics team. You may be able to task some existing employees to take on new responsibilities, or you may have to hire.
Roles and Responsibilities
A good predictive analysis team should be made up of the following job roles:
- Data scientists and business analysts: These data experts will use the data available to them to create predictive analytics models, develop algorithms for specific business outcomes, and draw insights from results.
- Business domain experts: Your company’s business domain experts will likely be team or department leads — people with an intimate understanding of specific domains of your business.
- IT and data engineering: Predictive analytics models require tools and technology to gather data from disparate sources and clean data to make it usable for scientists and analysts. IT and data engineering teams can do this.
Fostering Collaboration and Communication
Implementing predictive analytics is not a solo endeavor. It requires strong collaboration and communication between teams and key stakeholders. Team leaders should be encouraged to share data and data sources with the predictive analytics team, and systems should be in place to ensure data is transferred to the right destinations in real-time. Data analytics software will help foster collaboration, of course, but everyone in the organization should feel responsible for gathering and reporting data responsibly, making them an integral part of the team effort.
Upskilling and Training Opportunities
When implementing a predictive analytics program, you may only have a few people within the organization with the requisite skills to participate. You may hire a data scientist or engineer to help the initiative.
As with any new initiative, building a predictive analytics team is not just about the company’s growth; it’s also a great opportunity for personal growth and development. Encourage your data team to hold seminars to upskill any interested employee or offer to pay for classes if employees want to learn skills that will be valuable to the company. This investment in your skills and knowledge can be a source of inspiration and motivation, as it opens up new possibilities and enhances your professional value.
Implementing Predictive Analytics Solutions
Building a team and creating a roadmap are crucial steps to implementing predictive analytics solutions. These steps will help you build and maintain an effective program moving forward.
Defining Business Requirements and Success Metrics
Setting goals and objectives will help you determine the scope and aims of using predictive analytics software. But to set your solutions up for success, you’ll need to define the business requirements for a successful program and the key metrics to demonstrate success.
Identifying the right staff, investing in the right tools, and determining the goals and business outcomes you want to achieve are good starts. Then, find the key metrics that best indicate successful tracking toward those goals.
Data Collection, Integration, and Governance
Knowing the right metrics for your predictive analytics tools will help your organization understand what data it needs to collect. Whether it’s consumer, operational, or technical data, your organization needs to leverage tools to collect and integrate data from disparate data environments into a streamlined, centralized workflow.
That way, data scientists and analysts can easily source data for predictive analytics, gauge how the organization is tracking toward stated goals, and make data-driven decisions on how to improve the likelihood of achieving the desired business outcome.
Remember, if you’re using customer or other private data, you must always abide by data compliance laws and utilize proper data governance to avoid leaks, data breaches, or other violations. If you don’t, you will hurt customer retention and risk reputational damage, and your organization may face legal consequences.
Model Development and Validation
Machine learning models require significant training. The more data models are trained, the better they use data to determine predictive outcomes. Developing a model requires using historical data patterns and real-time data from the most important sources to your business goals.
Once you’ve integrated data collection solutions, training a predictive analytics model with enough data may take several weeks or even months.
When you’re ready to validate your models and hone predictive modeling techniques, you must divide data into training and test datasets to assess how well a model’s accuracy compares to test data.
Deployment and Integration with Existing Systems
When training a model, you’ve likely integrated with systems to source data and bring it into the model’s datasets. When you’re ready to deploy a model, do so on the right platform for your business, for instance, cloud-based services, on-premise servers, or edge devices. Upload the model file, configure its parameters and settings, and integrate it with existing systems to connect seamlessly to the data source and output destination.
You may have existing Business Intelligence (BI) tools like Microsoft Power BI or Tableau that will help with informed decision-making. These need to be connected to your model.
Monitoring, Maintenance, and Continuous Improvement
Predictive analytics models and data sources change over time. As your company grows and evolves, the data used to originally train the model may become outdated or you may change platforms or systems, forcing you to change where your model is drawing its data from.
Whatever the case, regularly monitoring and maintaining your predictive analytics model is the best way to ensure continuous improvement that will support your organization’s goals no matter how they change.
Communicating Predictive Analytics Insights
Data is useless if it can’t be explained or acted upon. Being able to draw actionable insights is the whole point of setting up data models in the first place.
This goes back to getting the right team in place. Any organization using predictive analytics needs data scientists and analysts who can translate technical findings into actionable recommendations. They should communicate to decision-makers and other key stakeholders a complete picture of past and present data and what future trends may develop if certain forward-looking decisions are made today.
Compelling visualizations and dashboards can communicate complicated technical findings to help business leaders make data-informed decisions. The data always tells a story, especially when working towards future goals, and presentations should reflect chronological trends and focus on only the most important, highest-level data to appeal to more visual thinkers.
It’s not just for executives or department leads to improve their decision-making processes. Business leaders should preach data-driven decision-making throughout the organization so that everyone on every team is encouraged to use data to do their jobs well. Everyone across the organization should feel comfortable using and analyzing data, so they have pattern recognition ability, can recognize trends, and notice other potential indicators that could reveal opportunities or inefficiencies.
Conclusion
Data is king in business today. Every organization uses data in different ways for different business purposes. However, leveraging predictive analytics solutions can be a powerful way to make better, more data-informed decisions to drive the organization forward and give you a business advantage. Predictive analytics can help companies better understand operational efficiency, customer behavior, and employee performance and provide decision-makers with data-driven insights to reach more positive business outcomes.
FAQs
How do you use predictive analytics?
Predictive analytics in cybersecurity helps identify fraudulent patterns, detect anomalies, and predict vulnerabilities to protect data from cyberattacks. In human resources, predictive analytics can identify skills gaps and predict future training needs to ensure the workforce remains aligned with organizational goals.
What are some examples of predictive analytics?
Predictive analytics in cybersecurity helps identify fraudulent patterns and potential vulnerabilities, enabling proactive security measures. In human resources, predictive analytics assists in identifying skills gaps and predicting future training needs for employees. Leveraging AI-powered analytics can provide real-time data access and AI-assisted insights for actionable decision-making.
Which technique is used for predictive analytics?
Predictive analytics in cybersecurity can help detect fraudulent patterns, anomalies, and vulnerabilities to prevent cyberattacks. In human resources, predictive analytics can identify skills gaps and predict future training needs to ensure the workforce remains aligned with organizational goals.
What are the four steps in predictive analytics?
Predictive analytics in cybersecurity helps identify fraudulent patterns, detect anomalies, and predict vulnerabilities to proactively protect data from cyberattacks. In human resources, predictive analytics helps identify skills gaps and predict future training needs to ensure the workforce remains aligned with organizational goals.
What is the correct workflow of predictive analytics?
Predictive analytics in cybersecurity helps detect anomalies, predict vulnerabilities, and trigger security procedures to prevent cyberattacks. In human resources, predictive analytics identifies skills gaps, predicts training needs, and creates personalized learning pathways for employees to align with organizational goals.
How do you prepare data for predictive analytics?
Predictive analytics in cybersecurity helps identify fraudulent patterns, detect anomalies, and predict vulnerabilities to proactively protect data from cyberattacks. In human resources, predictive analytics aids in identifying skills gaps and predicting future training needs to ensure a workforce remains aligned with organizational goals.
