Is it “just a hype” or a new trend to watch for? A relative new movement is predictive analytics. All kinds of organizations become increasingly aware of it and use it to steer their business and strategy.
Companies use it to reduce unreliable assumptions, anticipate outcomes of various systems and become proactive about future events. In this article, I will explain what predictive analytics is, what the main concepts are and why it matters for your organization.
Predictive Analytics explained
Before we dive into the details, let’s first put predictive analytics into perspective. Popular definitions define predictive analytics as a set of advanced analytics to make predictions about the future. This can be achieved when using high-quality historical data, combined with statistical modeling, data mining, and machine learning techniques.
Data mining, text analytics, and statistics combined help to create patterns and relationships from structured and unstructured datasets. Cloud technology plays an important role here since fast & advanced data storage and processing solutions are needed to put these techniques into practice. Speed and quality matter.
Why it matters
Organizations collect big chunks of data and this data is now put to action. Valuable data becomes increasingly important as historical data. What happened in the past, what is happening now, and what will happen in the future are the main stages for predictive analytics. Companies drive actions based on (the predicted) outcome of future trends. When these actions are linked to your business strategy, it is clear that this plays a significant role in your organization.
The end result is to identify risks and opportunities to which you need to react to. Being on the negative side (risks) helps companies to predict when things might go wrong in a certain situation. For example: stop the development of a physical product. On the positive side: use opportunities to attract new and high-paying customers.
The main phases
Simply speaking predictive analytics is based on three sequential phases. Start with valuable and reliable quality data in mind and progress through these phases:
- Reporting/analytics: answers the questions like what happened in the past and why did that happen at all. This phase acts as the foundation for historical data.
- Monitoring: collect real-time information about what is happening right now. Seen from a future point in time, this constantly feeds the historical data.
- Predictive Analytics: what will happen in the future and what will be the impact on your organization.
To drive a company, you constantly need to be on the lookout for (external) opportunities and threats. The last phase mentioned above helps to guide you in the most beneficial direction.
Common processes
Typical processes to execute within the concept of predictive analytics are the following:
- Project definition: start with the end in mind. Define the business objectives, list the deliverables, scope the project itself, and define the project outcomes. Critically important: identity and select the most appropriate datasets.
- Collection of data: mine the data which you need. Use multiple sources if needed to get a complete overview of your customers’ interactions or business processes.
- Analyze the data: process and clean up the data, aggregate, inspect and transform where needed. Conduct these activities to extract useful information out of the immense collection of raw data. This information is a core asset to draw conclusions later on in the process.
- Run the statistics: use standard statistical models to validate and test assumptions & hypotheses. Raw facts matter here over subjective (pieces of) information.
- Predictive models: one of the most important steps to predict the future. Create one or more predictive models (automatically) and choose the best solution by comparing those models with each other.
- Deployment. you need to deploy the analytical results into the daily decision-making processes to get proper results. A bit like the deployment of artifacts. Extract reports and automate the decisions based on the modeling (tools).
- Monitoring of models: similar to (business) applications, you need to monitor the “performance” of your models. This is needed to guarantee you get the results that you expect and to initiate a connection between the “working project” and the original project definitions.
As it is with DevOps, common processes for predictive analytics follow each other in a repetitive manner. Feedback loops connect the last process with (an adjustment) of the first process.
Real-world examples
If the concepts made you curious, let’s take a look at some real-world examples. The following examples show why predictive analytics is already on its way to infiltrate in our daily life.
Example 1: COVID19
Trending on all platforms and on all news websites: COVID19. Predictive analytics can help to predict which group of patients is (most likely) to become very ill in case they might catch the virus. It also helps to streamline critical (human) resources in hospitals to treat the maximum number of patients if needed. Furthermore, predictive analytics can help to predict disease outcomes and it can be used to track peoples’ connections. All to fight back against the virus which spreads so fast.
Example 2: Supply chain
Over the last decades, all processes which are part of modern supply chains are increasingly optimized to make sure (logistics) companies work as efficiently as possible. Predictive analytics takes this to the next level. It can help to avoid disruption, generate accurate supply and demand forecasts.
When taking into account machines and vehicles which require servicing, it can predict maintenance issues and proactively warn the users/owners of them. In turn, they can take action or even let the system take action on itself. For example, order spare parts to be delivered when they need to be replaced.
Example 3: Marketing campaigns
Marketing campaigns are a costly activity. Organizations send out a certain message to a large group of customers in the hope they will buy a product of service XYZ. A positive response can be very low, the number of people that actually buy the product or service is even lower. Predictive analytics can help to pinpoint your message towards the customers who are most likely to buy your product or service. By doing so, you can spend your marketing campaign budget very wisely. Optimize revenue by predicting the behavior of your customers is key here.
All of the above-mentioned examples show that predictive analytics become common in our industries. Companies that practice it are ahead of the competition.
Data matters most
Reliable data is at the heart of predictive analytics. Without a proper data-set, the results of your predictions are not trustworthy. Your data-set needs to be large enough with plenty of historical data. Besides this, your data needs to be reliable and up to date. Old data and data which is “polluted” do not give you useful results. As the impact of data-driven decisions becomes bigger every day, your data matters most.
Cloud technology helps to extract information from this big bunch of data. Use cloud compute power to analyze, clean up and process large quantities of data. Without cloud technology, predictive analytics would not be so important and impactful as it is nowadays.
Required knowledge/skills
Successful companies know they require new roles filled in by very smart people. Data scientists, data analysts, and data engineers are the new “cool kids”. They build solutions to process the data, create predictive models and draw patterns. Data becomes an art. Python and R are very useful and popular programming languages which are used in the activities mentioned above.
Data scientists focus on the development of the mathematical model, while data analysts focus on the usage of the tools and mathematical models. A masters’ degree in analytics helps both groups of professions to gain a solid knowledge of the topics they need to fully understand. Data analytics require skills to visualize the data so they need to be masters in data visualization (tools) as well.
All roles are highly specialized and require highly skilled people.
Software tools to help you
Useful software tools, both commercial as well as free software help you to build your models and to analyze your data. It’s impossible to name all of the software tools that are available on the market. Predictiveanalyticstoday offers a great list of software tools for various phases of predictive analytics.
Different categories apply here:
- Commercial predictive analytics software such as Anaconda, Google AI platform, Matlab, and many many others.
- Predictive lead scoring platforms to predict leads: Infer, Velocity, and IKO System offer their solutions.
- The list of free predictive analytics software is significantly shorter but still impressive: Orange Data mining, R Software Development, and NumPy are popular names.
- Predictive analytics software API: Microsoft Azure, H2O.ai, Dataiku DSS to name a few.
- Predictive pricing solutions and customer renew, up-sell and cross-sell tools such as Luminate Market Price, Pricefy, and Google AI platform, RapidMinder Studio is popular names.
Data analysts benefit from those tools to make their life a lot easier. The other good news is that also business analysts can utilize (some) of the tools. There is less specialized knowledge needed to work with existing models. It lowers the bar to start using predictive analytics for a lot of people.
Predictive versus Prescriptive
A final note on predictive analytics and its successor. Predictive models generate accurate predictions of what will happen in the future. On top of that comes prescriptive analytics. This tells (advices) what you can do as a follow-up to events of the predictions. Again, this is complex and relatively new. However, organizations value it very much.
Conclusion
Predictive analytics uses large data sets to predict the future. It helps companies to become more efficient, pinpoint their customers’ activities, and reduces risks. You need great data-minded people and a large and reliable data set. Just like DevOps, feedback loops are important to constantly validate your data models and the outcomes. When done correctly, it really helps companies to take the next step in their journey to best serve their customers.
