In a nutshell:
- Predictive analytics can help businesses forecast trends and future behavior from historical data.
- To be ready for predictive analytics, companies need clear business objectives, high-quality data, sound technical infrastructure, analytics talent, data governance, a culture that embraces change, long-term commitment, and identified pilot projects.
- Having a sufficient quantity of high-quality data is crucial for successful predictive analytics implementation.
- A company's technical infrastructure must be able to accommodate a predictive analytics platform.
- Analytics talent is essential for translating business objectives into actionable insights from predictive analytics.
Predictive analytics sounds both magical and scientific. And indeed, there is something magical about technology that can forecast trends and future behavior from hidden patterns within historical data.
Predictive analytics can help your business forecast product demand to reduce overstocks and stockouts, identify overlooked customer segments to help marketers sharpen their efforts, and detect anomalies in patterns to identify fraud or emerging market trends — among many other applications.
However, benefiting from predictive analytics and all of these use cases requires more than waving a magic wand. Unfortunately, not all organizations are ready for it, even once they’ve invested in a predictive analytics platform (although choosing the right solution can help a lot).
To reap the maximum productivity gains, cost savings, and top- and bottom-line improvements, a company needs to be prepared in several ways. Eight ways, to be exact. Below are the eight signs your organization is ready to implement and deploy predictive analytics to maximize your data.
Predictive Analytics Readiness Sign #1: You have clear business objectives
“Our goal is to reduce subscriber churn.”
“We want to anticipate how much seasonal staff to hire.”
“Our sales team wants to focus on the best leads.”
These are a few of the clearly defined business objectives that predictive analytics can help facilitate. “I want to increase revenue 20%,” on the other hand, is too vague to be actionable. Increasing revenue might be a foundational strategic goal, but when turning to predictive analytics to help you achieve it, you need to be more specific in the tactics. How do you want to increase revenue: By raising prices? Targeting the right customers? Retaining more customers? Increasing average order size?
And yes, predictive analytics can help with each of those, but again, you need to know what you will use it for because building a model takes time — it’s an investment.
Your business objective needs to be well-defined, addressing a specific problem. Only then can you determine if anticipating future actions based on historical data will help you solve the problem and meet your objective.
Predictive Analytics Readiness Sign #2: You have a sufficient quantity of high-quality data
Predictive analytics requires data sets of sufficient size and quality for its mathematical models to work through in search of patterns we mere mortals can’t detect.
What is sufficient size? That depends on your business case and the type and quality of your data.
When thinking about the quantity of data, you need to consider not just the number of records but also the number and relevancy of the data points for each record. You could have one million names in your database, but if you don’t have other useful information about each name — demographics, past behavior, psychographics — that data set is likely less useful than one consisting of, say, 10,000 names for which you have psychographics, demographics, and at least three years’ worth of purchasing history.
Between your CRM system, ERP, marketing automation tools, and other systems, you most likely have enough data, and if not, you can probably acquire outside data to augment it. (You can use Pecan, for instance, with as few as 1,000 rows of data.) But whether your data is clean enough and of suitable quality to be actionable is another issue.
In a recent survey, 27% of respondents reported that their organizations did not have systems in place to ensure that data was easily accessible and able to be integrated. “Garbage in, garbage out” applies to any system that uses AI, and predictive analytics is no exception. Data from multiple sources needs to be standardized, and discrepancies need to be addressed. For instance, one source can’t use day/month/year format if the others use month/day/year.
However, there’s good news. The aforementioned survey showed that 42% of organizations do have systems in place to clean and prepare their data. This suggests that it’s definitely doable. What’s more, some predictive AI platforms, such as Pecan, automate much of the data preparation and integration that you’d ordinarily need a dedicated data analyst or data scientist to accomplish.
Predictive Analytics Readiness Sign #3: Your technical infrastructure is sound
Having all that juicy data doesn’t do you much good if your predictive analytics platform can’t access it — or if your other business intelligence tools cannot access the predictive models’ results. Your existing tech stack needs to be able to accommodate the new solution, which needs to easily communicate with your data warehouse, CRM platform, ERP, attribution tracking tool, and other relevant components of your technology infrastructure.
A benefit of Pecan is that not only do our prebuilt, no-code data connectors simplify the extraction and consolidation of the data, but with one click, you can also transfer the results from the platform to the relevant business systems within the infrastructure.
Pro tip: If your organization has put off reviewing the efficiency and effectiveness of your tech infrastructure, now is a good time to do so. You might find redundancies and obsolete components you can eliminate, saving you money and improving performance even before you add the benefits of predictive analytics into the equation.
Predictive Analytics Readiness Sign #4: You have analytics talent at the ready
Think of the analytics team as translators: They communicate your business objectives via queries and models that your predictive analytics tool can understand; then they convert the results of the models into insights that members of the other teams — such as marketing, sales, or procurement — can act on to achieve their business objectives.
To ensure that the “translations” are accurate, analytics talent must also continually monitor the quality and integrity of data. They should also collaborate with other teams to explore new and better ways to optimize the data and the predictive analytics tools.
Your analytics team — and in some organizations, it may just be a team of one — should possess technical skills such as SQL, at least a basic understanding of statistical analytics, and soft skills like problem-solving, curiosity, creativity, and teamwork. Learning a programming language or statistics is typically easier than developing softer skills like interpersonal communication.
So, to expand your analytics talent bench, consider training current employees who exhibit the valued soft skills and some of the hard skills. In fact, even novices can use intuitive platforms such as Pecan to build actionable working predictive models in less than a day.
Predictive Analytics Readiness Sign #5: You have an established data governance plan
By 2027, Gartner predicts that 60% of organizations will fall short of their AI goals because they lack adequate data governance frameworks. Similarly, 72% of businesses in a recent survey did not have a solid data governance plan in place.
To avoid being one of those organizations, ensure your data governance framework goes beyond safeguarding the quality and integrity of your data. It must also incorporate data security and privacy policies that include who can access data and how.
Don’t forget the importance of complying with GDPR, CCPA, and other data regulations relevant to where you do business. These are ever-evolving, so your data governance needs to include provisions for keeping updated on and adapting to changes. Data lifecycle management, auditing, and classification all come into play.
Implementing and enforcing a comprehensive yet flexible data governance plan not only protects you from significant government fines but also enables you to make the most of your predictive analytics investment. Just as important, it strengthens the trust your customers have in your business.
Predictive Analytics Readiness Sign #6: Your organization’s culture embraces change
Fears that AI will take their jobs cause many employees to resist its introduction into the workplace. Others may be intimidated by technology in general and worry they’ll be unable to learn how to use predictive analytics.
That’s one reason buy-in from the top is essential. Management can initiate any change management strategies necessary to make certain the organization views predictive analytics, or any other AI, as a resource rather than a threat. The leadership team can also determine if the org chart needs tweaking to facilitate the cross-functional collaboration required to achieve corporate goals.
Along with providing any necessary training and upskilling, the company should also empower employees to ask questions, experiment, and suggest additional applications. A culturally-ready organization is one that enables employees to experience how much of an opportunity predictive analytics is.
Predictive Analytics Readiness Sign #7: You are committed beyond the short-term
Many of us have bought a piece of workout equipment, committed to using it religiously for several weeks, then grew bored and stopped, to the point that it acquired a fine layer of dust.
Don’t let the same thing happen with your predictive analytics solution. To reap ongoing benefits, your organization must commit time, personnel, training, and other resources to the initiative before and after implementation.
Among other aspects, this entails setting aside time and budget for ongoing monitoring and maintenance of your data, security, regulatory compliance, and models. This might mean hiring someone to serve as a data steward or upskilling an employee to take on the responsibility.
Build in the resources so that users can stay up-to-date on predictive analytics techniques and, just as important, continue experimenting with the platform. This enables them to create new use cases and models that can pay off in additional revenue or savings you may never have considered.
Predictive Analytics Readiness Sign #8: You’ve identified potential pilot projects
It’s tempting to make your first predictive analytics a big, flashy one. But just as your first solo drive after getting your license was probably to a nearby store rather than a cross-country road trip, your initial projects should be small ones. These enable you and your team to get comfortable using the platform with minimal risk. Small steps lead to big wins.
The ideal pilot projects should reflect your overall business objectives and take place in a controlled environment that, as much as possible, replicates the conditions in which you’ll ultimately be deploying the tool. They should also be relevant to your anticipated use cases. If reducing customer churn is a top business goal, your pilot program should be within that area rather than, say, improving warehouse efficiencies.
Ready, set, predict
If your business is ready to benefit from predictive analytics, congratulations. Your next step is to schedule a demo with our team at Pecan so you can see how easy it is to integrate analytics and AI into your operations with a low-code Predictive GenAI platform. We're here to help you take action on your readiness for predictive analytics.
