Do you have questions about predictive analytics? You’re not alone! Read our latest blog to learn more
On one hand, predictive analytics promises to provide us with unprecedented insight into our future. On the other hand, it sounds really intimidating, overwhelming, and, to some even, impossible to implement.
The good news: a top-notch predictive analytics program requires far less data, effort, and expertise than you think — and has far more advantages!
In this post, we’ll debunk some of the most common predictive analytics myths so you can continue your question for innovation optimistically and with a solid understanding of this promising technology.
Myth 1: The value of predictive analytics is only the predictions
Don’t get me wrong. The predictions that predictive analytics produce ARE valuable. It is incredibly beneficial to know the trajectory of your financials and the likely results of a given scenario. But that’s not all! Predictive analytics also has the following benefits.
-It relieves you of time-consuming work: Predictive analytics process, transform, and learn from diverse sets of data using automation and far less manual effort. This allows finance members to focus on strategy, analysis, and recommendations.
-It allows you to identify interdepartmental correlations: Predictive analytics incorporates financial and operational data within your organization and external data from outside your organization, connecting dots between disparate data unidentifiable — or not visible — to the human eye.
-It answers more complex questions.
-It automates analytical processes that would take days or weeks to execute.
But most importantly: the biggest benefit of predictive analytics is when you can learn “the why” behind the predictions.
Best-in-class software uses machine learning to explain its predictions, telling you why it’s made its prediction. Once you know what is driving your results, you can then improve those drivers and change the future.
Myth 2: Predictive analytics is just traditional forecasting that’s more precise
Predictive analytics far extends the limitations of traditional forecasting. Here are just a few advantages:
Data breadth: Predictive analytics extend your forecast's limits to include operational, customer, transactional, and external market data. Traditional forecasting only uses basic information to predict future outcomes, like level, trend, and seasonality observations.
Inquiry complexity: Predictive forecasts can investigate multiple variables and complex ratios by using machine learning models to account for biases, events, and anomalies in the data. Traditional forecasts base projections on simple ratios or questions that have a single dependent variable.
Performance analysis: Predictive forecasts identify in-depth correlations between diverse data to reveal performance drivers you might not have expected. Traditional forecasts can only predict results based on the drivers you determine.
Myth 3: If you don’t have years of data on hand, predictive analytics technology won’t work
The golden rule for predictive analytics is, the more data, the more accuracy. But most of us will fall short of the ideal volume of data needed for precise predictions. And that’s ok!
Think of predictive analytics in terms of real estate. Most of us don’t move straight into our dream home. We start with a condo or a townhouse and level-up over time. To start using predictive analytics, you don’t need to have castle of data. You can develop a thriving program based on more humble data beginnings, and add on later.
Having years and years of historical company data, and data types that range from operational, to financial to external is the goal but it’s not necessary to start your predictive analytics program.
It’s best to start a predictive analytics program even if you don’t have large volumes of data because:
1. You can improve the data model incrementally. As soon as you implement predictive analytics, the data model starts learning from the new data you feed into it and becomes more intelligent and more accurate over time.
2. You still benefit in efficiency. Predictive analytics automates forecasting, allowing you to spend more time on strategy.
3. You still get insight into performance drivers. Explainable machine learning can still provide insight into what’s driving your numbers. With this knowledge, you can begin re-directing your business decisions towards a more favourable future from the get-go.
Myth 4: You need to hire a data scientist
Another case of advantageous, but not necessary. You won't need to hire a data scientist if your predictive analytics software:
-was created by both data scientists and finance experts
-has pre-built functionality that you can easily configure
-uses a data model that the vendor trains on your organization's data
-was purposefully engineered for specific finance uses, like budgeting, planning, and forecasting
-uses 'explainable machine learning,' allowing you to look under the hood for a detailed explanation of why the software made the predictions it did
Have more questions about predictive analytics? You’re not alone! Read our latest eBook: 8 questions finance needs answered before leveraging Predictive Analytics.