Predictive analysis for project management
Predictive analytics and structured and structured data
The concept of predictive analysis is the idea of using data to make relatively accurate predictions of unknown future events. In the past, such a prediction would be deemed impossible because of two missing factors. The first one was a limited volume of data, which can now be overcome with big data. The second one is powerful analytical tools. These are capable of interpreting both structured and unstructured data to get actionable information. Here are several examples of how predictive analytics can be applied to project management.
Why Is Predictive Analysis Important for Project Management?
Every project is a significant investment of resources that should result in measurable results. By following this logic, it’s relatively safe to assume that the project could result in a net loss if things go wrong. This is where project risk analytics can be quite useful. Via this method, a manager would be able to examine how the project outcome may change under the influence of the risk event. This would help the team prepare for the worst-case scenario, thus making the project more resilient to this risk.
What Is the Difference Between Predictive Analytics and Statistics?
While it is clear that predictive project management is a superior method, many project leaders believe that they can replace predictive analytics with simple statistics. These two are not interchangeable. Statistics can identify correlations based on data - predictive analytics go one step further and predict outcomes based on predictive data models. The differences between predictive analytics and statistics run deeper than that, but that is the principle difference.
How Are Structured and Unstructured Data Different?
The biggest misconception about unstructured data is the idea that it lacks the structure of any kind. This is simply not true. In fact, rather than sticking to this strict division, it would be more accurate to talk about structured, semi-structured, and unstructured data.
- It is most commonly tabular
- It consists of relational databases
- It entails rows in a table that all have the same set of columns
Some examples of structured data are names, dates, addresses, stock information, etc.
- It doesn’t consist of data classified as structured
- It has some structure to it
Some examples of semi-structured data are JSON, XML, or .csv files.
- It is not organized in a pre-defined manner
- It consists of unstructured information
- Unstructured data examples are in various formats (audio, video, binary, text-heavy)
Some examples of unstructured data are texts, video files, audio files, or social media posts.
There’s also the fourth type, the so-called dark data, which consists of information that isn’t well defined but can be quite helpful, nonetheless.
Other than a division on these three sub-categories, it’s also important to stress the difference between relational and non-relational data. Relational data is a model process for easier interpretation and analysis. Non-relational data, on the other hand, is usually in its original format (or close to it).
Four Types of Data Analytics
Before we get to the question of organizing unstructured data, it’s important that we quickly address the issue of data analytics, in general. There are four types of data analytics worth addressing:
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
The simplest explanation would be to say that descriptive answers what, diagnostic explains why, predictive reveals what’s next, and prescriptive suggests an appropriate reaction. Together, they represent actionable project analytics.
The applications of this are numerous. They are pivotal in HR, predictive maintenance, customer lifetime value, finance, logistics optimization, and marketing. From this perspective, it appears that these analytics have the power to shake the very foundation of the business world by reinforcing its very backbone.
How to Deal with Unstructured Data?
While dealing with structured data sounds more or less simple and intuitive, figuring out how to handle unstructured data is not always as straightforward. According to some estimates, between 80%-90% of all data online is unstructured. With the use of an adequate Intelligent Information Management (IIM) platform, all this data becomes available. To put it simply, IIM represents a set of different processes combined with underlying technology solutions which are when combined used to handle all the company data.
The unstructured data analysis should be achieved through several relevant steps:.
- First, the analyst should set an end goal. You are dealing with numbers, and subjective estimation of whether or not the analysis was successful simply won’t cut it. So, you need measurable end goals.
- Second, you need to collect relevant data. On a project, the majority of relevant data would come from the analysis of internal repositories and databases. During a project, the analysis of unstructured data can help establish correlations between inefficiencies in processes and document workflow. As a result, it would drastically improve the efficiency of project workflow and accuracy.
- One more difference between structured and unstructured data is that unstructured data must be preprocessed/cleaned before the analysis. This will improve the results obtained through the chosen analytical tools.
- Finally, you need to pick an unstructured data analysis tool. Tools like MonkeyLearn, RapidMiner, and Power BI are just some of the options available, although a lot of people go straight for Excel and Google Sheets. The tool you choose can become a massive part of your digital arsenal, similar to your workflow optimization software.
The Mindset Problem
In 2021, the availability of structured and unstructured data is really not an issue, and neither is the availability of adequate analytical tools. So, what seems to be the problem? First of all, there are still not enough project managers with a complete understanding of the concept of big data. This prevents them from exploring and exploiting the concept entirely. According to some estimates, as much as 73% of company data is still unused for analytics. This is a massive waste of potential and can adjust only with a management mindset shift.
Both structured and unstructured data need to be approached carefully for your project’s predictive analysis to give accurate results. Learning how to treat this data, prepare it for analysis, and which tool to choose are critical factors in making all of it work. One more thing necessary for a successful predictive analysis in project management is a shift in mindset and openness towards new technological tools, trends, and concepts.
What are the types of predictive models?
There are seven basic types of predictive models. These are ordinary least squares, generalized linear models, logistic regression, random forests, decision trees, neural networks, and multivariate adaptive regression splines. Other, more commonly used names for predictive modeling are predictive analytics and (most frequently used) machine learning.
What are the three pillars of predictive analytics?
Data mining, machine learning, and advanced analytics. These three pillars also mark the maturity of data analytics both as a process and a trend. First, the data needs to be gathered and processed. Then, it needs to be utilized to serve statistical methods. Lastly, generate accurate predictions.
What are structured and unstructured data?
Structured and unstructured data are the two most common types of formats of data used in predictive analytics. Structured data comes in tables, and it’s easy to create mathematical relations between them. Unstructured data comes in all sorts of formats and needs further interpretation and organizational efforts in order to be used.
How is unstructured data used?
The most common method of using unstructured data is preprocessing it to make it easier to use or implement more advanced technology. Of course, there’s always the option of collecting data in real-time and transforming unstructured data into its structured counterpart. Still, such a degree of data processing can be challenging on a large scale.