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Whether you are running a test or using any project management suite or compiling the source code, you are generating data. Consider this. What if you could turn this data sets into useful information to identify patterns and predict future outcomes?
This where Predictive Analytics come in. Predictive Analytics is the use of statistical techniques of data, algorithm and machine learning to analyze current and historical data to make predictions about future outcomes and trends. Though this buzzword had been around for decades, it is relatively new to software development, especially mobile app development.
What does it do?
By employing Predictive Analytics in app development, you can optimize the app delivery pipeline with optimal estimates of time, effort, and cost; identify risks and opportunities; eliminate bottlenecks; and increase the quality and relevance of the app.
The application of Predictive Analytics in software/app development is termed as “Predictive Delivery”, which enables developers to deliver high-quality output quickly with limited risk and ambiguity.
So, how it works?
Using similar data in traditional analytics, such as hours required for app development lifecycle, amount of testing needed to produce a minimum viable product or expected bugs on the testing line, Predictive Analytics will help you predict the future behaviour of the data.
A predictive model often uses known results to create a model based on testing, validation, and evaluation to make predictions through a given set of input data. It is reusable and developed using trained algorithms from garnered data set. When you want to reuse it, instead of analysing the historical data, you can directly use the trained algorithm for the modelling.
The process involves running one or more algorithms during the lifecycle of a predictive model using current and historical data. It is an iterative process involving developing (training) the model, changing the models in a given time and determining the possibility of future events based on business understanding. It can be either directly used to extract an output or indirectly used to drive the choice of decision rules.
Predictive Analytics tools like Orange, RapidMiner and Apache Mahout use several algorithms to identify data patterns and understanding these patterns to make predictions about the data behaviour. Regression, Time Series, Decision Trees, Neural Network, Clustering and Association algorithms are a few basic algorithms used in the process of the predictive modelling.
What do you need to get it started?
Getting started with predictive analytics is no rocket science. Here is a 4-step process to get started.
1. Analyze – Why do you need to know about the future based on the past data? What do you need to predict and understand? What actions will be taken at the end?
2. Collect – Both structured and unstructured data (inputs) are to be cleansed and prepared for the predictive modelling.
3. Deploy – Refine your model in a way that it works on the chosen inputs. At the end of the process, you will arrive at a result (output).
4. Achieve – To turn analysis into real business results, you need an executive sponsor who can understand and manage both analytics and business.
Predictive Analytics has been used in many businesses to calculate risks, identify new opportunities, sharpen customer engagement, analyse market trends, manage supply-chain and measure consumer purchasing power and customer behaviour. Now the same process is being used effectively in app development.
Though the outcome of Predictive Analytics is seemingly a sequence of numbers, they make sense when they are put into a context. Remember Predictive Analytics is not a one-time process. You have to constantly work on the model to get accurate predictions. Make your every step the best step with predictive analytics!