Automated Analytics - Future Necessity for Predictive Analytics
Automated Analytics, is a new category of analytics. Here automated action is carried out on the analytic results. More particularly, Automated Predictive Analytics are services that allow data scientists to upload data and rapidly build predictive or descriptive models with a minimum of data science knowledge.
In the real world, data are generated from multiple channels and forms (structured and unstructured). Analyzing and predicting models that are applied by various analytical tools are time consuming and require expert analytic skills. There are not enough skilled data analysts who can analyze all the data and take necessary action.
According to a market research study, data scientists typically spends more than 80% of the time in
- Data cleansing
- Adding missing data
- Removing anomalies
- Transforming data for specific algorithm requirements
- Running multiple algorithms in parallel to determine the best possible predictive models.
To improve the efficiency of data scientists, enterprises are adopting automation for the predictive analytics process.
Automated Analytics technique involves
- Model Scoring – Predictive models are developed offline which are uploaded to the model management system and scoring is built into an automated process.
- Unsupervised Learning – Anomaly detection, social network, topic modeling or taste profiling for personalization.
This process is achieved through:
- Automate the process of building the predictive models.
- Automate the process of embedding predictive models into applications.
The automation of building predictive models occur in several steps:
- Some of the analytic tools require data to be transformed in specific ways. This cannot be done manually by the analysts for every data set. The script for data transformation operations can be run automatically to ensure maximum number of possible models are available for any data set.
- The search for the best predictive model requires tweaking the search parameters and techniques. A bootstrap and automated approach can be used on the information from early iterations to specify the subsequent test runs.
- Automating the management of the various predictive models.
Once the predictive models are built, they must be embedded in other applications that utilizes the analytics data to produce an actionable insight that can be implemented across the organization to gain business value. This model translates into either business rules or as a scoring mechanism. Based on the predictive analytics outcome, leaders can take business critical decisions.
This integration implies that automated analytics need to be closely connected to IT organizations and CIOs. Automated analytics are no longer a separate and ad hoc activity. It is becoming an integral part of the business strategy.
The key features to automate a predictive model covers:
- Automated model-dependent data transformations
- Optimization across and within the techniques
- Intelligent heuristics to limit the scope of the search
- Iterative bootstrapping to expedite the search
- Massive parallel design
- Platform agnostic design
- Custom algorithms
It is predicted that by 2018, more than 70% of the companies will leverage predictive analytics for their business processes. The deficit of skilled human expertise for advanced analytics can’t be overcome in the near future, but we can provide the existing resource to build better models through automation.
Enterprises can benefit from Infoholic Research’s analysis of the Predictive Analytics and Automation market to effectively gain valuable business insights. Engage with our experts for a detailed discussion on the scope of Automated Analytics for your business.
- Shantha Kumari,
Sr. Technical Writer,