Anomaly Detection with Dataiku

Every enterprise organization is implementing some sort of AI and data analytics strategy. The understanding that AI and predictive models bring hidden benefits is as important to an enterprise as it is ignored. Years of collected data points are going unused and their benefits wasted. Organizations like The Law Society of British Columbia have recently implemented new data analytics systems to reap its benefits. Some of the benefits include autonomous data risk assessment by leveraging technologies like Dataiku and a team of knowledgeable data scientists. 

One of the most common use cases of data analytics for an enterprise is risk assessment. Through the use of correct technologies and savvy analysis it is possible to create autonomous systems of business-wide impact. One example of this, the risk factor assignment of clients based on past data. The Law Society of British Columbia is a recent beneficiary of Dataiku and its systems.

Anomaly detection, specifically, allows the finding of outlying consumer behavior. This results in the recognition of changing consumer patterns, potential opportunities, or obvious signs that were hidden in the data. In the next section we discuss how anomaly detection can be extrapolated into other processes that result in immediate business benefits. AI and analytics also provide many ways to automate currently manual analysis processes. 

Beginning the Benefits Of Anomaly Detection

Anomaly detection typically occurs after some cause that prompts alert. Through the use of AI, anomaly detection can be instead, a preventive cause. AI interfaces can be customized to alert when new patterns of behavior occur in an organization. The only requirement: previous data.

To begin taking advantage of these systems and leveraging technology to identify patterns and behaviors, only previously collected data is necessary. Once data has been localized for use, the next step is to define the business problem needing to be solved, improved, or analyzed for new benefitting patterns. 

The second step involves creating a team. Usually, the initial part involves a group of stakeholders, capable of defining the business problems to be solved.

The third step involves creating a more specific team composed of data scientists and data analysts. This team is responsible for choosing the correct software solution to process data into meaningful business processes and to have the troves of data in a legible format. Usually displayed through an interface in the form of reports and charts.

The fourth step involves the creation of business objectives by using the S.M.A.R.T. methodology of goal setting. This provides an efficient way to measure the benefits of this whole process before more time is invested.

Step five involves the assembly of all the project stakeholders. This includes the technical experts such as data scientists, analytics, project managers and the business-related decision makers previous assembled.

The five-step process ensures the initial bedrock of processes and processors able to transform data into practical enterprise-impacting results.

 

Business Use Cases

This section describes ways in which enterprises may benefit differently from data analytics and future financial conditions and the risks involved.

For example, predictive analysis can provide a myriad of previously hidden trends and factors. Using the example again of The Law Society of British Columbia, they have opted for risk analysis given previously recorded law firm data. The negative behavior patterns form the law firms have provided the society a risk factor of low, neutral or high, for example. This can be extrapolated into anomaly detection, lead heat level and many other uses.

We believe one of Dataiku's most valuable features is its flexible tooling, which can create solutions to multiple applications across multiple platforms. They’re also a well-established data science company that are also used as data storage in data scientists' environments like Big Data and NQA. You have the great opportunity to learn more about their solutions, their approach to their workflows, their use cases, and the amazing tools they come up with as they work to improve their product, as well as their insights.

 

Benefits of Dataiku For Predictive Analytics

There are several benefits of building predictive analytics solutions with Dataiku:

  • Cloud access ensures company-wide access into data as well as control.
  • Considering step three above, the process of ETL (data Extraction, Transformation and Loading) is greatly enhanced by the tools provided by Dataiku. These allow helpers for data cleaning, extraction and transformation.
  • A selection of AI models available form a common interface and easily interchangeable for comparison.
  • Model deployment with Kubernetes into the cloud under a common application.
  • Model upkeep. Automatic monitoring metrics of model performance for best business impact.

Dataiku also enables data-driven decisions, helping enterprises be more agile and efficient with the massive amounts of data they hold from their customers or for internal purposes. Dataiku also allows the reduction of data teams by concentrating efforts and automating models from pre-built solutions.

The benefits of a data analytics team and a large software interface are obvious. For more help see our next article and contact us for your data analytics needs.