The understanding that AI and predictive models bring hidden benefits is important to an enterprise. Years of collected data points that go unused are valuable benefits wasted. Organizations like The Law Society of British Columbia have implemented 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 for business-wide impact. One example includes the risk factor assignment of clients based on past data.
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. Additionally, 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.
Anomaly Detection Requires Previous Data
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 you're ready 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 previously assembled.
The five-step process ensures the initial bedrock of processes and processors are able to transform data into practical enterprise-impacting results.
Use Cases and Enterprise Benefits
Enterprises may benefit differently from data analytics, future financial conditions, and the risks involved.
Predictive analysis can provide a myriad of previously hidden trends and factors. Using the example of The Law Society of British Columbia, they have opted for risk analysis given previously recorded law firm data. The negative behavior patterns from the law firms have provided the society a risk factor of low, neutral or high. This can be extrapolated into anomaly detection, lead heat level, and many other uses.
Benefits of Dataiku for Predictive Analytics
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 used as data storage in data scientists' environments like Big Data and NQA. Through this tool you have the 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. There are several benefits of building predictive analytics solutions with Dataiku:
- Cloud access ensures company-wide access into data as well as control.
- Enhanced ETL (data extraction, transformation and loading) processes, allowing helpers for data cleaning, extraction, and transformation.
- A selection of AI models form a common interface and are easily interchangeable for comparison.
- Model deployment with Kubernetes into the cloud under a common application.
- Model upkeep and automatic monitoring metrics of performance for optimal business impact.
Dataiku enables data-driven decisions, helping enterprises be more agile and efficient with the massive amounts of data they hold from customers or for internal purposes. Dataiku allows the efficient operation 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. Learn more about our partnership with Dataiku and how we can support you throughout your Dataiku journey.