Automate [Decision Analytics System]

Implement the right IT system to deliver analytics from data to insight to value. Get a full suite of tools along with implementation and maintenance services delivered by decision analysts, programmers and technical support staff.

A decision analytics system is needed to manage the process that converts data to insights, insights to advice, advice to actions, and actions to results. This is a different function from other analytics and business intelligence tools, that enable data exploration, visualize data, generate forecasts, optimize allocations, etc. CoBots provide the modular and extensible system you need for analytics, with all the functionality you need to prepare data, generate insights, and convert insights into value.

There is a wide range of analytics tools for descriptive, predictive and prescriptive analytics, but you need Decision Analytics Systems to drive the process that connects data to advice and advice to results.

• Ingest data from any source, cleanse and prepare it for analytics
• Provide consistent, timely and trustable analytics to the right people at the right time
• Monitor value realization from the application of decision analytics
• Continually track system parameters for preventive maintenance
• Eliminate the drudgery of repetitive tasks of analytics and operations professionals, free them up to innovate and improve the decision analytics processes

A Decision Analytics System enables and supports the decision process. This system embeds best practices for driving business results through the application of data analytics, operations research, and decision science. It has the functionality to provide advice, track execution, and assess results to make decisions more effective.

• Advice is created on an ongoing basis using statistics, optimization, machine learning, data visualization, etc. Targeted advice is pushed to the right people at the right time.
• Execution is tracked for each decision. Advice translates to decisions when it is adopted, so we track adoption. Decisions drive actions, so we assess how actions align to decisions.
• Adaptation and value realization come from continual review. Value leakages are assessed and trends are monitored. Adaptation is enabled with tools to learn and evolve using techniques such as A/B Compare and Design of Experiments.
• Data is managed in a hub that automates the processes of data preparation and governance to convert raw data into reliable insights. The Data Hub analyzes its data processing to create a "data heartbeat”. Algorithms monitor the data and its heartbeat to assess data integrity and provide proactive alerts to data stewards. The continual monitoring provides ongoing metadata recording and validation. This automation helps data stewards to assure data quality and metadata reliability.

The project starts with a review of the decision analytics tools and processes for the selected decisions. Next comes the process of designing, building, testing and deploying the decision analytics system. Deployment can be as a VM or application on your cloud, in a public cloud such as AWS, in an appliance on your site. After deployment comes operations support, maintenance and enhancement. The system is built to use analytics to drive actions to achieve business goals.

• Starts with defining your business goals and assessment of your data
• Design the decision analytics model based on actions to be done
• Deliver dashboard with recommendation for action
• Clean, manage data in real time
• Constantly update model based on data updates
• Web based dashboards means it can be deployed anywhere (e.g., on mobile phones)
• Integration with data visualization and self-service data tools such as Tableau®, Spotfire®, etc.

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