Lean analytics offer promise to better serving citizens

The House of Representatives recently passed the Government Customer Service Improvement Act – a bill intending to improve customer service delivery across federal agencies. Millions of Americans depend on federal agencies for vital services, and delays in processing those requests often result in inconvenience, frustration and financial hardship. Passage of this legislation will make the federal agency customer service process more transparent and efficient while focusing limited resources on improving front-line customer service functions.

As an agency executive in charge of interacting with citizens and providing vital services, it’s appealing to make decisions based on real-time information from colorful graphical dashboards that provide detailed insight from customer care SLAs to social media reports; however, the reality is often somewhat different.

According to recent industry reports, we produce more data every other day compared to the amount of data produced from the inception of early civilization until the year 2003 combined. It is understood that the volume of data today has surpassed most data analytics offerings and available resources. Hence, we are facing a common “useful information” gap.

There is no shortage of customer data; however we are facing specific challenges with how quickly data moves, how fast the volume of data is growing, and how heterogeneous the data is. These factors have outpaced our efforts to keep up with data interpretation, and agencies are often challenged with the immense scale of data transformation that is necessary to produce useful information.

As a result, some agencies have a substantial gap in transforming certain sets of Big Data, including customer care information, and exploring groupings and relations within that data to help agencies respond to citizens’ complex business issues.

To get in front of this crushing flood of customer interaction data, an entirely new approach is needed – one that can be easily managed and is sensible, while being transparent no matter how big it grows to be.

That is the concept behind agile data analytics.

It scales itself to organization’s’ growing customer data needs, and makes it efficiently accessible and actionable.

To have useful information, IT contractors must use highly sophisticated processing, modeling and analytics capabilities to collect, organize and analyze data into manageable pieces

Enterprise repositories of customer information – including structured and unstructured data, often with heterogeneous format – should be consolidated and rapidly integrated into a single non-traditional data warehouse like Hadoop and NoSQL technologies that support the processing of large data sets across clustered systems.

Stay tuned – in my next post I will discuss the types of research and development that has been done on agile data analytics to help government contractors and agencies identify new insights and opportunities to improve customer service.