Do data analytics really lead to better decision making?The claims of using Big Data to inform business decisions are well lauded. Frequently touted advantages and benefits include uncovering unexpected patterns and insights, improving the ability to make predictions, and creating a sturdy foundation for quantitative analysis and fact-based management. But the upfront investment is significant, both in time and cost. You may ask yourself, do data analytics lead to better decision making? And is that “better” effective and efficient enough to result in significant, quantifiable change?

CMA works with clients to create a data analytics solutions. It is our goal to facilitate a system that empowers effective, efficient decision making. Our clients are often in the public sector so our solutions often require cross-sector data analysis in a highly secure environment. Analytics are widely used at many levels of government. The goal is to support decision making in ways that substantially improve the agency’s services.

In our 20+ years of experience in data management, we’ve found that data analytics do work. Specifically, we’ve repeatedly seen data analytics giving organizations the ability to drives operating cost reductions and increase efficiency and effectiveness to serve their constituents.

We’re going to share with you two projects that helped position New York as a leader in containing costs and detecting fraud and abuse in HHS. Key challenges (for both projects) were about being able to gather together distinct but related data sources to aggregate, cleanse, and serve in a way so data analytics tools can reveal cross-dimensional analysis results.

New York State Department of Health: Medicaid Data Warehouse and Analytics

At the NYS Department of Health, we deployed the Medicaid Data Warehouse together with the OHIP DataMart analytics platforms, aiding the NYS Office of Medicaid Inspector General (OMIG) in the recovery of $879 million (for the year of 2013 alone).

OMIG has recovered more than $1.73 billion in improperly expended Medicaid funds (in a period of three years). As a result, NYS ranked #1 in the nation for fraud detection and recovery. Additionally, over a period of five years, NYS alone accounted for more than 54 percent of the national total of fraud, waste, and abuse recoveries.

We would call those successful results. Data analytics do lead to effective, efficient decision making, when properly planned and implemented. For more details, you can read our full Medicaid Data Warehouse & Analytics success story here.

New York City Human Resources Administration: Enterprise Data Warehouse Analytics Platform

At NYC’s Human Resources Administration (HRA), we developed an Enterprise Data Warehouse (EDW) with extensive reporting and analytical abilities, to support their business users, access large amounts of historical data from multiple systems to contain costs, place liens, detect fraud and abuse, increase productivity, and plan and evaluate programs.

The EDW is the foundation for data sharing across HHS agencies, to allow a more holistic view of the client and services being received. Overall, HRA has determined that data made available in the initial phases of the project helped yield $200 million in revenue. CMA’s solution for NYC HRA is estimated to have positively impacted revenue for the agency by over $1 billion to date.

The HRA EDW Analytics Platform also supports improvements in constituent services as well as agency efficiency and effectiveness. These are outcomes, from analytics, that directly benefited the constituents. For example: Helping clients obtain employment and to become/remain economically self-sufficient, and to ensure the integrity of public assistance and Food Stamp programs.

It’s also worth noting that the data that once took more than six weeks to access is now available within seconds. For more details on this NYC government “Best Practice,” you can read the entire HRA EDW success story here.

So, How Did We Do It?

CMA and our clients accomplished these successful data analytics solutions by following a comprehensive approach and methodology to building them. Our focus was on creating a system that remained robust and useful beyond the first set of queries asked of it. In short, we accounted for the future.

Check back soon for our next story in our data analytics series, “Understand the Cornerstones of Adaptable, Extendable Data Analytics Platforms” or contact us for more information.