6 Key Tools and Techniques for Taming Big Data

Using Big Data across the enterprise doesn't require massive investments in new IT systems. Many Big Data tools can leverage existing and commodity infrastructures, and cloud-based platforms are also an option. Let's take a look at some of the most important tools and techniques in the Big Data ecosystem.

1) Data governance. 

Data governance includes the rules for managing and sharing data. Although it's not a technology per se, data governance rules are enforced by technologies such as data management platforms.
"There's a lack of standards and a lack of consistency," explains Doug Robinson, executive director of the National Association of State CIOs (NASCIO). "There's certain data quality issues: Some of the data is dirty and messy and it's non-standardized. And that increasingly has made data sharing very difficult because you have language and syntax differences, the taxonomy on how information is represented.

... All that is problematic because there's no overarching data governance model or discipline in most states. Data governance isn't very mature in state government nor local governments today, and certainly not the federal government."

Data governance is critical to gaining buy-in from participating agencies for enterprise-wide data management. Before data sharing can begin, representatives of all participating agencies must work together to:

• Discuss what data needs to be shared
• Determine how to standardize it for consistency
• Develop a governance structure that aligns with organizational business & compliance needs

2) Enterprise data warehouse. 

With an enterprise data warehouse serving as a central repository, data is funneled in from existing departmental applications, systems and databases.

Individual organizations continue to retain ownership, management and maintenance of their data using their existing tools, but the enterprise data warehouse allows IT to develop a single Big Data infrastructure for all agencies and departments. The enterprise data warehouse is the starting point for integrating the data to provide a unified view of each citizen.

3) Master data management (MDM) platforms. 

With data aggregated into an enterprise data warehouse, it can be analyzed collectively. But first it has to be synthesized and integrated, regardless of format or source application, into a master data file. MDM is a set of advanced processes, algorithms and other tools that:

• Inspect each departmental data source and confirm its rules and data structures. Identify and resolve identity problems, duplicate record issues, data quality problems and other anomalies 
• Ascertain relationships among data
• Cleanse and standardize data 
• Consolidate the data into a single master file that can be accessed by all participating organizations
• Automatically apply and manage security protocols and data encryption to ensure accordance with privacy mandates

4) Advanced analytics and business intelligence.

High-performance analytics and business intelligence are the brains of the Big Data technology ecosystem, providing government centers of excellence with a comprehensive analytical tool set that leverages extensive statistical and data analysis capabilities. Through the use of complex algorithms, these platforms quickly process and deliver Big Data's insights. Functionality includes the ability to:

• Mine data to derive accurate analysis and insights for timely decision-making
• Create highly accurate predictive and descriptive analytical models Model, forecast and simulate business processes
• Apply advanced statistics to huge volumes of data 
• Build models that simulate complex, real-life systems

5) Data visualization. 

Data visualization tools are easy to use — often with point-and-click wizard-based interfaces — and they produce dazzling results. With simple user interfaces and tool sets, users of advanced business intelligence and visualization tools can easily:

• Develop queries, discover trends and insights
• Create compelling and dynamic dashboards, charts and other data visualizations 
• Visually explore all data, discover new patterns and publish reports to the Web and mobile devices 
• Integrate their work into a familiar Microsoft Office environment
   

6) Specialty analytics applications. 

Multiple analytics techniques can be combined to deliver insight into specialized areas such as:
Fraud, waste and abuse. By detecting sophisticated fraud, enterprises can stop fraud before payments are made, uncover organized fraud rings and gain a consolidated view of fraud risk.

Regulatory compliance. Analytics tools can help agencies quickly identify and monitor compliance risk factors, test various scenarios and models, predict investigation results, and reduce compliance risk and costs.

HR analytics. Hiring is critical to build capabilities quickly. Therefore it becomes important to hire employees who can meet its requirements and fit into its corporate culture. There in lies the challenge: "How to hire someone from outside - who has relevant knowledge needed in banking and who will fit in with the existing corporate culture." This challenge can be solved by using data analytics during the selection process.

Each BU will have several such tools and techniques that are important, but that can't be justified to create data silos. Breaking data silos, combined technology with analytics expertise, new organizational workflows and cultural changes to enable enterprise-wide data management.