Government agencies run the risk of being burdened by the massive amounts of raw data collected into its databases every day. With no way to effectively analyze this data, it can be both overwhelming and underutilized. With the aid of data analytics, IT-CNP can help government agencies use this information to benefit the causes they serve.
Explanation of Data Analytics
Data analytics is the overall process of using raw information to gain insights into an area of interest. Machines and algorithms aid us in transforming raw data to a level people can understand; these practices can allow government agencies to identify relationships or trends that would be almost “invisible” to the human eye. The information we glean from the process can then be utilized to understand a population, certain motivations, or to increase sales, etc.
Analyzing data can point out flaws in methodologies, thereby allowing a government organization to optimize performance which can improve efficiency and/or efficacy, save money, even make specialists safer. When implemented properly, data analytics allows for better decisions leading to higher satisfaction within the agency and outside.
For instance, in the government alone, data analytics has an abundance of uses. It can catch patterns of fraud and abuse of services being committed that a human analyst might not be able to manually detect. It can see trends in humans, allowing prediction of substance abuse based on prior or similar cases. Data analytics can even offer possibilities of how likely aircraft parts are to fail or generate augmented reality that will provide useful statistics to the user whether they’re in the field or a battle zone.
Basis of Data
The architecture of data, storing it, and related security all fall under the overarching umbrella of data management. Data design refers to the way data is structured and organized so the appropriate program can recognize and parse it. Data storage and security are two relevant challenges in today’s technology landscape. Data storage, how and where it is contained, can be a challenge. It is primarily determined by how often the information needs to be accessed or edited (versus archived data which doesn’t get changed); the chosen method must also offer an appropriate level of integrity so as to avoid data being damaged/destroyed. The security aspect comes into play by ensuring privacy, along with the aforementioned integrity, of data. This can prove to be another challenge for the government, specifically of data that may comprise varying security levels, including moderate or high.
Data mining involves examining large government databases to generate new information. Its basis lies in statistics, AI (artificial intelligence), and machine learning in which algorithms learn to make predictions from the data fed into the system. The federal government uses this process to find patterns and relationships by setting aside repetitive but less useful points to choose what’s more relevant.
Statistical analysis is the method of finding patterns and trends in the population by collecting and examining large banks of data; while visual analytics refers to analytical reasoning combined with some visual interface that can be interacted with, typically software such as SQL, R, SAS (Statistical Analysis System), Python, etc. This allows for storage and process capabilities of the data beyond what humans could manage.
Types of Data Analyzed
- Descriptive analytics—focuses on the passing of time and what has happened during that segment
- Diagnostic analytics—provides reasons why a certain effect happened, though this can be more hypothetical than concrete
- Predictive analytics—predicts what will most likely occur in the near future, usually based on what is currently happening that can directly affect the situation
- Prescriptive analytics—proposes a strategy or policy
- Outcome (consumption) Analytics—describes intentions of consumers picking a certain choice, i.e. outcome, allowing analysis of behavioral motivators
- Decision Support & Planning—when trends can be ascertained, this makes for better decision making based on facts
- Real-time Collaboration—allows for possible data patterns to be reviewed simultaneously by multiple agencies or organizations to work as a team
- Unstructured Data Capture—referring to data which is not organized (photos, emails, word documents) and cannot be easily assembled and loaded into a database to be searchable; this costs time and money to a business, as does the transformation to structured
- Structured Data Visualization—structured data is “made for” machines and computers; it folds into databases easily and can be searched instantly