5 Big Data Best Practices for Government Agencies

October 27, 2022

Big data refers to large amounts of information that can be analyzed in order to gain insights into human behavior. The term was coined by IBM in 2005 and has since become an important part of business strategy. Discover how government agencies can solidify their big data strategy with these five best practices. 

Know Your Audience.

As a government agency, you need to understand who your audience is and how they use technology. You also need to understand their needs so that you can provide them with the right services. This will help you build trust with your constituents and ensure that you are providing value to them.

The U.S Census Bureau has been collecting data from Americans since 1790, and it’s still going strong today. In fact, the bureau recently announced plans to collect data on people’s online activities, such as what websites they visit and which apps they use.

The new initiative is called the American Community Survey (ACS), and it aims to provide information on the nation’s population, economy, housing, education, health care, crime, and other topics. The ACS will be conducted every year, and the results will be used to help guide federal programs and policies.

The Census Bureau has been conducting the survey since 1940, but the 2020 census was the first one where respondents will be asked questions online. This change will allow people to respond from anywhere at any time, which should increase participation rates. In addition, the bureau plans to use big data techniques to improve accuracy and reduce costs. For example, the agency will use machine learning to identify patterns in responses and adjust the sample accordingly. 

So when government agencies ask themselves – do I know my audience – the Census Bureau has the data you need to get started.

Understand the Value of Analytics.

Analyzing data is an essential part of any business—including government business. Whether you are looking at sales, customer service, or product development, analytics helps you make better decisions.

The value of analytics is often overlooked because many people think that it requires advanced programming skills. However, there are several ways to use analytics without having to learn complex coding languages. For example, you can use Excel spreadsheets to analyze data. You can also use Google Sheets to create dashboards and reports. And if you want to get into the world of machine learning, you can use tools such as Tableau, PowerBI, and QlikView.

There are many different types of analytics, and each one has its own purpose. 

  • One type of analytics is descriptive analytics, which helps you understand what happened in the past. 
  • Another type of analytics is predictive analytics, which helps you predict what might happen in the future. 
  • *Descriptive analytics uses statistics to describe trends in data.* 
  • *Predictive analytics uses statistics to predict trends in data.* 
  • *Machine learning is a form of predictive analytics that allows computers to learn from data and then apply those lessons to new situations.*

The most important thing to remember when using analytics is that there is no single right answer. Every situation is unique, and every business needs to find the solution that works best for them. 

For example, if you want to know whether or not a particular product is selling well, you could use descriptive analytics to look at historical sales figures. If you want to predict whether or not a certain product will sell well, you would use predictive analytics. 

In either case, you would be able to get some insight into the current state of affairs. However, you wouldn’t necessarily be able to tell exactly why things were going wrong or what steps needed to be taken to fix the problem.

Create an Effective Data Strategy.

A good data strategy will help you understand how much data you need, where to store it, and how to analyze it effectively. It would be best if you also considered whether you need to use multiple sources of data and whether you need to combine different types of data together.

The first step in creating an effective data strategy is determining what kind of data you want to collect. 

For example, if you plan to create a campaign, you might want to gather information such as customer demographics, purchase history, and product preferences. If you plan to build a website, you might want to collect information such as visitor location, browser type, and operating system. 

Once you know what kind of data you need, you can determine which tools you need to collect it. If your goal is to find out where your most loyal customers are, you could use Google Analytics to track visitors to your site. Or, if you want to see which products sell well during certain times of day, you could use a tool called Google Trends.

When it comes to collecting data, there are two main types: structured and unstructured. Structured data includes things like names, addresses, phone numbers, email addresses, and dates. Unstructured data includes things like text messages, photos, videos, audio files, and documents. You can collect both kinds of data using different tools. For example, you can use Google Forms to collect structured data, while Google Photos allows you to store unstructured data.

The most important thing to remember when creating a data strategy is that you should always consider what kind of information you want to collect. If you’re trying to build a database of reviews, then you might want to consider asking customers to rate products on a scale from 1 to 5 stars. 

Build a Data Culture.

If you are going to collect data, make sure you build a culture within your agency that supports data collection and analysis. This means creating an environment where people feel comfortable sharing ideas and opinions, and where everyone has access to the same tools and training.

The first step to building a data culture is to create a plan. Start by defining what kind of data you want to collect and why. 

  • What questions do you want to be answered? 
  • Who needs to know the answers? 
  • How often do you need to collect the data? 

Once you have defined your goals, you should think about who will be responsible for collecting and analyzing the data. You might decide to assign one person to each goal, or you could divide the responsibilities among several people. Whatever approach you choose, make sure that everyone involved understands the purpose of the data collection and analysis.

The next step to building a data culture is to set clear goals. You should know what kind of information you want to collect, how you plan to use it, and who you want to share it with. 

And once you’ve created a plan, and defined your goals, now you can create a data strategy. A data strategy is a roadmap for collecting, analyzing, and using data. It includes defining the types of data you want to collect, determining which tools you’ll use to analyze them, and deciding who needs access to the data. 

Finally, you need to understand the importance of data governance, which involves establishing policies and procedures for protecting sensitive data.

Leverage Technology.

There are several ways to leverage technology to improve the quality of your data. 

  • First, use software that allows you to easily capture data. 
  • Second, make sure you have the right hardware to store and analyze large amounts of data. 
  • Third, make sure you have enough staff to manage the data and provide insights. 
  • Finally, make sure you have a plan to share the results with stakeholders.

The first step in leveraging technology is to find a tool that works well for your organization. For example, if you want to collect data from multiple sources, such as customer service calls, social data, and product adoption, then you should look for a solution that integrates all three types of data into one system. You might also consider using a cloud-based platform that provides easy access to data across different devices.

When it comes to leveraging technology, there are many tools available. However, some are better than others. 

  • For example, a CRM (customer relationship management) software program allows companies to track interactions between customers and salespeople. 
  • A content management system helps businesses manage large amounts of information. 
  • And a web analytics tool collects data about website visitors. 

These technologies help organizations gain insights into their business and improve marketing strategies.

The key to using technology effectively is to understand what it does and how it works. In addition, it’s important to know which tools work well for your company and which ones don’t. 

For instance, if you use a CRM software program, you should be able to see how much revenue each customer generates. If you use a content management system, you should be able to access reports that show how often people visit your site. And if you use a web analytics tool, you should be able to view statistics that indicate how many people visited your site during certain times of the day.

If you’re looking for a privacy-safe tool to get you from aspirational to operational, discover NOW Privacy — a single data discovery platform. It offers ultimate visibility across your structured and unstructured information. Visit here to learn more.

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