Make Data Work For You By Identifying Actionable Data

The above quote is often attributed to big data and analytics pioneer Clive Humby. And it’s become something of a truism in the business world. But what does it really mean? Is data really that critical to a business’s success? Yes, and no.

Data is only valuable if it can be turned into actionable insights. Too often, businesses collect data for the sake of collecting data but don’t have a plan for what to do with it or know how they can use it to improve their business. As a result, the data sits idly, gathering dust and taking up space.

This is where actionable data comes in. Actionable data is information that can be used to make informed decisions and take action. It’s the difference between data that just sits there and data that you can use to improve your business.

In this article, we’ll explore the process of turning data into actionable insights, what actionable data is, and how you can use it to improve your business. We will also review actionable analytics and the benefits to businesses. You can use the included table of contents if you want to skip ahead.

What Is Actionable Data?

The amount of data produced daily makes it increasingly difficult to understand and distill it into only the most actionable information. So what exactly is actionable data?

Actionable data can help you make decisions, take action, and improve outcomes in a business or other context. This data is usually measurable, quantifiable, and specific enough to help inform decision-making. Ideally, actionable data is also relevant enough to improve a current situation in a timely manner.

Data must be high quality to be truly actionable, meaning it should be accurate, complete, and relevant to the task at hand. It will also be actionable across different departments and teams within a company. This ensures that everyone is working with the same information and that decisions can be made quickly and efficiently.

The growth of technology has made actionable data more important than ever before. With so much information available, businesses need to be able to sift through it all and find the bits that will help them improve their bottom line. Actionable data can come from various sources, including customer surveys, financial reports, website analytics, and more.

A few examples of actionable data

There is a multitude of actionable data that is useful for businesses and organizations. This data can help decision-makers understand what is happening in their industry, make better decisions about strategic planning, and track progress toward goals.

Some common examples of actionable data include:

Sales data

Sales data is defined as the data captured during the sales process. This data can include customer contact information, product or service ordered, price, terms of sale, and date of sale. Sales data can be captured manually or through an automated system such as Customer Relationship Management (CRM) and Point of Sale (POS) systems.

Sales data provides information that can be used to improve the sales process. This data can be used to track customer trends, optimize marketing campaigns, and forecast future sales. Sales data can also identify areas of the sales process that need improvement.

Customer data

Customer data can be used to understand customer behavior, track customer satisfaction, and change marketing and customer service strategies. For example, customer data can segment customers by location, age, gender, or interests. The division of actionable customer data can help businesses target their marketing efforts more effectively.

Operational data

Operational data is defined as data directly related to the execution of specific business processes. This type of data falls into two categories: data that supports the execution of a business process and data that results from the execution of a business process. The first category would include information such as product specifications, customer information, financial data, and inventory data. The second category would comprise data like sales figures, production numbers, and shipping manifests.

Operational data is used to support the day-to-day operations of a business. This data is typically stored in operational data stores (ODS), which are designed to quickly retrieve data needed for business transactions. Operational data is also used to generate operational reports, which provide a snapshot of the current state of a business process.

Financial data

Financial data is defined as data that pertains to the economic activities of a business or entity and includes both historical financial information as well as current operational data. This type of data is used to make decisions about where to allocate resources, how to grow the business, and what strategies to pursue.

There are a few different ways to turn financial data into actionable insights. The first is to use trend analysis to identify patterns and understand how the business has performed over time. This information can be used to predict future performance and make decisions about where to invest resources.

Another way to turn financial data into actionable insights is to use comparative analysis to compare the performance of the business to other businesses in the same industry. This information can be used to benchmark the company’s performance and identify areas where improvements can be made.

Website traffic data

Website traffic data is the quantitative information website operators collect about the visitors to their site. This data can help improve a website in several ways, from understanding which content is most popular to determining what kind of marketing campaigns are most effective.

One of the most important uses of website traffic data is turning it into actionable insights. By understanding which content is most popular, website operators can make better decisions about what content to create and how to market it. They can also use visitor behavior data to improve the overall experience for everyone.

Marketing data

Marketing data is described as quantifiable information regarding a company’s marketing activities. This data can be used to measure the performance of marketing campaigns, track customer behavior, and understand which marketing strategies are most effective.

There are many different types of marketing data, but some common examples include conversion data, customer satisfaction surveys, and social media analytics.

Social media data

Social media data is data that is generated through social media interactions. This data can be used to track and measure the performance of marketing campaigns, understand customer sentiment, and create targeted marketing messages.

Some examples of social media data include:

• Likes
• Shares
• Comments
• Retweets
• Posts

This data can be collected through a number of different methods, including social media monitoring tools, surveys, and customer feedback forms. Social media data can help businesses and organizations identify influences, understand customer sentiment, and adapt their social media plans.

Employee data

Employee data is a subset of social data that encompasses all the data points generated by an organization’s employees. This can include everything from employee productivity and performance data to employee communications and interactions. Employee data can be highly actionable for organizations, helping them improve employee productivity, identify training and development needs, and foster better communication and collaboration within the workplace.

Examples of employee data include:

• Employee performance data
• Communications and interactions data
• Training and development needs
• Productivity data
• Employee satisfaction data

Qualities of actionable data

For data to be actionable, it must have certain qualities. These qualities set actionable data apart from other types of data and make it more useful for decision-makers.

Some qualities of actionable data include:

• Timely: The data is up-to-date, and the more recent, the better.
• Accurate: Inaccurate data can lead to bad decisions. You need data that is free from errors and provides a true picture of what is happening.
• Precise: The data provides specific information that is often quantifiable and can be measured and compared, making it easier for decision-makers to understand and use.
• Relevant: Actionable data is relevant to the situation at hand and can help you solve a specific problem.
• Consistent: The data is comparable across different data sets and can be used to track trends over time. This makes it easier to identify patterns and trends and take advantage of them.
• Available: Actionable data is available when it is needed. This means that decision-makers can access it promptly without delays.
• Verifiable: Decision-makers need to be able to trust the data they are using. Verifiable data can be checked for accuracy and correctness.
• Aligned: The data supports and helps achieve the organization’s goals. Data not aligned with business goals can be a waste of resources.

Benefits of actionable data

Actionable data can turn data into action. In today’s data-driven world, actionable data is more important than ever. With the right data, businesses can make better decisions, improve their operations, and take action to achieve their goals.

The following are the potential benefits of using actionable data:

Improved decision making

In today’s fast-paced and ultracompetitive business environment, organizations need to be able to make quick and informed decisions to remain competitive. With accurate and relevant data, businesses can make better decisions that lead to improved outcomes.

Increased efficiency and productivity

By understanding which areas of their business are performing well and which areas need improvement, businesses can make changes that lead to increased efficiency and productivity.

Reduced costs

Another key benefit of actionable data is that it can help businesses to reduce costs. Businesses can understand where money is being spent and identify inefficient processes. This can free up money to be reinvested in other areas of the business or used to improve profits.

Improved customer satisfaction

Faster and better-informed responses to opportunities and threats lead to better customer service. Taking advantage of actionable data enables a company to understand how customers interact with their brand, what they say about the company, and where improvements can be made.

What Is Actionable Analytics?

Actionable analytics is the process of converting data into insights that can be used to make decisions. This can be done through various methods, including data visualization, data mining, and statistical analysis. The goal of actionable analytics is to help businesses make better decisions that lead to improved outcomes. For example, a retail business might use actionable analytics to understand its customers better and make more targeted decisions about stock levels, pricing, and promotions.

Appreciating actionable analytics and how it can be used is one thing, but putting it into practice is another. Actionable analytics can be challenging to implement because it requires the ability to collect, clean, and analyze data. It also requires a certain amount of statistical knowledge and understanding of data visualization techniques.

However, actionable analytics is becoming increasingly important as businesses strive to make the most of their data. There are many ways to make actionable analytics more accessible, including using data visualization tools and employing experts in the field.

Benefits of actionable analytics

Many benefits can be derived from actionable analytics. The following are some of the most important:

Improves utilization of collected actionable data

Actionable analytics can help businesses glean insights from their data and improve business strategies. One of the most important benefits of actionable analytics is that it can help businesses make better use of the data they collect. Too often, businesses collect data but fail to take advantage of it.

Helps you identify the most critical tasks

With so much data available, it can be challenging to know where to focus your attention. Actionable analytics can help you to understand what is most important for your business to thrive and make decisions accordingly.

Enables facts-based decision-making

Actionable analytics enables businesses to make decisions based on facts rather than opinions. With actionable data, businesses can make decisions backed by data and evidence rather than guesswork.

Examples of actionable analytics

Most businesses today rely on data to make decisions. But for it to be useful, it needs to be actionable. As you strive to make better decisions faster, you’ll progress through four phases of actionable analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Descriptive analytics

This is the initial phase of actionable analytics. In descriptive analytics, you collect data and summarize it to better understand what has happened in the past. This phase is important because it helps you identify patterns and trends in the data.

Diagnostic analytics

Diagnostic analytics is a method of analyzing data to identify the cause of a problem and understand why it occurred. In diagnostic analytics, you drill down into the data to find the root cause of problems. When you know the root cause of a problem, you can take steps to prevent it from happening again.

Predictive analytics

Predictive analytics is defined as a statistical technique to predict future events. In this phase, you use historical data to build models that can be used to make predictions about future events. This phase helps you anticipate future trends and patterns.

Prescriptive analytics

In prescriptive analytics, you use data to generate recommendations about the best way to solve a problem. You identify the best course of action to achieve the desired outcome, making decisions based on data rather than intuition.

Tools you can use for actionable analytics

When it comes to turning data into insights that drive action, a few key tools can help. Here’s a look at some of the most popular options for actionable web analytics tools.

IBM Watson Analytics

Watson Analytics is a cloud-based analytical tool that makes it easy to get started with predictive analytics. It offers an intuitive drag-and-drop interface and comes with a variety of built-in tools for data preparation, visualization, and predictive modeling.

Google Analytics

Google Analytics is a free tool that provides detailed insights into website traffic and activity. It can track conversions, goals, and other essential metrics. It also offers features like custom reports and segmentation, which can be used to further drill down into data. Once data is collected, you can create meaningful reports and visualizations that can help drive actionable insights. Google Analytics can help you make better decisions about your website and online marketing efforts.

Tableau

Tableau is a paid tool that offers powerful data visualization capabilities. It can be used to create interactive dashboards and reports that provide insights into website traffic and activity. It also offers features like predictive analytics and real-time data that can analyze website performance. Using its AI-driven analytics, Tableau can help you uncover hidden trends and patterns.

Microsoft Power BI

Microsoft Power BI is a paid tool that offers similar functionality to Tableau. It can create interactive dashboards and reports that provide insights into any data. You can organize your unrelated data sources into actionable insights using Microsoft Power BI.

Components of making data more actionable

When it comes to data, there are a few key components that can make it more actionable. Here are a few examples:

Data querying

Data querying is the process of asking questions about data to extract specific information. This vital process allows you to get the information you need promptly and efficiently. This process also helps you get the information you need without having to wade through a lot of data irrelevant to your query.

Data algorithms

The heart of any analytics program is its algorithms, a set of rules or instructions for solving a problem or completing a task. In the context of actionable data analytics, algorithms are used to take raw data and turn it into insights that can be acted upon.

Different types of algorithms are used for different purposes. Some common algorithms used in actionable data analytics include:

• Segmentation: Segmentation algorithms identify different groups within a data set. This can be helpful for targeted marketing or for understanding which groups are most likely to convert.
• Clustering: Clustering algorithms group together similar data points. This can be used to find trends or patterns in data.
• Regression: Regression algorithms can predict future values based on past data. This can help forecast sales or understand how changes will impact a business.
• Anomaly detection: Anomaly detection algorithms are used to identify outliers in data. This can help identify fraud or other potential problems.

Data tracking

You can’t have actionable insights without data tracking. To get started, you need to understand what data you should track and how to track it effectively. You can use a variety of tools, including web analytics, Google Analytics, and more.

Once you have your data tracked, you can analyze it and look for patterns. This is where you can start to glean actionable insights from your data. To do this effectively, you need to have a good understanding of statistical analysis and data visualization.

If you can master these tools, you’ll be well on your way to delivering actionable insights that can help grow your business.

From Data to Insights: Turning Data Into Action

Raw data is useless if you can’t interpret it and discern what it means for your business. Many businesses struggle to do this, but you can unearth actionable insights from your data with the right tools and processes.

There are many ways to turn data into actionable insights. The most important step is having a clear understanding of what you want to achieve and what decision you want to make. Once you have that, you can look at the data and see what it can tell you.

One way to get actionable data insights from data is to use analytics tools like the ones mentioned earlier. These tools can help you see patterns and trends and predict future ones.

Another way to get actionable insights from data is to talk to people who are experts in data interpretation. These people can help you understand what the data could mean for your business and help you to identify any areas where you might need further research.

Turning raw data into actionable insights

The data is out there, and it’s growing every day. But what does it all mean? How can you turn all of that data into actionable insights that will help your business grow? Businesses often end up with a massive amount of raw data, and you need a plan if you want to take advantage of it.

Here are the steps for turning raw data into actionable data insights:

1. Define what you want to achieve

Defining what you want to achieve is the first step in turning data into insights. What are your goals? What do you want to improve? Once you know what you want, you can start looking for the data that will help you reach those goals. It is important to set measurable goals so you can track your progress.

A clear goal will help you focus on the right data and find the insights you need to achieve it. Measuring your progress will also help you see how well your data-driven efforts are paying off.

2. Collect the data that will help you achieve that goal

There is a lot of data out there, but not all of it will be helpful to you. Collecting the right data is essential to getting actionable insights. Start by collecting data from your own systems, such as sales, customer, and website data. Then supplement that with external data, such as industry or market data. For example, if you want to improve your website’s conversion rate, you might collect data on website traffic, conversion rates, and customer demographics.

The methods of collecting data will vary depending on the type of data you need. Some data, such as website data, can be collected automatically. Other data, like customer surveys, will need to be collected manually. Once you’ve collected the data you need, it’s time to start analyzing it. Simplifying your data collecting method will make it easier to get actionable insights.

3. Clean and organize the data

Once you have collected the data, it is important to clean and organize it. This will make it easier to analyze and find useful insights. There are many ways to clean and organize data, but common methods include sorting data by time, grouping data by category, and identifying outliers.

4. Analyze the data to find trends and patterns

After the data is clean and organized, it’s time to start analyzing. Look for trends and patterns in the data, and try to identify what is causing them. For example, if you see a decrease in website traffic, you might look at the data to see if it’s related to a specific time or page. If you find increased conversion rates, you might try to identify what changes on the website caused this increase.

At this stage, it is also helpful to visualize the data. This can be done using charts, graphs, and other data visualizations. Visualizing the data can help you see trends and patterns that you might not have been able to find in the raw data.

5. Present your findings in a way that is easy to understand

Once you have analyzed the data and found some insights, it’s time to present them in a way that is easy to understand. Use charts, graphs, and other data visualizations to help explain your findings. Try to avoid using jargon or technical terms. The goal is to present your results in a way anyone can understand.

Presentation is also important when it comes to getting buy-in from others. If you want others to take action on your findings, you need to present them in a way that is convincing and easy to understand. Make sure to highlight your top findings and explain why they are important.

Customizing your presentation for your target audience is also important. For example, if you are presenting to executives, you might want to focus on the business impact of your findings. When presenting to analysts, you might want to focus on the data and methods used to find the insights.

6. Draw conclusions from your analysis

After you have presented your findings, it’s time to draw conclusions and take action. Based on your analysis, you should be able to identify how to improve your business. For example, if you want to increase website conversion rates, you might need to make changes to your website design or copy. If you want to decrease customer churn, you might try changing your customer service strategy.

All conclusions must be backed up by data. Don’t make changes based on hunches or guesses. Always make sure that your conclusions are supported by the data you have collected and analyzed.

7. Take action based on your conclusions

Finally, don’t forget to take action on your findings. Actionable insights are only valuable if they are used to improve your business. Once you have drawn conclusions from your analysis, make sure to put those insights into action. Implement changes based on your findings and track the results. This will help you see if your changes have the desired effect.

Monitoring your results over time is also important. Things can always change, so you need to make sure that you are constantly monitoring your data to identify new trends and patterns and take action accordingly.

How Web Scraping and Proxies Can Help You Obtain Actionable Data

The goal of data analytics is to take the data you have collected and use it to improve your business. But how do you go from data to insights? One way is to use a process known as web scraping.

Web scraping is the process of extracting data from websites. It can collect data such as prices, product descriptions, contact information, and more. However, web scraping can be difficult because websites often have measures to prevent bots from scraping their content, like CAPTCHAs and rate limits.

One way to bypass these measures is to use a proxy server. Proxy servers act as intermediaries between your computer and the website you are trying to access. Your IP address will be hidden from the website, and you can bypass many antibot measures. They can also be helpful if you’re trying to access a website that’s blocked in your country. You can bypass any restrictions by using a proxy server from a different country where the content isn’t restricted.

A proxy server can also cache web pages that it has already fetched. If you need to access the same page multiple times, the proxy server can retrieve the cached version instead of accessing the page from the website each time. This can be a major time saver, especially if you’re scraping large websites.

There are many different types of proxy servers, so it is important to choose the right one for your needs.

Data center proxy

The data center proxy is a shared proxy service that helps you easily and securely connect to the internet from anywhere in the world. Their biggest drawback is that they are easily detectable by antibot measures. Most regular users won’t be accessing websites from a data center, which is where these proxies are hosted. However, they are very fast and often the most affordable option.

Residential proxy

Residential proxies are IP addresses provided by an Internet Service Provider (ISP). They are assigned to a specific physical location, which is why they are also known as “geo-specific” or “location-specific” proxies. Residential proxies are the most difficult to detect and block, making them the ideal choice for web scraping.

If you’re scraping data from a website that provides its content only to users in the United States, you can use a residential proxy with a U.S.-based IP address. This would make it appear as if you’re a normal user accessing the website from within the United States, and you would be able to view that content.

Rayobyte’s residential proxies are sourced from real people who are fairly compensated for the use of their IP addresses. Rayobtye also takes steps to ensure the owner is not inconvenienced by allowing proxy use only if the owner’s device is not currently in use, is connected to Wi-Fi, and is either 50% charged or plugged in.

ISP proxy

ISP proxies are a hybrid of data center and residential proxies. They are hosted in a data center, but an ISP issues the IP address. They are as fast as data center proxies but are not as easily detected and blocked by websites, making them ideal for web scraping.

ISP proxies are also more expensive than residential proxies. Still, they are much faster, rivaling the speed of data center proxies. Just make sure that whichever proxy you choose, you also pick a reputable provider such as Rayobyte to ensure your data is safe and secure.

Use Scraping Robot to make data scraping easier

Traditionally, getting actionable data has been difficult. It often involved hiring expensive consultants or spending hours trying to gather and analyze data manually.

Scraping Robot changes all that. The minds behind Scraping Robot are leading experts in data scraping, and the automated process makes it easy for you to get the actionable data you need. With top-notch infrastructure and capacity, Scraping Robot can gather and structure data more effectively than anyone else. This means that businesses of all sizes can rely on Scraping Robot to get the actionable data they need to succeed. Its team of experts is dedicated to providing the best customer service possible, so you can be sure that you’re always getting the most out of your investment.

Powerful proxy management and rotation features keep your scraping activities hidden from the target website, and there are server management features that make it easy to scale your scraping operations as needed. And with CAPTCHA solving capabilities, it is possible to scrape even the most challenging websites.

With Scraping Robot’s help, you can quickly and easily gather data across the internet and then use the insights gained to make informed decisions about your business. Whether you need to understand customer behavior or want to improve your marketing strategy, Scraping Robot can help.

Final Thoughts

Actionable data is essential for any business that wants to succeed. As we have discussed above, actionable data allows you to make informed decisions backed up by data, which can give you a significant competitive advantage. When combined with actionable analytics, you’ll be able to make decisions that positively impact your bottom line.

There are many ways to get actionable data, but web scraping is one of the most effective. And with the help of a reputable data scraping tool like Scraping Robot, you can get the actionable data you need to take your business to the next level. Reach out today to learn more about how this prebuilt scraper can help you get the most out of your data.

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