Information intelligence: what it is and why it matters

Businesses around the world rely on data more than ever before. It’s a critical source of information that underpins a company’s operations, business decisions, and reporting metrics. However, without the proper tools and expertise, businesses can struggle to turn raw data into actionable insights. 

That's where information intelligence comes in.

In this article, we’ll explore what information intelligence is, why it matters, and how it works. We’ll also discuss some real-world applications and give you tips for implementing them in your own business.

What is information intelligence?

Information intelligence is the process of gathering, interpreting, and transforming raw data across all relevant internal and external sources. Business leaders then use this data to inform their decision-making and strategic planning processes and gain a competitive intelligence advantage in the market.

But information intelligence is about more than just data management. On its own, this process can help businesses keep their operations organized and streamlined, but if they don’t implement effective information intelligence strategies to create meaningful outcomes, it’s mostly useless.

It’s important to understand that data, information, and intelligence aren’t the same things. The process begins with collecting raw data, recording it, organizing it into datasets, and then using it to inform decisions. So, a business might begin the process by collecting a raw set of numbers or an email marketing list.

Companies develop information when they use data to generate statistics and answer relevant questions. For example, they might investigate the click-through rates (CTRs) on their newsletters over a given period of time. 

Intelligence then takes it one step further by using the insights gleaned from gathering information to make informed decisions on company strategies and directions. If a company sees that its newsletter engagement has decreased over time, it might decide it’s time for a revamp to give its readers a fresh perspective.
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Why information intelligence matters

Information intelligence management takes the guesswork out of the equation. Everything is much more clinical, with qualitative or quantitative evidence backing decisions. It will often result in improvements across many areas of business operations.

The right information intelligence can deliver significant benefits, including:  

• Increased efficiency and productivity: With the help of powerful artificial intelligence (AI) automation tools, businesses won’t need to burden their team members with carrying out these tasks. They can use AI to gather, interpret, process, and report on the data, leaving human agents to focus on higher-value activities.
• A competitive advantage: A business that utilizes information intelligence will likely gain important insights that businesses without an information intelligence strategy will miss. It gives them an advantage when it comes to identifying new opportunities and trends and enhancing the customer experience.
• Innovation: Information intelligence will uncover gaps in the market to be exploited, which can result in new avenues for product development, service improvements, and market expansion.
• Risk management: Businesses that use information intelligence are more likely to uncover risks before they become a reality, as the data will help reveal potential weaknesses or flaws in business strategies. These can include risks related to finance, operations, or those directly related to market conditions and projections.

These are just some of the key reasons why businesses that use information intelligence end up achieving a high level of success.

How does information intelligence work?

Information intelligence collection can be a complicated process. However, many AI tools are now available to help businesses automate, simplify, and speed up the process. Fortunately, the process is generally the same, regardless of what forms of data you’re gathering and which types of insights you’re hoping to uncover.

‍The main steps of the process include:

1. Data collection and integration
2. Data analysis and interpretation
3. Visualization and reporting
4. Actionable insights and decision-making

Let’s take a look at each of these in more detail.

1. Data collection and integration

‍The whole process starts with gathering vast amounts of data from both internal and external sources. Internal sources can include data from transactions, churn rates on a website, or anything that a business’s customer relationship management (CRM) service or data platform collects. Social media channels and news outlets are examples of external sources.

Once you’ve gathered the raw data, it’s integrated into the data processing software your business has chosen and standardized, making it easier to interpret.

2. Data analysis and interpretation

‍This is where raw data is turned into data information, primarily by using data and analytics tools that will interpret the data and transform it into something that your business can use.

This is also the stage where AI learning takes place. Machine learning, deep learning, and natural language processing (NLP) all need to incorporate raw data into their algorithms to identify patterns and trends in the data. Eventually, these algorithms give AI applications the ability to complete the tasks they were designed for.‍

3. Visualization and reporting

‍Once you’ve generated information from the raw datasets, you can represent it in graphs, charts, and reports to make it easier to understand and interpret. The insights you derive from these tools will help guide your decision-making. 

For example, you may want to know how customers are responding to a new product. You can gather data across product reviews, social media posts, and website engagement, and through an algorithmic process, discover the ratio of good and bad reviews. 

Presenting this information in a pie chart or with statistics will make it easier to see how successful the new product is. 

4. Actionable insights and decision-making

With the data now processed and the information now organized into a statistical or written form, your management team has all the information it needs to make intelligent, data-driven decisions about the company’s strategies and direction. 

For example, if you’ve gathered impressions across certain pieces of content, you can present them in a report highlighting the best content and identifying what isn’t performing as well. Then the team can use this information to start producing more of the successful content to drive more growth.

Real-world applications of information intelligence

It might not seem immediately obvious, but the information intelligence process is taking place in real time across every industry every single day. It can be something as simple as an inventory check to inform future purchasing or as complex as conducting epidemiological surveillance to monitor the spread of diseases. All of it follows the same process.

Let’s consider a few more real-world applications of information intelligence in different industries:

• Business and marketing: Developing personalized ads through analyzing customer behaviors, monitoring competitor pricing to inform pricing plans, and forecasting sales
• Healthcare: Predictive analytics for health diagnoses, hospital capacity, and resource allocation
• Law enforcement: DNA data for crime resolution, facial recognition, crime pattern analysis for hotspots, and policing resources
• Environmental: Temperature patterns for climate models and satellite and sensor data for natural disasters
• Cybersecurity: Data protection threat identification, such as malware and phishing campaigns, and real-time alerts for repeated login attempts 

What makes information intelligence possible?

There are a number of key information intelligence tools and technologies that all businesses will likely need if they want to power their operations with thorough information intelligence practices. These include:

• Data analytics software: This is the umbrella term for the engines that can collect and analyze data, integrate with data sources (SQL, Excel, etc.), and process the data ready for implementation.
• Machine learning: This is the most basic form of AI programming. It enables a machine to emulate human capabilities and complete single tasks without external input. A spam detection tool for emails is an example of machine learning.
• Deep learning: This is a more advanced form of AI. It enables a machine to use more powerful algorithms (known as neural networks) to complete more complex tasks. A virtual assistant would be an example of a deep learning application.
• Natural language processing (NLP): This is a form of AI used specifically for processing text-based data and information prompts to create responses that humans can understand. A website chatbot is an example of AI using NLP.
• Cloud computing: This is the overarching process of storing all data on a cloud database as opposed to using local servers and manual records management. All key business operations are completed via cloud applications as well.
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Get started with information intelligence in five steps

If you want to implement information intelligence into your own business practices, there are five fundamental steps to keep in mind:

1. Define your objectives: You first need to make sure you have a clear understanding of the outcomes you want to achieve with information intelligence. When you have a clear goal, you’ll know exactly what types of data you need to gather in order to achieve it. 

2. Assess your data landscape: Once you know the types of data you need, you have to locate it. This could be a good opportunity to audit your entire data landscape so you know exactly what types of data you have access to and where you can find it. It can be a complicated process, but RecordPoint’s data discovery software makes it easy.
   
   You also need to determine whether you have high-quality data, as outdated or missing data can affect AI machine learning and skew the end results. Our data minimization software is designed to help you identify what data is no longer necessary and set rules for when to remove it.

3. Choose the right tools: When you have a clear idea of your desired outcomes, you’ll be better equipped to determine which tools you’ll need to make it happen. Consider the three forms of AI discussed a little earlier — will you need to use just one or a combination of them? 
   
   If you have huge amounts of data to process, consider integrating it all into a CRM or a similar cloud computing technology.

4. Develop a strategy: Be sure to develop a plan for how the data will be collected, analyzed, and integrated into your decision-making workflows. Don’t forget to include timeframes for the entire process. It’s also a good idea to decide on the best forms of interpretation you’ll need to represent the results effectively. 

5. Start small, and scale up: If you’re completely new to using information intelligence, then focus on a narrow implementation and application, using only a few datasets. Once you can demonstrate that decision-making based on this intelligence achieves positive results, then you can consider scaling up into larger areas of the business.

Information intelligence best practices

As with any business process, there are good ways of going about things and approaches that are less optimal. Here are some best practices to guide you.

In brief

The reliance on data is a modern-day phenomenon that all businesses have to navigate. And while some businesses neglect the data and simply allow data to accumulate in unwieldy data silos, others are embracing it and using it to their advantage.

By investing in information intelligence, businesses can take full control of the direction of their operations by employing clinical, data-driven decision-making on strategies that will have the best chances of achieving excellent results.

At RecordPoint, we’ve been helping our customers organize and govern their data for over 15 years. We’ve built our entire business around helping companies achieve information intelligence across all business operations. Get in touch now to book a demo and see if we’re the right partner for you.

FAQs

Why do I need ‘clean’ data for information intelligence?

It’s vital that any data that’s allowed to pass through for processing is clean and accurate. This is because the quality of the data has a direct impact on the quality of the outcome or application. Outdated or incomplete data won’t give an AI algorithm, for example, everything it needs to learn effectively, which will make it less valuable for the business.

How is information intelligence different from business intelligence?

Information intelligence is used as part of business intelligence, but isn’t specific to just businesses. It’s used across every industry and sector in some capacity. Business intelligence involves analyzing and improving operational efficiency across all areas related to the business, including those areas that aren’t necessarily informed by information intelligence.

Can I use information intelligence even as a small business?

Yes, you can. All businesses generate data in some form. A small, independent corner shop, for example, will still have data on their earnings and their inventory that could provide insights into where they can make improvements.