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At present, 53% of businesses are in the process of adopting big data analytics as part of their core strategy – and it’s no coincidence.
To win on today’s information-rich digital battlefield, turning insight into action is a must, and online data analysis tools are the very vessel for doing so. The world’s inherent rise in digital transformation coupled with today’s consumers' appetite for the World Wide Web (and all entities either connected to it or surrounding it), there has never been a better time to utilize this raft of information for your advantage.
But with so many insights and such little time to analyze it, where do you even start? With data intelligence, you’ll be able to drill down into the insights that really matter to your company and use them to make informed decisions that will ultimately help you improve your business.
In this post, we ask “what is data intelligence?”, explore intelligence technologies, and consider how these offers continued value in a real-life business context.
Let’s start by looking at the definition.
What Is Data Intelligence?
Data intelligence is the process of using analytical tools to give meaning to massive amounts of business data. Thanks to intelligence technologies such as AI and machine learning, companies can turn their data into actionable insights to improve their performance.
This invaluable analytical concept drills down into the analysis of information to extract value and meaning as well as promote enhanced data-driven decision-making across the organization.
When collecting and curating digital insights for intelligence purposes, businesses turn to a variety of valuable sources, such as performance metrics, consumer-centric, periodic trends, and a host of other descriptive information sets.
In contrast to business intelligence, which is largely centered on making logical sense of data and presenting it in a digestible, widely accessible format, intelligence in data focuses more on analyzing the information in its purest form.
As such, data intelligence software covers five critical informational components: predictive, decisive, descriptive, prescriptive, and diagnostic data.
By adopting intelligence software and methodologies in your organization, you will gain eyes and senses you never knew you had – the same goes for information intelligence, a similar term, but slightly different.
We’ve explored the data intelligence definition. Now it’s time to look at the information intelligence definition.
What Is Information Intelligence?
Information intelligence is a branch of data-centric intelligence offering a wider scope for turning seemingly unmanageable data and transforming it into initiatives that are beneficial to the ongoing success of the business.
An official information intelligence definition from IGI Global suggests:
[Information intelligence is] the technique of turning large volumes of complex data into relevant and actionable intelligence in order to better manage risk and increase profitability.
What this means is that through information intelligence methodologies, it's possible to extract value from wider sets of insights to improve, enhance, and streamline core business initiatives. You can start by simply choosing from various KPI examples relevant to your business, or continue reading to delve into the benefits and real-life business scenarios of utilizing intelligence and data. But first, let’s explore these two concepts in more detail.
Difference Between Data, Information & Intelligence
If you are still confused about what is the actual difference between the two concepts we just mentioned, don’t worry, we’ve got you covered. In order to understand these two complex, but actually straightforward, concepts we first need to look into the differences between data, information, and intelligence.
If you have been in the analytical world for a while, then you have probably been bombarded with a bunch of concepts such as big data, BI, data science, and analytics, just to name a few. While these concepts might all be used interchangeably on some occasions, they do not mean the same. This is what happens with data, information, and intelligence. Let’s look at their main differences and how they work together.
- Data: It is a quantifiable collection of factual measurements or statistics from an event related to your business. Here we are not talking about analysis or insights, just about raw data coming from various internal and external sources selected by an organization.
- Information: On the other hand, information is the way to bring all the data together and give it meaning. In other words, grabbing structured data that has been organized in a way that has relevance to a specific context or narrative. The information is used to generate questions that are relevant to the growth of the organization.
- Intelligence: Lastly, intelligence is the process of using information to drive successful business decisions. Instead of telling a story, as you do with information, you paint a picture. It tells you what might happen with the help of advanced methods such as predictive analytics and AI. The important takeaway here is that intelligence is not a result, is the process that is derived from information, or in other words, the analysis process.
In short, these concepts do not mean the same, but they have the same importance. Get the wrong data and you will get meaningless information and intelligence which will lead to bad decision-making.
The Benefits Of Data Intelligence
We’ve delved into the meaning of data intelligence as well as information intelligence and considered how the two concepts overlap. Now, let’s look at the primary benefits of data intelligence tools and practices in the world of business.
- Adaptability: The digital world is in a constant state of evolution, which means that these days, businesses must be more adaptable to change than ever. That said, data intelligence solutions and practices offer the ability to transform raw data into actionable insights, spot trends, and drill down into invaluable consumer and data discovery processes. As a result, you will be able to put together continued improvements in response to the environment around you, making your business exponentially more adaptable and, in turn, increasing your chances of long-term success.
- Scalability: Expanding on the previous point, this access to smarter analytics will also offer your organization the adaptability to better respond to commercial growth, making it far easier to evolve and expand your practices, processes, and resources over time.
- Knowledge: By gaining the ability to structure sets of big data and dig deep into specific areas of the business, intelligence processes will provide you with a level of knowledge that will not only streamline your internal and external strategies but also allow you to discover groundbreaking new insights that could catalyze your commercial success. A 10% rise in data accessibility can result in a $65 million increase in net income for a Fortune 1000 company – a testament to the power of data-driven knowledge.
- Vision: Intelligence data analysis, if implemented wisely, can also offer an unrivaled predictive vision for today's discerning business. A recent study suggests that the use of predictive analytics in business can result in an ROI of up to 25%. By providing a means of analyzing trends, patterns, and specific shifts within particular segments of data, data intelligence services boast a viable way of crafting your business processes and marketing strategies to improve lead generation while understanding your target consumers on a deeper, more meaningful level.
How To Implement Successful Data Intelligence Systems For Your Business
In the past years, the use of data in the business context has become more and more popular. Being data-driven is no longer an added value but a mandatory requirement for businesses that want to succeed in this fast-paced environment. That said, the analytics journey comes with many challenges which include mining, cleaning, and analyzing, just to name a few. That's where the conversation about data intelligence systems begins. With the right system, companies can optimize their processes, increase efficiency, and drive innovation. Here we tell you how to build your own!
1. Have clear goals and desired outcomes outlined
As mentioned, data intelligence is concerned with providing context and meaning to large sets of unstructured data. With that in mind, a system or platform should be the means to making better business decisions. For that reason, the first step you need to take is to define clear goals and desired outcomes that you want from this process. This will help you have a clear mind and understanding of what your needs are and make choices based on that knowledge. For example, when choosing which software to invest in, it is fundamental to keep your needs in mind, as you can end up using a service that is way too complex or simplified. To avoid this, you can outline a roadmap that will help you make the right decisions. This leads us to our next point.
2. Do a governance roadmap
Data governance and intelligence are directly related. Governance has emerged as the answer to the growing challenges of managing big sets of information in a way that is efficient, secure, and accessible. Gartner defines it as “the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics”.
In practice, effective governance ensures data is trustworthy and doesn’t get misused. It sets clear responsibilities and roles to manage the data and ensures companies stay compliant with privacy and security regulations. For this reason, implementing a governance roadmap is a fundamental and valuable practice when we want to extract the maximum potential out of our data-driven efforts.
3. Gather data from various sources
Once you have outlined all steps and responsibilities coming with managing sensitive information. It is time to think about the data sources you want to use for your decision-making. Organizations gather data from several internal and external sources that can include customer behaviors, marketing, sales, finances, and HR, just to name a few. The problem comes when you face this mass of data and need to deal with it. For that purpose, you should consider software that help you manage massive amounts of information without the need for heavy manual work as this takes time and is prompt to human error. We will talk more about this at a later point.
4. Ensure accessibility and literacy
Once you have all your data organized and categorized it is time to provide context to it and involve the entire organization in the process. By giving each dataset a context and meaning you are making sure the information is accessible and understandable for everyone, which will make the decision-making process more efficient across the organization.
One of the greatest challenges organizations face when it comes to analytics is the knowledge gap also known as data literacy. It is the responsibility of the organization to provide its employees with the necessary tools and skills to integrate data-driven practices into their workflow. For that purpose, there are various initiatives that can be implemented, such as workshops or a data dictionary. Additionally, the company can invest in self-service BI solutions that make the approach to analytics more friendly and intuitive for every type of user. This leads us to our next and final point.
5. Invest in data intelligence software
A fundamental step to ensuring you have a successful data intelligence platform is supporting your strategy with the right technologies. Here, you need to think of solutions that will actually serve the intelligence purpose. For instance, BI tools focus more on organizing information and making it more accessible for any type of user. However, data intelligence focuses mostly on the analysis of the information available, therefore, the tool you choose should cover this aspect. Some of the attributes you should look for in data analysis tools include:
- Data connectors: As mentioned above, selecting the sources that you will use is an important step that requires a lot of manual work. That said, technologies such as modern data connectors allow you to connect various internal and external sources with just a few clicks. This way you don’t need to worry about manual tasks as the data updates itself, leaving you enough time to focus on other important analytical tasks.
- Cloud environment: Another important requirement of intelligence technologies is a cloud environment. Not only does the cloud offer users full access to their information from any location, but also a secure and private environment for data storage which is fundamental considering the rise in breaches and cybercrime.
- Predictive analytics: As mentioned throughout this post, the use of predictive analytics to drive meaningful insights from your business performance is the basis of data intelligence. With that in mind, an efficient tool should include predictive technologies powered by AI and machine learning to help users answer critical questions and make better decisions.
- Automation: When it comes to any data-related process manual tasks require a lot of time and skills, which leaves them prompt to human error. With that issue in mind, various data intelligence online solutions offer automation options in various areas such as collection, classification, and reporting. Like this, processes are fully optimized with a minimum error rate.
Data Intelligence Examples In The Real World
By now, it’s clear that intelligence data analysis provides a wealth of tangible benefits to those who embrace it. Here, we look at the use of data-driven intelligence in a real-life context, according to industry or sector.
The travel industry has long been dependent on using data to predict when people might travel, their reasons for travel, and what their specific needs might be to provide the best possible service at the best price point.
To help enhance their service levels and optimize their pricing strategies, many travel providers use intelligence information to examine historical data to understand times when there is more or less demand for tickets while tailoring their amenities or packages to suit the requirements of specific customers. By utilizing specific customer service KPIs, travel providers enhance their knowledge and adjust their strategies to be able to provide the best service possible.
Education is one of the world's biggest and most important sectors. Yet, educators have often failed to utilize big data intelligence to help them provide a more valuable learning experience to their students.
When students migrate from one classroom to another and meet different lecturers throughout their day, keeping track of an individual student’s progress can seem impossible. But through big data intelligence, institutions are now able to equip teachers with a deeper, more panoramic insight into their students’ academic progress, empowering them to spot potential weaknesses, provide support when needed, and tailor the learning experience to the individual.
Offering the right treatment or taking the right action at the right time is critical in the healthcare industry – sometimes, it can mean life or death. The ability to analyze complex, real-time datasets to improve decision-making is a much-needed asset.
Data intelligence technologies and healthcare analytics tools have played a pivotal role in improving the healthcare sector in a number of key areas, most of which you can explore in greater detail with our healthcare reports guide. Moreover, an excellent example of data intelligence technologies in healthcare comes in the form of our hospital dashboard.
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- Treatment Costs
- ER Wait Time
- Patient Wait Time
With overstretched hospitals, in particular, a great deal of insights get bounced from one department to another, causing fragmentation, which leads to inefficiencies across the board.
Focusing on treatment costs, ER waiting times, and patient waiting times, the hospital KPI dashboard provides a cohesive panoramic snapshot of critical data from one central location, offering a level of insight that significantly improves service levels while boosting economic efficiency.
The retail sector is the very embodiment of supply and demand. Data has long been used to understand consumer behavior, but with the right retail KPIs and data intelligence software, the level of understanding improves exponentially.
To illustrate this point, let's take a glimpse at our retail store dashboard – one of our most comprehensive data intelligence platforms that expound on retail analytics at its core.
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- Average Transaction Size
- Average Units per Customer
This data intelligence platform helps to identify invaluable customer patterns, gaining a deeper insight into buying behaviors and transaction values. Moreover, this intuitive piece of data intelligence software will help you gauge which items are most popular among your consumer base – priceless information for any modern eCommerce business.
The energy sector thrives on striking the best balance between cost and service. The vast majority of power plants or suppliers have a firm grasp of when demand is higher or lower. However, by using data intelligence software, companies can make energy provisions more efficient while driving down costs. By drilling down into historical demand, power plants can now predict minute-by-minute, hour-by-hour energy demands, according to factors such as the season and time of day, using this information to provide the exact quantity of energy required for specific purposes.
To demonstrate the value of data intelligence technologies in the energy sector, here’s our energy management dashboard for reference.
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- Power Cuts & Average Duration
- Consumption by Sector
A state-of-the-art data intelligence tool, this dashboard helps energy providers develop more sustainable initiatives that not only help the environment but also cut down operational costs and enhance the energy analytics process. Also, by being able to gain a greater understanding of consumption in particular sectors as well as power cuts and downtime, it’s possible to make your internal processes and practices significantly more efficient and productive.
So far, we’ve provided examples that can help businesses do their core activities better thanks to informed decision-making. Now we will cover an IT dashboard for an internal perspective on cybersecurity.
In the past years, cybercrime and data breaches have become a constant threat for businesses of all sizes. In fact, from 2020 to 2021 cyber attacks grew by 15.1% with a more sophisticated modus operandi which makes this matter a growing concern for organizations. As mentioned before, data intelligence software offers a safe cloud environment for big amounts of data, but also the tools to monitor the status of cybersecurity activities in real-time.
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- Cybersecurity rating
- Amount of intrusion attempts
This tool allows IT professionals to carefully monitor the performance of their various cybersecurity activities in real-time. This level of access allows them to easily spot any threats or actual attacks as soon as they arise and tackle them before they can harm the organization. The dashboard also provides insights into common threats as well as the time it takes the team to detect and resolve them so they can be prepared in advance to confront these issues.
Key Takeaways from Modern Data Intelligence Technologies
There’s no doubt about it: data intelligence is the way forward for today’s businesses, and regardless of your industry or sector, by embracing its power, you will enjoy a brighter, more prosperous tomorrow.
For more on how datapine can take your business analytics and intelligence to the next level, try our software for a 14-day trial completely free, and explore your data in just a few clicks!