Data Intelligence: Components, Benefits, and Use Cases

The five components of data intelligence

Data intelligence is comprised of five major components within the financial and business sectors. These composing elements refer to specific data types as they are commonly utilized in different types of analysis processes.

1. Descriptive data

The first component is descriptive data, which generally refers to metrics that summarize and describe the features of a data set. In business, descriptive data is leveraged to produce descriptive statistics, that provide a useful summary of many data types, enhancing the success of predicting future outcomes through historical data relations.

2. Predictive data

This data type is used in predictive analytics and focuses on forecasting the probability of market events or decision outcomes surrounding various scenarios. Predictive analytics rely heavily on various AI-based technology and tools, and is becoming increasingly popular among business experts. According to Forbes, artificial intelligence has provided businesses with 10 ready-to-use applications, and this number is expected to grow. Among the most notable are the machine learning methods that help to build predictive models.

3. Prescriptive data

Along with the above-mentioned types of data analysis, prescriptive analytics has greatly impacted business analytics – the larger and more general analysis process that aims to discover data-based and actionable recommendations for companies. Prescriptive data is the kind of information that is used in the final step in this process – prescriptive analytics. Here simulation, modeling, and other techniques are used to recommend the next move that will benefit a business the most.

4. Decisive data

Decisive data is the kind of data that can be used in decision intelligence, a field that is rapidly gaining attention from businesses and investors. Decision intelligence utilizes achievements in various social sciences and decision theory and combines it with data science to improve decisions made in business as well as other fields.

5. Diagnostical data

Diagnostical data is information that devices, machines, and software collect and store automatically in order to for tracking and recording keeping purposes. Publicly available data from transportation vehicles, computers, and sensors are common examples of diagnostical data. This data is used to troubleshoot problems when errors and issues occur, providing data analysts and information technologists with data so they can troubleshoot and problem solve in a timely manner. 

Data intelligence benefits and use cases

Data intelligence is used both by businesses and investors. However, they each utilize and harness data intelligence techniques and strategies for a number of different reasons, all based on varying KPIs, objectives, etc. Here are a few examples of use cases that attract industry professionals to employing data intelligence.

• Market trends can be predicted with advanced business analytics, improving investment, and managerial decisions.
• Data intelligence enables companies to deal with massive data volumes with the help of machine learning and other artificial intelligence methods and tools.
• It’s all about improving the quality of decisions made in business. Companies can enhance this, with the help of public web data, historical data, and advanced AI technology.
• Data intelligence helps to analyze the existing and potential customer base thus enhancing lead generation, conversion rates, and customer retention.
• It also helps to make the business processes more efficient by providing a better understanding of what works and what needs to change which also leads to cost efficiency.

The future of intelligence in business

Clearly, data intelligence has critical role in the future of business and investing, as financial decisions are becoming increasingly complex and strategic. For this reason, it is projected that by 2023 about 33% of large enterprises will have analysts working specifically on decision intelligence, which focuses on improving decision-making with public web data. This makes all kinds of decisive data, including the eminent growth of public web data, an extremely important source of information for companies. 

Another characteristic of data intelligence that is already important but is likely to play an even bigger role in the future is cloud computing. It is expected that the market size of cloud analytics will grow to 65.4 billion by 2025. Thus, the development and increased usage of cloud technology will be a pivotal trend within data intelligence in the coming years. 

In relation to cloud computing, it is also worth noting the growing importance of mobile business intelligence.  As mentioned above, this is the tracking, storing, and organizing of publicly available business-relevant data collected on mobile devices, such as smartphones or tablets. Consequently,  in the upcoming years data intelligence will continue to reflect an increasing trend in globalized and mobile data-driven decision-making.