What is Data Intelligence?
The world is leading towards data-driven intelligence. Organizations must make data and AI-based decisions to stand in the world of evolving technology and the competition phase. It becomes difficult for organizations that are not working on those aspects and data to know the facts and insights while making decisions.
It enables the process of multisource data and generates meaningful insights that would help to make valuable decisions. It allows combining unstructured data and text analytics results with structured data for predictive analytics. It can give a real-time statistical analysis of structured or unstructured data to understand data patterns and dependencies.
Why do we need it?
It is required to process and understand the data. It is rapidly becoming one of the most important elements of big data. It has progressed from the infantile stage to a point where it can handle vast amounts of data with intelligence. It will not fold its wings either; the immediate positive results have attracted many organizations' attention. Various entrepreneurs have expressed interest in using and developing it to make intelligent decisions in driving their business. There are multiple cases where we may need it. Some instances have been discussed that help to know why we require it:
Combining unstructured data and text analytics results with structured data for predictive analytics. Taken From Article, Data Intelligence Business Implementation Strategy
- Artificial Intelligence: Using a machine learning algorithm helps find the predictive analysis and recognize correlation. It helps to find domain-specific custom entities and word usage.
- Intuitive Visualization: It allows us to understand data effectively in less time using informative intuitive charts and graphs. Visualization helps to understand complex data within seconds rather than reading and understanding an excel or any other data file. Visualization also generates insights and clear data patterns that are difficult to find in tables or datasets. It enables to easily filter and drill down the reports according to the requirements.
- Insight Generation: Based on the collected data, it allows generating or taking insight from the visualization that helps understand business progress and customer needs.
- Data-driven decision-making: To make better and data-driven decisions so that correct decisions can be taken to gain customer satisfaction and revenue.
What are the types of Data Intelligence?
The major types of data intelligence are Descriptive, Prescriptive, Diagnostic, and Predictive.
- Descriptive: Using recent and previous data to find trends and links is known as descriptive analytics.
- Prescriptive: Prescriptive analytics aims to answer the query, "How do we get to this point?" It uses machine learning and other artificial intelligence (AI) techniques.
- Diagnostic: Diagnostic events are related to automated or manufacturing processes.
- Predictive: Predictive intelligence is a technique for tailoring a customer experience to a single person by tracking their activity and developing a profile of their preferences.
Deep Learning algorithms mimic human brains using artificial neural networks and progressively learn to accurately solve a given problem. Click to explore our, Deep Learning: Guide with Challenges and Solutions
How to implement Data Intelligence in an Organisation?
It performs the following steps to identify relations and mentions of unstructured or structured data.
- Data Ingestion: It collects structured and unstructured data from different sources such as documents, emails, databases, websites, and data repositories. Data can be inserted into the application or platform manually or scheduled at fixed intervals. Data can be processed and used by that application to perform tasks.
- Data Processing: Now, data collected from sources can be processed and generate insights. It makes it possible to find a relation between data. Several tools provide an easy-to-use interface for creating custom models to train and test the model to find entities and data relations. It allows the use of models for future predictive analytics.
- Reporting and Visualizations: Reporting and Visualization is the final step that analyzes the data using charts and graphs. Visualization makes it easy to understand large and complex data effectively.
What are the Challenges of Data Intelligence?
As organizations attempt to make sense of the enormous volumes of data they acquire, businesses face various challenges. Due to these obstacles, making DI operations efficient, effective, and valuable is more challenging.
Some of the challenges that might occur while adopting it are
- Data Silos and Data Quality Issues
- Inconsistent Data
- Using and Managing Data Intelligenc Tools
- Integrating Data from multiple sources
- Low Adoption DataIntelligence Tools
Analyze the data requirements that are required for the business processes. Click to explore about our, Data Modelling Techniques and its Tools
Prime Features of Data Intelligence Platform
Some of the prime features of it why businesses adopt it
are:
- Data Quality monitoring and improvement
- Track data lineage to ensure reliable and trustworthy data
- Facilitate superior data governance and stewardship
- Identify sensitive data that may pose a compliance risk
- Enhance the overall visibility of your organization's data assets
- Automate time-consuming tasks such as metadata tagging and updating spreadsheets
- Enable collaboration among IT and commercial enterprise users
and records governance teams
Benefits of Analytics and Data Intelligence?
It gives wings to the technology by providing intelligence in their daily tasks and decisions. Let's discuss the benefits of it that why organizations should embrace them:
- Changing demands: It makes the organization adopt the dynamic changes of the industries. The business nowadays is continuously evolving. To stand in the competition and reduce the chances of failure, organizations must accept and update the newly emerging trends. For example, the adoption of selfie cameras in smartphones was increasing. Mobile businesses that do not capitalize on the trend are doomed to fail.
It helps organizations to understand customer behavior and change. Firms are informed about repeated changes and the pattern of occurrence by smart adaptive dynamics. It allows the company to make informed decisions based on the analysis. - Strong Foundation of Data: It makes big data more strong and strengthened by restructuring the process of data arrangements. It allows to gather insights from big data and then render optimized engagement capability.
- Useful Data: No doubt the world is generating a large volume of data every day that can change the world and improve the services according to customer demands and preferences. But most of the data is not in the form of use. It is not possible to directly use that data.
It cleans and transforms data into smart capsules of ready-to-use data that can be used in the company to assess results. It is required to transform it into a helpful form to use data. It is also in charge of converting raw data into cumulative information. Data intelligence makes it possible not to worry about defining particular cases to computers. - Augmented Analytics: Advanced statistical approaches are used in it to advance visualized predictive and prescriptive analytics. Advanced simulations enable businesses to predict potential outcomes and make changes to prescriptions as needed. Instead of building a complete application every time, it automates the data processing that can be completed just by simple steps. If required, further changes may be recommended based on the results. There is no way for business plans to fail with such extensive planning for a real-life scenario.
- Accelerate Innovation: It makes it possible to accelerate innovations by making smart use of data. It allows using data insights to drive business innovations and develop their services by considering customer preferences and requirements.
The analysis and collection of customer data to understand the customer needs and interests, provide the best services. Taken From Article, Customer Intelligence Benefits with AI
Difference between Data Information and Intelligence?
- Data: It is the raw form of data recorded truth at a time. It might be a conversation, a purchase, or an interaction with your company's website. Data is the compilation of results from those incidents that are then quantifiably recorded so that companies can review them easily.
- Information: It is a collection of data or a way of bringing data together. When data is picked from an event and put into narrative forms, it helps to answer the following questions:
-What is the churn rate of employees?
-How long is the sales cycle of an organization?
The information helps to answer these questions that move the business. - Intelligence is a group of information to derive intelligence or decisions in their application or tasks. For instance, suppose you are selling more in southern regions, then the smart and intelligent answer will be why that might be. To get a response, it will look at the number of events, amount spent on advertisements, marketing campaigns southern region clients receive. After that, it can be compared with the other region(North region).
Through this analysis, we know that there are more client interactions in the southern region, so to increase sales in the North region, it is necessary to do the same.
Visualization is a conventional term for any approach to introduce information to an individual. Taken From Article, Real-Time Streaming Data Visualization
Use Cases of Data Intelligence
Helping the various industries as below mentioned
Healthcare
Rapid digitalization of healthcare systems are adopting technologies to create a connected healthcare environment. Hospitals need to synchronize with the technology to become smart, advanced, and accurate. Hospitals use various types of sensors, apps, and digital equipment that regularly generate a large volume of data. This data can be used to automate several administrative, treatment, and clinical processes. Its capabilities allow ML, AI, and Deep learning to make healthcare processes more accurate and fast and help practitioners handle the increasing number of cases and methods. These advanced technologies allow extract real-time intelligence and make decisions regarding the diagnosis process, prescribing medicines, hospital management, laboratory, patient care, etc., leading to high operational efficiency and care delivery.
Supply Chain Management
Supply chain software generates and collects a vast amount of data. But they are not aware of how they can best use it to make their operations more effective. The supply chain management network data intelligence predicts business risk, minimizes loss, and makes automated self-learning supply chains. As a result, it drives real-time coordination and innovations.
Human Resource
Organizations are using HR software to manage internal HR functions such as payroll, employee benefits, recruitment, training, talent management, attendance management, employee engagement, etc., to enhance their features and capabilities. They always have to do many tasks to understand employees better, attract top talent, and initiate programs to retain them and analyze their performance. They have a lot of data generated from their HRMS(Human Resource Management System) software. It can help them analyze and understand the data, gather insights, and make a precise decision that can make their organization drive healthier and faster.
E-commerce
One of the success secrets of an e-commerce website is using customer reviews to know their experience, preference and then use them to make profitable decisions. Using ML and Natural Language Processing techniques to interact with their customers, get data from them, and use it to drive performance, improve Customer Engagement, Service Quality, Support Quality, and ultimately Sales.
Data Intelligence makes it possible to accomplish these tasks, recommend products, understand customer preferences, solve their queries, improve quality and services, etc.
Harnessing this information can give you a treasure trove of insights that can power your products and processes, improve customer experience, marketing, manage store operation, etc.
What are the Business Data Intelligence services?
Data Intelligence delivers insightful information using artificial intelligence and machine learning tools, which transform massive amounts of data into intelligent information. We use these insights to improve our services and investments.
Some popular data intelligence tools are:
Tableau is one of the most popular service providers.
- Qlik: This high-performance platform represents how the data should get related in the best possible manner. Qlik boasts of an interactive visualization that further aids in effective decision-making.
- Datapine: This service provider stands the ability to let you connect your data from various sources and analyze them with advanced features.
- Looker: Looker has gained recognition as a data intelligence and visualization platform that allows users to combine, drill down, and analyze that data in real time in dashboards and reports.
Future of Data Intelligence
It is used by forward-thinking businesses to address these problems. Data governance, metadata management, and quality are all combined in it. It extracts "intelligence" from metadata, enabling businesses to grasp the nuances of their data and unlock its full potential. The digital age has delivered many innovations and data-driven technology. It involves monetizing data and using it to inform decision-making in the future.
Businesses that adopt its strategies have observed significant growth in increased efficiency, improved business insights, improved operations, faster decision-making, etc
Conclusion
XenonStack is a data intelligence platform that provides intelligence using analysis and learning by processing various sources.
- Explore here our Big Data Maturity Model and Assessment
- Read more about Responsible Artificial Intelligence in Government
- Discover here about the Augmented Analytics Latest Trends
