- Data intelligence refers to the dynamic collection of data points, the application of artificial intelligence and the related technologies & tools that a company uses to effectively consolidate its business decision-making.
- Data intelligence is essential to a company’s digital transformation and its growth in an ever-changing technological landscape.
A vast collection of raw, unanalyzed data can be helpful, but it cannot offer insights, assist businesses in making decisions or offer advice.
The envisioning of ‘data intelligence at work’ starts at this point.
The entire business landscape has undergone a dramatic change because of the varied digital-first technologies. Data, however, is what drives it. Today, data has become a crucial component of a company’s digital strategy. Businesses frequently utilize data smarts to race ahead in the competitive industry market.
What is Data Intelligence?
The methodology for delivering trustworthy, dependable business data is known as data intelligence. It can include metadata or information about the said data as well.
IDC, the global market intelligence firm first used this phrase, which claimed that it “helps enterprises answer six essential questions about data.” Consider data intelligence as the output or outcome of fusing the appropriate data, insights, and algorithms to create spectacular data-driven results. This enables process optimization, increased efficiency and innovation-driven growth for all data users.
Data intelligence is the collection of business data points, application of artificial intelligence and related technologies & tools that a company deploys to utilize, analyze and interpret data effectively, and also consolidate its business decision-making.
Data intelligence can be applied differently. Developing a data intelligence plan relies on the organization’s size, breadth and objectives. You can also be selective about the data intelligence tool you adopt. Seek to answer questions like – How do you decide which data intelligence tool is ideal for your business? And which tools can assist you in achieving your online objectives?
Note that selecting the appropriate data analytics tools is difficult since no product meets all requirements. Hence, once you have collected data, you may analyze it using various tools. Consider your organization’s business requirements and discover who will utilize the analytics product. Some tools are offered at no cost, while others need a license or membership fee. It is hard to imagine, but the costliest tools are not always the most feature-rich; thus, consumers should not disregard the numerous robust free options accessible.
According to Mordor Intelligence, the business intelligence market will be worth $40.50 billion by 2026 owing to its popularity.
Microsoft Power BI, SAP BusinessObjects, Sisense, TIBCO Spotfire, Thoughtspot, Qlik, SAS Business Intelligence and Tableau are a few of the most sought-after data intelligence solutions by enterprises. Data intelligence firms offer data intelligence platforms and solutions such as Data Visualization Intelligence, Strategic Data Intelligence, and Global Data Intelligence. However, keep in mind that various data intelligence products differ in terms of robustness, integration capabilities, technical usability and the cost.
Types of Data Intelligence
Data intelligence is a permutation and combination of the following data categories:
- Metadata management
- Data quality
- Data governance
- Master data management
- Data profiling
- Data curation
- Data privacy
There rests a question at the core of every business operation: What are the varied sorts of Data Intelligence? Consequently, the following is a list of unique data functions and applications:
- Descriptive Data – To examine facts and grasp performance
- Prescriptive Data – To generate alternative information and fresh suggestions
- Diagnostic Data – To discover why an event occurred and its causes
- Predictive Data – To evaluate previous information and forecast likely future incidents
- Decisive Data – To evaluate data value and propose new actions
Drawing Out the Business Benefits of Data Intelligence
Data intelligence and digital transformation are complementary concepts. It is a frequent and erroneous belief that only tech-savvy, digitally modern firms can profit from data insight.
The reality is that data intelligence clouds and solutions may do far more for your corporation than you initially believe. Moreover, businesses would gain only more from data intelligence investments.
You can transform raw data into very insightful and relevant information using high-quality data intelligence tools and platforms. Investing in data intelligence can provide enormous returns for businesses of all sizes and industries, and we’re going to get into the “whys” right now!
Trustworthy data and improved data quality: When it comes to data, your data is only useful if its quality can be trusted. Poor data quality can hinder business decisions in several ways. There are several reasons why data might be considered flawed. Bad data can influence corporate decisions and operations, provide erroneous or inaccurate information or provide obsolete analysis. Consequently, if you cannot rely on the quality of your data, you cannot rely on the outcome of any project that relies on that data.
A dependable data intelligence system can help you monitor the quality of your data, offer real-time lineage and cataloging to guarantee the data’s sources are credible and monitor the data’s evolution over time to provide more significant information regarding its quality.
With the proper data intelligence solution, you can effortlessly increase the quality of your data, making it a far more reliable source for your team. As is common knowledge, reliable data is the cornerstone of sound business choices. A wise decision is to utilize robust data quality tools that enable you to find, comprehend, and rectify data problems to improve decision-making and governance. DataOps guarantees that data quality is maintained across an enterprise-ready data pipeline to satisfy all business objectives.
Improves data accessibility:
If your organization desires to engage in digital transformation, develop a data culture and enable its workers to apply data and comprehend its essence, you need to have simplified access to data.
With an organized, streamlined framework for data intelligence, your data citizens will be more equipped than ever to access and comprehend the data they use.
Guides better business decisions: The capacity to trust your data, contextualize your data, empower your data citizens to utilize that data appropriately and cohesively, track your data, and overall have a more cohesive knowledge of your data – all lead to a single good outcome: the ability to make better business choices.
Effective, dependable, high-quality, correctly analyzed and streamlined data can help you get a clearer, more directional picture of what your data means and how it should channelize your actions.
No matter what your organization’s industry, size, breadth or specialization, highly useful, easily accessible and operationalized data may make a difference – from the beginning to the finish.
Data-driven decision-making (DDDM) is utilizing facts, measurements, and data to lead strategic business decisions that align with your goals, objectives, and ambitions. With a contemporary business intelligence system, data-driven decision-making is elevated from being a burden to bolstering the company’s goal. This results in quicker, better-informed judgments. And these decisions will result in a better bottom line, increased innovation and commercial success and increased employee engagement and cooperation.
One such example is the Lufthansa Group, a worldwide airline conglomerate that, at one point, lacked standard analytics reporting across its more than 550 offices. Utilizing a single data intelligence platform, their efficiency rose by 30%, they gained greater decision-making freedom and expanded departmental independence.
Data lineage and auditing: A high-quality data intelligence platform will assist you in storing, accessing and analyzing your data and comprehending its ongoing evolution.
The distinctive feature of data is that it is only sometimes simple to trace, source or trust.
Consequently, the ability to trace the lineage of a data collection can not only assist you, but give you vital answers regarding the context of that data, but also aid you when the time comes to audit the data’s credibility.
Enterprises must extract data from its source, convert it, and then put it into a data warehouse or BI tool before serving it to data scientists for analysis to gain significant insights from raw data.
Businesses would be flying in the dark without a data lineage tool that illuminates the data flows throughout a complex ecosystem of interdependent data flows.
Any data lineage tool should enable the mapping of the data lifecycle as it travels from its source to its final destination during the Extract Transform Load (ETL) data process.
Data lineage tools enable you to respond:
- Who accessed and modified the information?
- What modifications have been made to the data processes?
- How have these changes affected the existing state?
- When were these modifications made?
Use-cases of Data Intelligence
To improve decision-making using alternative data, 33% of big organizations will have decision intelligence analysts by 2023.
Cloud computing is another data intelligence facet that is likely to become more essential. Cloud analytics will reach 65.4 billion by 2025. Thus, cloud technology will dominate data intelligence in the approaching years.
Below are a few industries that Data Intelligence is helping to drive:
Supply Chain Management
Data from supply chain software is massive. Most don’t know how to use it to improve operations. Supply chain management network data intelligence anticipates business risk, reduces loss, and automates self-learning supply chains. It fosters real-time cooperation and creativity.
E-commerce
An e-commerce website’s success is leveraging customer reviews to understand their preferences and make successful judgments. Using ML and NLP, businesses can connect with customers, gather data and increase performance, customer engagement, service quality, support quality and sales.
Data Intelligence helps promote items, identify client preferences, address customer problems, enhance quality and services, etc.
- Amazon
More often than not, Amazon knows you better than you know – when making product recommendations. However, these customized ads are the product of data analytics that allow the retailing behemoths to see when you make purchases, how you rate them and how other consumers with similar purchasing patterns purchase. Amazon discovered, for instance, that customers who purchase a television also purchase a TV mount, which gave them the idea to cross-sell and promote these goods.
More than data optimization and data intelligence are needed in this scenario. It requires enterprise-wide data collation and interdepartmental cooperation plans, processes, and technologies.
Data intelligence entails more than adopting a new system and discarding all the gathered historical data. It requires going above and above to alter this dynamic, unleash the value of internal and external data, and convert it into a strategic and competitive asset.
Conclusion
To optimize data intelligence, the primary objective is to make it simpler for knowledge workers to locate the data they require, learn from it, contribute to it and draw inferences from it.
Data intelligence is not limited to a few executives or specific specialties; it is all-encompassing and helps reinvent every corporate activity. It empowers everyone to utilize data to solve issues, develop ideas and expand enterprises. It promotes streamlining of workflows to generate tangible enterprise value!
