The average business user today needs more technical knowledge than ever because to the growth of self-service BI and analytics solutions. Workers from all departments utilize data to identify patterns, grasp opportunities, and streamline procedures. As business intelligence and embedded analytics become more interwoven into daily operations, employees will need to have a basic understanding of data literacy in order to respect governance norms and act on useful insights. Businesses have access to an increasing amount of data that can be utilized to better understand their customers and inform more strategic decision-making. Because they are provided in so many various formats, these enormous datasets, or “big data,” are challenging for organizations to manage and utilize.
When most people think about data, they see rows and columns of names and numbers in a database table. However, just 20% of data is thought to be structured, and that percentage is steadily declining. Businesses are now more than ever attempting to extract useful insights from unstructured data, which may be extremely difficult to standardize, store, and analyze.
Learning the differences between organized and unstructured data is one of the first stages in acquiring data literacy. We shall talk about the distinctions between structured and unstructured data in this post.
So, what exactly is structured data?
Numerical information that has been arranged such that a database can readily read and comprehend it is referred to as “structured data.” More specifically, because it adheres to a well-known data model, conventional structured data is typically machine produced to fit into a format like a table, making analysis of that data easier.
The sorts of data that are often found in Excel CSV files or SQL databases, which are typical instances of structured data, include financial data, contact data, and stock data. It is simple to aggregate data from many sources into a single database for rapid and efficient analysis thanks to labeled rows and columns.
Due to the consistency and structure of financial and transactional data, many businesses have experience using it for business decisions. It may be applied to modeling and assessment after data purification and standardization. Despite its value, structured data has several limits for contemporary enterprises, especially in light of the fact that business intelligence tools increasingly include AI and ML capabilities. If executives only concentrate on the organized data they have gathered, they risk missing crucial insights.
What exactly is meant by “unstructured data?”
The proportion of unstructured data in all corporate information is steadily increasing. Unstructured data refers to any data utilized by a business that is not in a typical spreadsheet or other specified data model.
Standardization is more difficult with unstructured data since it is often more textual than numerical or pictorial. This includes, but is not limited to, data from chat logs, emails, websites, and postings on social media. However, unstructured data also includes non-textual data forms including audio and video recordings, pictures, and scientific information. Any qualitative data that is challenging to standardize can be put into a spreadsheet under the unstructured category, where it can then be searched.
A growing portion of a company’s data collection is unstructured, making normalization necessary before analysis. Unstructured data may be a huge asset in helping firms make better strategic decisions if handled properly. If not, the expense and danger of keeping this data in noSQL databases and data lakes might swiftly rise.
Confused about semi-structured data?
Semi-structured data is typically categorized as unstructured even if it comprises both structured and unstructured data aspects. Both structured and unstructured parts may be found in semi-structured data, and while the structured elements can be read and analyzed inside pre-built and standardized data models, they seldom stand alone to fully represent the value of the data.
One kind of semi-structured data is the source code for web pages. HTML lacks contextual information, which makes it difficult for databases to comprehend. When a significant amount of data rearrangement is still necessary before it can be evaluated and insights can be gained, business intelligence (BI) tools might be helpful.
Inform businesses of the benefits of both organized and unstructured data.
Finally, organizations employ data analysis tools to enhance all aspects of decision-making. Adopting a data-driven culture gives businesses a major competitive edge because its executives can more readily identify and seize business opportunities, enhance customer service, develop more alluring goods and services, and formulate more efficient marketing and sales strategies.
Despite the fact that many organizations have depended on Excel models that make use of structured data, this sort of data only offers a little window into a company’s clients and the trends they adhere to. Business intelligence (BI) solutions like Qlik, Microsoft Power BI, and Tableau may help organizations find new insights rapidly by integrating the analysis of structured and unstructured data.
Although some unstructured data still needs to be normalized for analysts to use, technological advancements like natural language processing (NLP) are making it possible to transform unstructured data that was previously inaccessible into a format that business intelligence (BI) tools can understand. The next level involves BI tools using AI and ML to identify trends and get rid of unnecessary or irrelevant data. Business intelligence (BI) tools and embedded analytics may be used by analysts and business executives to query and view this bigger database in novel ways.
Companies can explore a bigger, more varied pool of data with the use of AI and ML to find new, useful insights. Predictive analytics is a feature of BI systems that uses AI to assist organizations in making better decisions and finding new income prospects.
In the past, many organizations were only able to use easily accessible spreadsheet data, which may take an analyst days or weeks to compile into a report. Business executives might spend days looking over various information to be ready for the following quarter or to make a vital choice. However, with the introduction of self-service BI technologies, both structured and unstructured data can be swiftly evaluated, allowing corporate decision makers to focus more on value-driven action and less on discussion.
Improving decision-making with both structured and unstructured data.
Business executives must make decisions every day. When making crucial judgments, certain historical leaders valued professional judgment. Without monitoring their performance using data, they might not be able to tell whether they made a wise business move until the revenue numbers change or increase.
As a result of the usage of both structured and unstructured data, company performance is no longer random. By applying cutting-edge business intelligence tools, professionals from around the organization may discover more about what is and isn’t performing. Additionally, these heads of state may receive personalized guidance from AI and ML on how to go forward and attain the greatest results.
When supported by facts, a leader’s business knowledge and skills might appear more credible. By studying customer data to understand their preferences and behaviors, businesses may strengthen their relationships with consumers and the quality of the goods and services they provide. When structured data and unstructured data are merged, employees can develop new perspectives on company operations, become more receptive to new prospects, and react more rapidly when a decision goes wrong.
Utilizing the data in your company begins with having the right software. Business intelligence solutions greatly simplify data analysis, standardization, and visualization. It is significantly easier to comprehend structured, unstructured, and semi-structured data when artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and other elements common to BI tools are included.
You should delve in to learn what effective data analysis can accomplish for your business. To explore your needs for business intelligence, contact us right now.
