In the age of digital transformation, information is paving the way for business owners to stay relevant and achieve success against competitors.
Data has quickly become the nucleus of any business.
In the age of digital transformation, information is paving the way for business owners to stay relevant and achieve success against competitors. There are systems and processes along the way to help businesses deal with such large amounts of data. For example, the process of collecting, analyzing, and storing data culminates into a well-built system or repository, such as a data lake.
Data lakes are implemented to manage raw data in an effort to enhance a company’s business intelligence (BI) and business analytics (BA) capabilities. Once that is in place, both internal and external users need a way to sift through the massive amounts of information and manipulate the findings to produce useful terms or reports. The effort to streamline and further enhance the quality of data is often delivered through software to create a semantic layer.
Semantic layers take the complex data and simplify it to create a unified, searchable pool of information. Data scientists are able to label certain sets of raw data into terms relative to the business, allowing users a more filtered result. Those filters can organize data in multiple ways, including by timeline, trend analysis, product usage, billing calculations and many other ways.
The benefits of a semantic layer are many. One of the most beneficial uses is the ease in which users can collect certain points of data with familiar search terms, without the need of being caught up in the technology used to store and analyze it. They also have the ability to access exactly what they need immediately, producing more efficiency in their work and providing better sharing and collaboration capabilities across the organization.
In addition, a semantic layer provides more security to stored data, which is extremely important, especially in fields such as finance and healthcare. With a semantic layer, information is pulled through the predefined search terms, creating more control over what is accessed. It prevents any compromises in the information database, eliminating instances of misuse, accidental changes or unneeded views.
As data becomes more secure and effectively used through the process of a semantic layer, it culminates into an experience that allows users to make better, more informed decisions about the next steps in the company’s growth in a more efficient manner.
In the technology industry, semantic layers are known as the “sources of truth” because they are built to return a desired field of information that proves to be useful in future decision making. It is real-time analytics that enables a user to measure the company’s true performance. While they are incredibly useful tools in providing a more focused look at data, it does take careful consideration when modeling them to ensure the most effective use of what they can offer.
The following are several factors that businesses should keep in mind when optimizing the use of semantic layers:
Businesses that are ready to implement semantic layers as part of the BI and BA strategy should first equip themselves with internal and/or external experts. This team can help determine how the system will align with the mission of the company. Company officials should ask how semantic layers can find and solve problems in relation to data management. They should also consider what issues they may have with security risks and how semantic layers can alleviate those problems. Finally, a company should realize how such a system can financially boost the bottom line.
Internal data scientists or external partners should be knowledgeable in both the reporting needs of the business as well as how to use the data pulled in from various sources. From there, the team can define the characteristics most needed by the company and implement the functions of the semantic layers based on those attributes.
Transitioning to a tech-centric business model entails more than just the IT department. One of the advantages of a semantic layer is the provision of ability to employees from across the organization to access data, analyze it and make decisions based on it. This provides more autonomy because they don’t have to rely on the IT department to provide the information to build the reports.
Developing a good sense of data education can provide internal users the tools needed to effectively use data that can then be shared with others in the company for further growth and strengthen decision-making capabilities to scale the business.
As semantic layers connect data from various sources to provide better data quality and business performance, the process has engaged a level of measurability that only the advanced technology can achieve.
This is why it is crucial that businesses continuously monitor semantic layers and make any changes or tweaks along the way to ensure the most effective use. The outcome of well-governed and closely-monitored semantic layers add to the overall unification of data sources. As those processes are reviewed and perfected, they can be used in other areas of data draws and analytics in a more efficient manner.
Better is always, well, better. Having search results that return the most useful and relevant data is key in outperforming competitors. Semantic layers are incredible tools no matter the industry in which they are used to stay ahead of the game.
Pairing other tech-centric resources and tools such as artificial intelligence and machine learning with semantic layers can elevate the value as it improves processes from data storage to organization to collaboration.
Whether a business is looking to take stock of data sources and streamline how they communicate with each other or use that data to push forward and scale their companies, or perhaps seeking all of those benefits, semantic layers are a wise choice to add to a business information technology system.
Data is the name of the game when it comes to doing business these days. No matter the sector, whether it is financial, health or retail, companies depend on large amounts of information to scale business and drive success.
Big Data is the practice of collecting information from both traditional and digital sources to identify trends that can help businesses grow and operate more efficiently.