Data modernization— specifically, data-first modernization—is central to successful digital transformation. With data growing at an explosive rate, businesses need more efficient ways to manage it and new ways to use it to glean deeper insights that can help fuel growth.
Organizations that adopt a data-first approach and harness the power of today’s new technologies are 20 times more likely to beat their competitors and 11 times more likely to exceed their revenue goals by 10% or more. Here’s why.
Why Is Data Modernization Important?
Data modernization is the process of converting data into a form that is easier to access, manage, and use. It eliminates silos and unlocks the value of data to enable faster decisions and better insights. Data modernization helps businesses achieve the scalability, agility, and speed they are looking for in cloud solutions.
Data modernization involves migrating data from legacy systems to a modern, often cloud-based, solution and merging data from disparate sources into one database to create a single source of truth. First, the data must be cleaned up, unified, and normalized across all fields and records. Then, it can be imported into the new system.
What Is Data-first Modernization?
Data-first modernization is a business strategy rather than a process. An organization that takes a data-first modernization approach understands that data is its primary asset. Putting data at the forefront generates valuable insights that drive better decision-making.
With data-first modernization, businesses can extract value from all their data, regardless of where it lives—whether on the edge, in a data center, or in the cloud. Companies can then leverage technologies, such as advanced analytics, to generate intelligence that accelerates business.
5 Important Technologies for Achieving Data Modernization
Many new technologies are driving data modernization in today’s business landscape. This shortlist summarizes the top five.
The Cloud offers a golden opportunity for businesses looking to move away from legacy technologies and on-premises data centers. Companies that adopt cloud solutions not only reduce the total cost of ownership (TCO) associated with IT infrastructure but also gain tremendous flexibility and scalability.
Modern cloud providers offer a full range of services that include Software-as-a-Service (SaaS) solutions, cloud-based storage and processing, and data modernization capabilities. The cloud enables businesses to take advantage of powerful new technologies, such as big data, advanced analytics, and artificial intelligence (AI).
If metadata is data about data, then what is active metadata?
Active metadata provides context that gives details about everything that is done to the data. It provides additional information that enables organizations to discover, categorize, maintain, use, and govern their data.
Metadata must be generated, assembled, and indexed continuously whenever data is created, updated, or moved. As the volume of data grows, so does metadata. This makes it crucial for businesses to replace traditional metadata management tools with a modern active metadata management solution that continually collects and analyzes metadata and creates intelligence from it.
Manual routine tasks make employees’ jobs boring and lead to fatigue, which increases the chance of human error. But with modern digital tools, businesses can automate those repetitive tasks and ensure greater accuracy. Automation frees up time, allowing employees to focus on more critical endeavors like business intelligence and data analysis.
But automation isn’t just for routine tasks. Companies can also automate complex business processes. For example, active metadata management can be automated using APIs.
Advanced analytics is a broad term that refers to three separate, but related, technologies: predictive analytics, prescriptive analytics, and artificial intelligence.
Predictive analytics uses data to create models of possible future scenarios. Leaders can then use these models to choose the best course of action, whether immediate or long-term. Prescriptive analytics uses algorithms to evaluate data and recommend ways to optimize business processes to achieve desired outcomes. It can identify the cause of bottlenecks and other problems and show how making strategic changes would improve various aspects of a business.
Artificial intelligence and machine learning
The most advanced type of analytics—artificial intelligence—includes machine learning (ML). AI differs from predictive and prescriptive analytics in three ways: it is capable of learning, evolves over time, and understands natural language.
AI and ML work hand in hand to drive business by providing convenience, accessibility, automation, and efficiency—all of which lead to greater productivity and better user experiences. Together, AI and ML can rapidly analyze large volumes of data, find connections between multiple data sets, and identify patterns and anomalies. Because AI can make decisions faster and more accurately than humans, it saves companies considerable time and money.
Data Modernization Fuels Business Growth
Data modernization fuels every digital transformation initiative, enabling organizations to gain valuable insights from their data. OneNeck IT Solutions offers advanced technologies like Azure Data Analytics that can help your business achieve a competitive edge.
Contact us to learn more about how OneNeck can help you scale your business and maximize performance.
Frequently asked questions…
What is the goal of data modernization?
The goal of data modernization is to transform traditional data architectures and processes into modern, more agile, and efficient ones that can support the growing demand for data-driven insights and decision-making. Data modernization involves migrating data from legacy systems to modern platforms, implementing advanced data analytics and machine learning capabilities, adopting cloud-based technologies, and ensuring data security and compliance.
What is advanced analytics vs analytics?
Analytics refers to the process of examining data to draw conclusions or insights about past events or trends. Advanced analytics, involves using complex and sophisticated techniques such as data mining, machine learning, predictive modeling, and artificial intelligence to analyze data and make predictions or recommendations. Advanced analytics allows organizations to gain deeper insights and achieve more accurate predictions, optimize business processes, reduce risks, and identify new opportunities.
What is difference between machine learning and artificial intelligence?
Artificial intelligence (AI) involves creating intelligent machines that can simulate human cognitive processes such as learning, reasoning, and problem-solving. Machine learning (ML) is a subset of AI that involves training computer algorithms to learn patterns in data, make predictions, and improve their performance over time without being explicitly programmed.
What is Azure Data Lake?
Azure Data Lake is a cloud-based storage and analytics service provided by Microsoft that enables businesses to store and analyze large amounts of structured, semi-structured, and unstructured data. It allows users to store data of any size, shape, and speed easily and securely, and perform powerful analytics and processing tasks.