Big Data: The Challenge Of Managing Structured And Unstructured Data
Big Data
Big Data is a reality that organizations cannot ignore when it comes to capitalizing on data, one of the main assets of the business. But the adoption of technologies that satisfy this need is not a simple matter and, among other challenges, involves managing structured and unstructured data.
Leaping towards the new context created by the digital revolution means addressing the issue of hybrid systems. Suppose Business Intelligence focuses on analyzing the cause of a result based on structured sources of information, for example. In that case, the technologies that take advantage of big data are based on predictive analytics.
Confirmation versus discovery of the occult. In both cases, the analysis of data of different types is the key to business intelligence to improve productivity and decision-making to be more competitive.
Both systems coexist and must be integrated following an ad hoc design, which facilitates the fulfillment of business expectations. Ideally, from a data-driven approach, supported by a cultural and technological change in the organization and adequate data governance for its analysis and intelligent use.
Big Data: Trend Analysis
Within this new paradigm, the demand for data scientists continues to increase, and storage technologies and automated solutions allow access, integration, and analysis of different types of data, whose volume is growing exponentially.
Given that the traditional databases are designed to house and work with structured data, they are inadequate to handle massive data growth, all too often in unstructured form. They are, in short, a tool that cannot respond to the gigantic heterogeneous data that is included under the term Big Data.
In this regard, NoSQL, Hadoop and its rich ecosystem have proven beneficial solutions due to their scalability, low cost, efficiency and reliability, and the same can be said of the resources and services offered within the cloud computing framework.
Big data analytics seeks to establish patterns or profiles, find trends and, finally, process massive unstructured data from the point of view related to predictive analytics. But this does not mean a replacement for classical data analysis, and quite the contrary, in many cases, the best solution is to get the best of both systems.
An Advantageous Hybrid Combination
In effect, systems based on hybrid solutions are ubiquitous, both because it is a transition period and convenient to meet the company’s requirements.
That is, when the organization’s strategic objectives require the consolidation and integration of structured and unstructured sources of information, whether of internal or external origin. If, in short, among other needs, you seek to optimize processes within the organization, get to know the customer better and make better decisions, in batch or real-time.
When it comes to implementing Big Data technologies together with Business Intelligence tools and conventional data warehouses, ultimately, access to data becomes a significant challenge. And, with it, also their treatment to transform them into an asset that adds value to the company.
A challenge for whose fulfillment, timely access to reliable information, the design of an architecture in which different technologies have a place, and defining anew focus that goes beyond technological change, fundamentally oriented towards adding value for the organization.
Also Read: Big Data: Technological Challenges And Intangible Effects
