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Big Data Management: definition, benefits and strategies
For many years the priority of companies has been to obtain data, the only concrete and effective element to measure their business.
Thus, the validity or otherwise of the choices made and the direction taken by the organization, an obligatory step to understand how to act and what strategies to adopt in order to be competitive in the market and meet the needs of customers. Technological evolution has rapidly turned the tables, because with the arrival of digital and the ability to analyze single data from multiple perspectives, information has multiplied and with it the sources from which to obtain it.
Thus, data has become the main business asset.
A sort of lifeblood for the enterprise, which by studying the data can sense the acceleration to be given to the business, refine sales, figure out what services or new functions to launch, improve customer care, optimize the administrative department, and launch marketing campaigns tailored to achieve certain goals. In essence, with the knowledge and use of data, every company can find its way to make strategic choices with a high probability of success while reducing errors.
That is why, now that every company is constantly being reached by a huge amount of information, it is crucial for every business to be able to read data, an aspect that enables it to govern business trends and determine the steps to be taken to meet the company’s targets and profitability.
The ideal recipe, therefore, for achieving success.
Thus, the validity or otherwise of the choices made and the direction taken by the organization, an obligatory step to understand how to act and what strategies to adopt in order to be competitive in the market and meet the needs of customers. Technological evolution has rapidly turned the tables, because with the arrival of digital and the ability to analyze single data from multiple perspectives, information has multiplied and with it the sources from which to obtain it.
Thus, data has become the main business asset.
A sort of lifeblood for the enterprise, which by studying the data can sense the acceleration to be given to the business, refine sales, figure out what services or new functions to launch, improve customer care, optimize the administrative department, and launch marketing campaigns tailored to achieve certain goals. In essence, with the knowledge and use of data, every company can find its way to make strategic choices with a high probability of success while reducing errors.
That is why, now that every company is constantly being reached by a huge amount of information, it is crucial for every business to be able to read data, an aspect that enables it to govern business trends and determine the steps to be taken to meet the company’s targets and profitability.
The ideal recipe, therefore, for achieving success.
What is Big Data Management
To understand what Big Data Management is and its importance within any organization, it is necessary to take a step back and think about the concept of big data, which has become central to any business.
As anticipated above, the massive amount of information circulating nonstop determines the need to find an effective way to navigate and identify such data.
That is why with the term big data we wink at computer science but also at statistics, as data collection is characterized by the enormous volume available and the equally large variety within it. A maremagnum that is too dense to be able to identify interesting information and give it an order of importance with respect to a company’s activities, objectives and area of expertise.
Net of the different business needs, however, big data is united by the analytical methods and specific tools useful for extracting valuable information from the mountain of available data (the set of these techniques is called Data Mining).
Specifically, then, in addition to identifying the data, there is the need to integrate the most relevant data together, that is, to relate heterogeneous data, whether structured or unstructured, to highlight correlations and trends with which to predict future scenarios and act accordingly. Hinting at how central data is to defining business strategies is the list of new professions that have arisen with the advent of digital and the multiplication of information: Data Scientist, Business Intelligence Analytics, Data Analyst, Operations Analyst and Data Engineer are just a few of the new roles whose importance continues to grow within companies. Returning to the initial concept, it is now easier to understand what is meant by big data management, which indicates the organization, administration and management of large volumes of data in order to ensure a high level of accessibility and quality for analytical applications.
When integrated properly, it is easy to see how big data management can prove to be a crucial element in facilitating companies to acquire value, to be spent to attack the market with new and targeted business plans, conducive to increasing revenues but also brand reputation. What has been described can happen as long as a big data management strategy is established that, along with analytical objectives, safeguards information with data processing that complies with current regulations, starting with GDPR, the General Data Protection Regulation applied in all European Union states since May 25, 2018.
Demonstrating the weight assumed by the matter in business terms is the 18 percent growth in Italian companies’ spending on Infrastructure, Software and Services for data management and analysis recorded in 2023.
According to the Big Data & Business Analytics Observatory of the School of Management of the Politecnico di Milano, the sector, which has been characterized by the rise of generative artificial intelligence over the past 12 months, has reached a value of 2.85 billion euros.
As anticipated above, the massive amount of information circulating nonstop determines the need to find an effective way to navigate and identify such data.
That is why with the term big data we wink at computer science but also at statistics, as data collection is characterized by the enormous volume available and the equally large variety within it. A maremagnum that is too dense to be able to identify interesting information and give it an order of importance with respect to a company’s activities, objectives and area of expertise.
Net of the different business needs, however, big data is united by the analytical methods and specific tools useful for extracting valuable information from the mountain of available data (the set of these techniques is called Data Mining).
Specifically, then, in addition to identifying the data, there is the need to integrate the most relevant data together, that is, to relate heterogeneous data, whether structured or unstructured, to highlight correlations and trends with which to predict future scenarios and act accordingly. Hinting at how central data is to defining business strategies is the list of new professions that have arisen with the advent of digital and the multiplication of information: Data Scientist, Business Intelligence Analytics, Data Analyst, Operations Analyst and Data Engineer are just a few of the new roles whose importance continues to grow within companies. Returning to the initial concept, it is now easier to understand what is meant by big data management, which indicates the organization, administration and management of large volumes of data in order to ensure a high level of accessibility and quality for analytical applications.
When integrated properly, it is easy to see how big data management can prove to be a crucial element in facilitating companies to acquire value, to be spent to attack the market with new and targeted business plans, conducive to increasing revenues but also brand reputation. What has been described can happen as long as a big data management strategy is established that, along with analytical objectives, safeguards information with data processing that complies with current regulations, starting with GDPR, the General Data Protection Regulation applied in all European Union states since May 25, 2018.
Demonstrating the weight assumed by the matter in business terms is the 18 percent growth in Italian companies’ spending on Infrastructure, Software and Services for data management and analysis recorded in 2023.
According to the Big Data & Business Analytics Observatory of the School of Management of the Politecnico di Milano, the sector, which has been characterized by the rise of generative artificial intelligence over the past 12 months, has reached a value of 2.85 billion euros.
Benefits of Big Data Management
In a world, corporate and otherwise, driven by data, it is evident that effective big data management can generate a number of benefits to businesses, providing a number of major advantages over the market in which they operate and their competitors. First, the right approach to managing big data means better analytical and business intelligence capabilities, which enable the company to gain strategic insights into its operations, customer behavior, and market trends.
This translates into increased performance and the ability to make more informed decisions, by virtue of available information and historical data. But there is more, as having valuable data generates better operational effectiveness, with the streamlining of operations and identification of inefficiencies in the supply chain leading to cost savings and faster response to market transformation and customer needs.
Related to the latter point, then, is the opportunity to personalize offerings and optimize the customer experience, aspects that are easily improved when one knows the customer’s preferences, habits and weaknesses. Another predominant aspect concerns the scalability of the system, as appropriate big data management methods are designed to be scalable, enabling organizations to handle increasing volumes of data without hassle.
Other important benefits take root on this basis, starting with the ability to drive innovation, as the availability of detailed information enables the design and development of new products and services tailored to customer preferences. Looking inward, on the other hand, skillful big data management and the resulting predictions help reduce business risks, not to mention an additional key aspect that relates to data security.
Effective data management practices facilitate cybersecurity activities, making IT infrastructures more secure in the event of attacks by cyber criminals, limiting vulnerabilities that foster potential breaches that are harmful both financially and to the company’s image.
This translates into increased performance and the ability to make more informed decisions, by virtue of available information and historical data. But there is more, as having valuable data generates better operational effectiveness, with the streamlining of operations and identification of inefficiencies in the supply chain leading to cost savings and faster response to market transformation and customer needs.
Related to the latter point, then, is the opportunity to personalize offerings and optimize the customer experience, aspects that are easily improved when one knows the customer’s preferences, habits and weaknesses. Another predominant aspect concerns the scalability of the system, as appropriate big data management methods are designed to be scalable, enabling organizations to handle increasing volumes of data without hassle.
Other important benefits take root on this basis, starting with the ability to drive innovation, as the availability of detailed information enables the design and development of new products and services tailored to customer preferences. Looking inward, on the other hand, skillful big data management and the resulting predictions help reduce business risks, not to mention an additional key aspect that relates to data security.
Effective data management practices facilitate cybersecurity activities, making IT infrastructures more secure in the event of attacks by cyber criminals, limiting vulnerabilities that foster potential breaches that are harmful both financially and to the company’s image.
The challenges of big data management
Big data management presents a number of challenges related to the inherent complexity of managing large, diverse, and constantly changing volumes of data.
Let’s look at them in more detail to delve into each:
The quality of the data
Having to deal with a huge amount of data, coming from different sources, implies the use of useful processes to clean up information from inconsistencies, duplications, redundancies and anomalies.
A necessary step to avoid errors in subsequent analyses, on which decisions to be made for the future of the business depend, and to ensure homogeneity and consistency to the extracted data set.
In a word, then, the challenge is to ensure the quality of the data, on the basis of which the success of the business is built.
A necessary step to avoid errors in subsequent analyses, on which decisions to be made for the future of the business depend, and to ensure homogeneity and consistency to the extracted data set.
In a word, then, the challenge is to ensure the quality of the data, on the basis of which the success of the business is built.
Volume, speed and variety of data
One of the major challenges of big data management concerns the volume, velocity and variety of data.
From sensors to social media feeds and transactional data, organizations are inundated with incoming data from different sources.
Therefore, managing and storing the flow of information reaching the enterprise in real time and in different formats (structured and unstructured data) requires scalable solutions and powerful processing capabilities.
Not least because complicating an already difficult task is the speed at which new information arises, a scenario that forces companies to resort to advanced processing systems.
From sensors to social media feeds and transactional data, organizations are inundated with incoming data from different sources.
Therefore, managing and storing the flow of information reaching the enterprise in real time and in different formats (structured and unstructured data) requires scalable solutions and powerful processing capabilities.
Not least because complicating an already difficult task is the speed at which new information arises, a scenario that forces companies to resort to advanced processing systems.
Information security and privacy
In addition to quality, it is crucial for any business to ensure data security and privacy.
Different but equally important are the elements to be considered here, because on the one hand data breaches are increasingly frequent, and on the other hand regulations such as the GDPR oblige companies to maintain certain approaches to protect data.
Safeguarding their assets from cybercriminal offensives and ensuring compliance with current regulations are, therefore, two sides of the same coin of one of the most relevant challenges at the enterprise level.
Different but equally important are the elements to be considered here, because on the one hand data breaches are increasingly frequent, and on the other hand regulations such as the GDPR oblige companies to maintain certain approaches to protect data.
Safeguarding their assets from cybercriminal offensives and ensuring compliance with current regulations are, therefore, two sides of the same coin of one of the most relevant challenges at the enterprise level.
Data management tools
Data management tools cover techniques for collecting information, solutions for storing data, and most importantly, software for processing data, a crucial step in obtaining the key information from which decisions can be made to increase performance and optimize business success. Beyond the Database Management System (DBMS), the system with which to create the structure to manage, organize and store large amounts of data, the preeminent issue concerns the choice of software to process the information.
Among the best tools capable of processing large data sets are Apache Park, Talend, Xplenty, Lumify and Cloudera.
Among the best tools capable of processing large data sets are Apache Park, Talend, Xplenty, Lumify and Cloudera.
