It is an era governed by data and there has been no other time in history where economies and the society have become so dependent on data.
Hence it is imperative to say that technologies have transformed and evolved to accommodate more and more data that can be aptly used as a major resource for many planning and decision-making processes.
With the growing volume of data, it has become extremely difficult and impossible to collect and use this data that is way beyond human capabilities. Hence the birth of a stream of science called Big Data Management.
What is Big Data Management and Analytics? To put it straight it focuses completely on organizing, administration, and governance of high volumes of both structured as well as unstructured data. Here we are talking about terabytes and petabytes of information that are collected in various file formats. It is thus the task of effective data management to help extract relevant information from these huge sets of data that are accumulated from numerous sources.
The fundamental purpose of big data management is to ensure a high level of data quality extracted from raw data that can be made accessible for business intelligence and analytics.
According to the latest research statistics it is been estimated that 2.5 quintillion bytes of data are generated worldwide every day and Data-driven organizations are 23 times more likely to acquire customers than their peers. Businesses today are spending $187 billion on big data and analytics according to the estimates in the year 2019.
We considered it to be important in providing some of the major trends in Big Data Management that are evolving in itself according to the decision making and analysis of many organizations.
Data Analysis Automation:
It is estimated that by the year 2025 almost 30 percent of all generated data will be real time. One of the main reasons for automation is that earlier data analytics has been performed by business analysts who pass on their interpretations, findings, and insights to business leaders, marketers. But the process over all these years has been very slow particular because business analytics derive their findings based on the history of cases. This in no manner can be applied to present scenarios as there is a constant transformation in business operations. Hence data analytics has started to become a directly built component into the business processes that can create insights based on real-time data.
Augmented Analytics:
To simply put forth augmented analytics can be defined as the use of technologies such as artificial intelligence, machine learning and natural language processing that will help in assisting data preparation, insight generation and result in explanations. Developers of data analytics are going to largely benefit from this as it will help generate valuable information with minimum coding and deployments and that too in real-time. Many aspects of the data science can be automated which includes development, management, and deployment as well.
Edge Computing:
As the number of connected devices grows that will help in collecting real-time data from the field it also creates huge data management analysis challenges as well. It is going to be a humungous task to collect such huge volumes of data from edge devices and after that managing as well as analyzing the same.
It is estimated that almost 150 billion devices will be connected all over the globe and more than 90 zettabytes of data will be collected by the year 2025. One of the best was to tackle such exponentially growing data is Edge computing. Data can be processed and analyzed in real-time by distributed systems also with the edge devices themselves. The result of such a process can be directly sent to central IT data centers.
Data As A Service:
Data is the king today as businesses and organizations are solely relying on data and digital files for insights that will help them do better forecastings. As the value of data keeps increasing today data is provided by many companies as a service basis both for internal usage as well as commercial. With cloud computing being used so widely and facilitated by many providers data is all locked up in data stores that can be easily shared and made available anywhere at any time.
Just like software-as-a-service organizations are increasing providing Data-as-a-Service by using cloud technology. This will make the lives of analysts much easier as the can access real-time data for better business analysis and forecasting tasks.
Data Management Regulation:
With the increase in the collection and use of data from various sources in many cases that involve profiling of citizens, many organizations have started misusing data. This has forced many governments to step up and introduce data management regulations that are set to take effect this year. Also with growing calls on stricter and comprehensive regulations, businesses and organizations can expect rigorous data governance, security and privacy practices. Such rules and practices are bound to impact how businesses and organizations collect, process, handle and use data, especially as it relates to consumer data.
Final Thoughts:
Big data management is the in thing today with many conventional approaches included coupled with newer technologies. Today Big Data management strategy has to include tools that will enable better data discovery, data preparations along with data cleaning and thereby generating valuable information for businesses.
