In this current era of information and advanced analytics, big data is no longer new to businesses and society. While big data is not a new concept, it has become a common way for organizations to generate insights, make data-driven decisions, recognize market trends and improve productivity. It’s so common that several data management tools and techniques exist for small and large firms.
Impressively, big data offers so much more than an avenue for making effective corporate decisions. It also provides several opportunities for organizations to make a beneficial impact on other businesses, the workforce and society. Therefore, every organization must understand the intricacies of big data and how it can affect productivity.
What is big data?
Big data is a complex collection of structured, semi-structured, and unstructured data collected by organizations. This data is mined and analyzed to obtain hidden information and gain insights for several projects, predictive modeling and other advanced analytical applications.
Big data often describes the process of obtaining, cleaning and analyzing massive chunks of data, enabling businesses to derive information such as market trends, patterns and customer preferences. With this information, organizations are better equipped to make informed business decisions.
Different organizations generate data constantly and seek novel ways to optimize, manage, analyze and store this data. Big data’s potential to help organizations maintain a competitive edge has made it one of the biggest markets globally. To put things into perspective, the global big data market is predicted to be worth about $103bn and to become the most significant market field by 2027, accounting for 45% of the market share.
Big data’s impact on business and society
Big data has proven extremely useful in recognizing the latest information necessary for making more intelligent and quicker decisions. Employing data analytics also allows businesses to improve how efficiently they operate, obtain a competitive edge and identify more productivity opportunities.
Additionally, organizations look to increase their revenue by improving customer service with the insight generated from big data. Most companies employ big data to explore new ways to improve customer satisfaction. However, businesses can achieve other goals through big data, including a more effective marketing strategy, cost-effectiveness and improved efficiency.
Big data has provided businesses with several social and economic advantages, most of which also impact society. Its effect on society is so apparent that organizations must conform to government policies established to foster extensive data development. These developments help to streamline several aspects of society, including logistics, traffic management, healthcare, fraud detection, urban and community planning and more.
The 5 V’s of big data
Established companies owe much of their success to big data and the vast number of data points that can be mined for helpful information. However, while big data offers many benefits to businesses, it is only the starting point, not the end.
Maximizing the full potential of data requires organizations to recognize big data’s challenges and innate characteristics, which are represented by the five V’s – volume, velocity, variety, veracity and value.
Understanding these characteristics allows organizations to obtain more value and customer-centered insights from data. In truth, these are vital in making big data a valuable business strategy, so vital that organizations hire professionals who understand the intricacies of data and analytics. These skills and knowledge can be developed with the help of an online MBA in data analytics, such as the one offered by Walsh University.
With the help of business intelligence (BI) solutions, understanding the challenges and these big data characteristics can be invaluable in answering questions that were previously beyond reach. Here is a breakdown of the 5 V’s of big data:
Volume refers to the size of big data, which has recently increased rapidly due to innovations such as cloud computing, IoT and smartphones, to mention a few. As hard as it may seem, relevant companies in the late 1990s and early 2000s handled terabytes of data. Today, the volume of data has increased massively to petabytes and even exabytes.
But just where does this data come from?
There are several data points through which we can generate data. In today’s technologically advanced world, data is obtained from transaction processing systems, mobile applications, databases, emails, social networks, website-capturing systems and monitoring devices. For instance, Amazon has 1.4 million servers and generates about 2.5 quintillion bytes of data daily.
Handling these large volumes of data is an entirely different proposition. Depending on the amount of data, various options exist, from data warehouses to data lakes and management systems. There is also the option of storing data on private cloud systems or service providers such as Google Cloud. However, with the global data predicted to surpass 180 zettabytes daily by 2025, these options may need to be revised soon.
As the name implies, velocity refers to the speed at which big data grows. It also incorporates the rate at which data is accumulated. Previously, organizations employed batch processes to measure real-time data from different sources and generate reports. Today, batch processes cannot control the constant flow of real-time data from an ever-growing number of sources.
For instance, Google’s batch process measured their daily engagement as 32.8 million searches in 2000. Fast forward to 2018, and the site receives over 5.6 billion daily searches. Other online platforms have also witnessed an upsurge in monthly active users, including Facebook (2.96 billion), YouTube (2.291 billion), WhatsApp (2 billion) and Instagram (1.2 billion), among other platforms.
Aside from technological advancements, a significant factor in the high velocity of big data is the short lifetime of data. The data’s usefulness might expire if insights are not generated within a short period. For instance, a company looking to identify its top seasonal sales must analyze the data prior to the start of the season. Additionally, the best organizations employ Business Intelligence solutions in analyzing data and making timely decisions.
Variety refers to the different forms data takes because it stems from various sources. As varied as digital data is, there are different ways to mine and process each data for insight. There are three varieties of data – structured, unstructured and semi-structured.
Structured data refers to traditional data organized to follow a formal structure. The standard form of data refers to collecting and storing data in rows and columns, making it easier to access and modify efficiently. At some point, information was mainly extracted from structured data stored in internal databases such as RDBMS and Excel spreadsheets. However, different organizations now utilize less structured data for extracting insights.
Semi-structured data is partly organized but does not conform to the formal data structure. Some examples include log files, JSON files and CSV files. On the other hand, unstructured data is entirely unorganized and does not fit into relational databases‘ row and column structure. Unstructured data includes text files, images, social media comments, videos and audio files.
Obtaining, storing and processing unstructured data is complex; so organizations analyze it in real time. Some powerful self-service BI tools, such as Tableau and Power BI, make the real-time analysis of unstructured data much more straightforward.
Across different studies, veracity has been highlighted as an essential characteristic as it sets the foundation for a successful business. Veracity refers to the assurance of data quality, integrity, accuracy and credibility. An analysis is only as good as the data, and the efficacy of an organization’s decision depends on the data’s accuracy.
Data is only helpful to an organization if it’s clean, accurate, error-free, consistent, complete and free from bias. Organizations should check data for inaccuracies to ensure data meets all these requirements. Some common data contaminating factors that should be checked for include:
- Inaccurate statistical data that provides false information or market representation.
- Insignificant information that misrepresents the data.
- Irregularities in the dataset that deviates from normal behavior.
- Software vulnerabilities that could result in hacks and data breaches.
- Incorrect insights due to human errors in obtaining, processing or analyzing data.
Data is generated and obtained from different sources. So, it is necessary to always check the data for accuracy before using it for business decisions. Clean data offers several benefits to organizations, from more effective choices to improved productivity and help with privacy issues.
Data volume, velocity, variety and veracity ultimately mean nothing if valuable insights are not extracted. The value of data refers to its usefulness in decision-making. Organizations can only convert data to useful information through proper analytical processes. With valuable data, organizations can:
- Create a more transparent operation.
- Make better decisions.
- Provide personalized products and services to segmented customers.
- Mitigate operational risks.
- Discover hidden opportunities and insights.
Data in itself has no use until it’s converted into something valuable. So, value is another fundamental characteristic of data. While different organizations can employ similar data, the means they choose to derive value from the data should be unique to each organization.
It is a complex path to be successful in a dynamic industry, yet successful businesses discover new ways to employ technology to streamline most processes. Today, savvy companies use comprehensive data management solutions to maximize all these data characteristics. With these tools, companies can quickly obtain, store, clean and process massive volumes of data in real time.
Businesses are different, and each must adopt different approaches towards big data. However, when employed correctly, companies can take advantage of the endless possibilities of big data and become a powerhouse in their industry.