Every time someone opens an email, uses a mobile application or visits the web, interacts with a customer service representative or speaks to a virtual assistant, technology collects data about them. Organizations rely on big data to identify trends, provide insight and drive decision-making.
To leverage big data, organizations need to ensure the data’s quality and find ways to efficiently store it. This is where four major trends in big data come into play.
Every day, your customers generate a wealth of data when they use your mobile apps, tag you on social media, make an online purchase or speak to a customer service representative. Likewise, every employee, supply chain and marketing effort also creates data that needs to be stored, managed and leveraged for business success.
This volume of data has become so large and fast-moving that traditional systems cannot store and process it in real time. Often, this information is unstructured or semi-structured and includes text, images, video and sensor data.
Businesses are using big data to solve a wide range of problems. For example, a financial firm might utilize big data to enhance cybersecurity and personalize financial decisions for consumers. A healthcare company might analyze big data to improve patient outcomes and accelerate research on diseases like cancer and Alzheimer’s. Meanwhile, a manufacturing company might use big data to predict mechanical failures by correlating sales, return rates, online reviews and customer service calls.
Big data arrives at a rapid pace. Some companies can produce terabytes of data in a day. The key to dealing with this velocity is being able to process it in real time. This is the heart of big data analytics.
It’s also important to note that this velocity is not limited to storage. It includes how quickly data can be gathered, processed and analyzed. This is especially crucial for businesses that need to act on data urgently, such as medical professionals identifying drug interactions or energy companies analyzing stock market trends.
It is also critical for businesses to be able to store and manage the large volumes of data they generate. This involves determining where data should reside and cataloging it so that other systems know it exists. Then it can be analyzed for insights that help drive business decisions. For example, a retail company can use big data to make recommendations on products to customers based on their browsing and buying habits.
Unlike structured data that neatly fits into a relational database, big data often comes in a variety of forms. It may be unorganized and require additional preprocessing to make it usable, or it could be raw, such as a stream of sensor data or the content of a web page. It might also be semi-structured, like emails or text messages, or completely unstructured, such as MP3s of rock music or hand-written notes.
As the volume and velocity of digitized information increases, businesses must develop new tools to process it. These include frameworks to organize and wrangle unstructured data, as well as methods to determine which signals represent valuable information versus noise. In addition, the veracity of this information must be examined, such as determining whether it is accurate or if it contains mistakes. Inaccurate or untrustworthy data can lead to inaccurate insights and bad decisions. It’s important for organizations to create a culture of data trust and transparency.
Most organizations are only just beginning to evaluate and understand the benefits of big data. Some, like a new breed of Internet companies that monetize their data, realize it is a critical corporate asset to be analyzed and leveraged for competitive advantage. Others have started to collect data because of a business imperative, such as gaining or losing market share.
In this evaluation and discovery phase, the company may start to reorganize its infrastructure for storing large amounts of data. It starts to see data as a valuable corporate asset and may begin to establish policies for governing how that information is used.
In this stage, the organization has developed a more cohesive architecture and infrastructure that is capable of managing data analytics at a greater scale. The organization is able to identify key areas of the business, define a data strategy, develop an understanding of the scope of big data analytics and implement a data governance process that ensures that the analysis is focused on the most value derived from the data.