Big Data Analytics: What It is and Why is So Important?

The current world is powered entirely by big data. These data are generating from every step of an individual’s online activity, be it browsing the internet or the use of advanced connected devices, particularly IoT devices. This surge in data has given the rise of big data analytics. It is the complex process of analyzing big data to discover information such as concealed patterns, market trends, customer preferences, and others. This will substantially help organizations to make informed data-driven business decisions. As big data analytics enables businesses to harness their data and use it to identify new opportunities, it leads to smarter business moves with efficient operations and higher ROI.

Today, people as well as businesses generate data from millions of sources at an unprecedented rate. These data sources are present everywhere in the world; social channels are the largest sources of data available out there. Thus, harnessing the power of truly big data analytics tools and techniques at a broader scale, they provide a means to examine data sets and derive actionable insights.

Since big data is not a new concept for businesses, enterprises are leveraging different types of data analytics tools to excerpt meaningful information from their data.

Here are some most relevant types of big data analytics.

Prescriptive Analytics: This data analytics concept prescribes what action to take to remove future problems or capitalize on a promising trend. Prescriptive analytics essentially provides an organization with a laser-like focus to answer a specific question. It also helps them to determine the best solution for a future opportunity or avoid future risks.

Predictive analytics: It uses big data to identify past patterns to predict the future. Predictive analytics draws its power from numerous methods and technologies, such as big data, data mining, statistical modeling, machine learning and assorted mathematical processes, among others. By utilizing this model, an organization can use past and current data to reliably forecast trends and behaviors.

Descriptive analytics: This data analytics method provides insight into what has happened historically and will provide businesses with trends to get in-depth detail. Descriptive analytics defines as a preliminary stage of data processing that creates a summary of historical data to yield meaningful information and possibly prepare the data for further analysis.

Diagnostic Analytics: With this analytics technique, historical data can be measured against other data to answer the question of why something happened. Essentially, data scientists turn to this technique when trying to determine “Why” behind something happened. Diagnostic analytics can be beneficial in the sales cycle, for instance, to categorize customers by their likely product preferences and sales cycle.

Adopting big data and analytics technologies provide considerable benefits to organizations across industries, including healthcare, education, governance, retail, manufacturing, BFSI, and supply chain management & logistics, to name a few. Companies of all sizes and types utilize big data analytics for their business growth.

Here’s a look at the most promising applications of big data analytics.

Customer Experience: As the world is increasingly becoming digitized, access to customer information, such as buying behaviors, preferences, and dislikes is inevitable to deliver an enhanced customer experience. This can be achieved effectively by the better use of big data analytics tools. They collate and interpret vast volumes of data to extract meaningful, insightful, and useful data that provides value to a customer.

Cost optimization: Cost optimization, a business-oriented process, typically blends digital, IT and business that aids a company to accomplish business goals on time. Using data analytics for cost optimization enables consistent data management that helps lower last-minute complexities and the costs involved. It also cuts testing cycles which can optimize time and money invested.

Operational Efficiency: It generally means maximizing an organization’s desired business output whatever that may be. Once the business is rotten and running, decision-makers are likely spending their time attempting to improve their companies’ operational efficiency. By integrating big data tools, they can improve operational efficiency by leaps and bounds. The tools glean large amounts of useful customer data by interacting with customers or clients and gaining valuable feedback.

Real-Time Monitoring: Big data analytics provides businesses with real-time insights, allowing them to calculate and measure the impact of price changes; implement competitive positioning for maximizing profits; assess finances to get a clearer idea of the financial position of your business; execute pricing strategies based on local customer demands, customer purchasing behavior, and competitive market patterns; and automate the pricing processes to maintain price consistency and eliminate manual errors.

Undeniably, data without analytics is of no use. Data can bolster profitability if it is analyzed optimally. Gartner predicts that the amount of data that is worthy of being analyzed will surprisingly be doubled by 2020. It is also predicted that global revenue for big data and analytics will grow from US$130.1 billion in 2016 to US$203 billion by 2020, at a staggering CAGR of 11.7%.