Transforming Business Models With Big Data Analytics: An Industry Guide
Businesses that embrace the full spectrum of technological opportunities will not only achieve a competitive edge but also transform their existing business models and industries by enabling growth in innovative ways. Data is transforming business principles. With the help of advanced analytics and data sources, enterprises are optimizing their service offerings and also extending their customer service. This blurs the boundaries between businesses and customers. Companies that use big data analytics with the dynamic business shifts discover new opportunities for revenue streams, customer service, product portfolio and service verticals. It is astonishing to witness how swiftly the new form of predictive analysis has taken over the way companies do business.
Industry leaders and other leading stakeholders certainly accept that big data has become a big game changer. If not all, but most of the industries are using this revolutionary technology to discover industry insights, customer trends, and personalized marketing practices. As big data continues to optimize business processes, we have seen a tremendous shift of focus from the hype girdling it to discovering real value in its application. Enterprises are accelerating business growth with high Volume, Velocity, and Variety of data.
Big Data: Unlocking Opportunities
Big Data is still a riddle for many organizations. Most of them have heard about it, and to some extent using it, but unaware of its complete execution or immense potential. The general understanding of Big Data is a large collection of data stored in the cloud in order to use it for future business decisions & accessing business insights. This general understanding is not unreliable. But in a comprehensive sense, Big Data is the growth in the volume of structured and unstructured data, the velocity at which it is created and collected, and the scope of how many data points are included.
Big data comes from various sources and appears in multiple formats. Data mostly arrives unstructured. We need to process algorithms in order to analyze and organize it into useful information. It takes a substantial amount of processing power to organize data. So, the better the processing power, the better the quality of analytical information. OLAP (Online Analytical Processing) cubes are being used to observe and analyze multiple dimensions of data to accumulate insights which will help us define a robust business strategy. As data grows in volume and velocity, managing these cubes need a solution. Technologies like Cloud Computing, AI, and IoT are playing a major role in allowing Big Data to evolve as one of the most robust technology today. It is rational to predict that the more technology improves, the quality of Big Data’s analytical information will enhance.
The Emergence of Data Science
You have a huge amount of data. Now What? How to unlock real value from these data? Here comes data science. Data science is a combination of data inference, algorithm, statistics and technology to resolve analytically complex puzzles. Data science is the mechanism behind uncovering data to understand complex customer behaviors, trends, and insights to make smarter business decisions. Search engines (Google, Bing, Yahoo etc.) use data science algorithms to show top search results within few seconds. Digital marketers use data science algorithms to get higher CTR for the display ads.
The Significance of Data Visualization
When it comes to traditional business intelligence, data visualization is very crucial. With data visualization, a user can avoid the hassle related to looking at long reports and can have a glance at graphs or info graphics. But when we talk about big data, data visualization is a must. It is impossible for a user to understand the insights of a data which is huge in volume and keep on increasing. Visualization enables organizations to arrange complex outputs and present them in a way which is informative and can engage stakeholders. A strong understanding of data science is required for a successful data visualization. We have to consider the fact that a human brain can process two to three pieces of information at a time, and in order to ensure impactful results, we have to use advanced analytics and visual modeling to accurately predict consumer behavior and relevant business KPIs.
i) Data Visualization supports a wide range of tables, charts, graphs and offers the user the ability to see the same set of data in different ways to make the dashboard more informative.
ii) Allows you to look at the data with a geographic element on a map and bring in an entirely different aspect of the analysis process.
iii) Helps enterprises make smart decisions by penetrating the data to find insights. Enterprises can see patterns, trends, correlations, and reports to identify cost-cutting areas, operational glitches, and business growth areas.
iv) Data visualization’s storytelling feature can show that data is not about facts and figures but it is a way people see the world around them.
v) This allows users to organize and present massive data intuitively. Data is meaningless unless you arrange & present it properly.
There is a gamut of specialized data visualization tools available both in the form of open source and custom developed. But, there are few which are widely popular as they can offer all the features.
Data Analytics: Why it Matters?
Driven by various specialized analytics systems, data analytics can point the way to multiple business gains, which includes new revenue streams, efficient marketing practices, bigger customer engagement, advanced operational efficiency and competitive advantages. Data analytics enable data scientists to manage and analyze the ever-growing volume of data and the other forms of data which are left unexplored by conventional BI tools.
In other sense, data analytics helps organizations get the true meaning out of their business data and make informed business decisions. Data analytics involves complex applications with other elements such as predictive models, statistical algorithms and what-if analysis, driven by high-performance analytical systems.
Importance
Big data analytics helps enterprises embrace their data and use it to discover new opportunities and extract information from your data to enhance products, more efficient operations, higher profits and satisfied customers. Big data analytics found value in following ways;
Who’s using it?
Businesses rely on quick and agile decisions to stay competitive in today’s world. Big data analytics is involved in making businesses transform their operational processes. Here are few different types of organizations use the technology:
i) Telecommunications
Telecommunication service provider uses data analytics to analyze and process thousands of documents, images, manuals, customer data and reports in minutes to help their customer service team resolve customer queries more efficiently. They can save a significant amount of time & money when it comes to resolving customer issues over the call.
ii) Retail
Customer service has changed dramatically in past few years as shoppers are expecting retailers to understand what exactly they need and when they need. Retailers are using big data analytics to meet customer demands. With an ocean of customer data from loyalty programs, e-commerce sites, buying habits and all, retailers use predictive analytics to predict trends, future demands and business strategies to boost profitability.
iii) Healthcare
Healthcare industry is one of the most used analytics industry. Healthcare service providers are using data analytics to extract key insights from unstructured patient medical history. The patterns and other information captured from the data help doctors identify patients for chronic disease & other diseases, to offer timely treatment & reduce risk.
iv) Manufacturing
Manufacturing companies use data analytics to fine-tune production in order to avoid inventory overruns and balance demand-supply ratio. With the use of predictive analytics, manufacturers are creating personalized products to match customer expectation and lower inventory and warehouse expenses.
v) BFSI
Banking & Insurance industry has multiple challenges in the form of securities fraud warning, tick analytics, card fraud detection, audit trail archival, credit risk reporting, trade visibility etc. With the use of data analytics, banks and other financial institutions are monitoring financial market effectively. Trade analytics is being used by many top banks & financial institutes for high-frequency trading.
There are plenty of other industries like communications, energy, technology, government, fashion, entertainment are using data analytics in order to get the best out of their business & customer data.
How it works & key technologies
Enterprises who are using data analytics agree to the point that there is no single technology that embraces big data analytics. Indeed there is advanced analytics which can be applied to big data, but it is a combination of several technologies which work together to help organizations gain the best value from their data. The sole goal is to develop a big data environment which is highly integrated, scalable, less complex and low in cost. Data & analytics are the hubs from where the power of big data analytics is accessible to the entire organization. With the underlying infrastructure, data streams and user toolsets needed to uncover valuable insights, make better decisions and resolve actual business predicaments. That’s ideally how big data should work.
Big Data Empowering Artificial Intelligence
AI is among the most robust technological forces of current time. While debates are still passing on whether AI will transform the world in a good or evil way, no one can deny the fact that AI would be nothing without big data & it will continue to mature with the exploration of data.
With the availability big data, we can insert data into a machine-learning algorithm which then learn and understand how to replicate the behavior. Data collection is extremely quick, which means the progress on the AI front is steadily speeding up. There are companies who are making waves with their AI innovations. This is only possible because of the availability of massive amounts of data. Data analysts strongly believe that AI & machine learning will disrupt the business world. But, as AI is dependent on data, big data is essential to AI’s growth. AI learns through trial & error, which makes the availability of a large amount of data even more important. The more data AI has, the more accurate it becomes. So, in order to achieve best results in AI and machine learning, the key is to analyze the data. Big data empowers AI in various ways & is a critical factor in AI implementation. Below are few ways in which big data is enabling AI to become one of the most disruptive technology.
· An exponential increase in computational power.
· Machine learning from actual data sets, not just sample data.
· Voice and image processing algorithms.
· Open-source programming languages and platforms.
· Availability of low-cost and highly reliable large-scale memory devices.
Final Thoughts
Businesses understand that there is a lot of value which can be extracted from the huge amount of data they generate every day. But, most of the companies are not being able to obtain the full opportunity that awaits them. Though companies are analyzing data to get insights, they are yet to understand how data analytics can transform their business and change the way they work. Most of the companies either lack technical expertise or don’t have enough budget for the investment. But, the emergence of data analytics service providers has made advanced analytics service affordable & accessible. BangBit helps enterprises deploy big data analytics for a seamless digital transformation process without disturbing the bottom-line. Contact us today to learn more.
