As seen by the volume, value and the variety are the characteristics of big data, and it is produced with the rate of over 2.8 zettabytes each year. Big data is now becoming popular for the 21st century as it is consumed and utilized by the businesses in large quantity.
Every day, 2 million blogs are posted, 172 million users visit Facebook, 51 million minutes of video is uploaded, and 250 million digital photos are shared. We continue to generate 294 billion emails each day, even though many consider email an outdated form of communication. Investment is being increasing day by day just to have improvement in productivity and for generating exciting and huge analytics solutions.
You can generally split big data into two different types, structured and unstructured. The 294 billion emails being sent per day can be considered structured text and one of the simplest forms of big data. Financial transactions including movie ticket sales, gasoline sales, restaurant sales, etc., are generally structured and make up a small fraction of the data running around the global networks today. Other forms of structured data include clickstream activity, log data, and network security alerts. Unstructured data is a primary source of growth in big data as well. Old television shows and movies are other sources of variation in the nonstructured realm. There are over 864,000 hours of video uploaded to YouTube each day.
Beyond technology in general, big data is going to require changes in most business processes to ensure decisions with proper analytic judgments are made. In order for them to recognize these requirements, two main ideas will need to be focused on more closely. First, exploration of how businesses can leverage current technological solutions to both segment and then dissect the data is required and second, the presentation and then prediction of the ways in which businesses have and will use the data to form strategies to create, maintain, and then enhance their different revenue streams will need to occur.
Businesses have been segmenting customer markets for decades, but the era of big data is making segmentation more essential and even more sophisticated. The challenge is not just to gather the information rather it is a race to understand customers more intimately. Segmentation is a foundational element of understanding customers. In its simplest form, customers are grouped based on similar characteristics. As the data improve the approaches to segmentation become more sophisticated. Right now, enterprises are practically drowning in all the data being collected and if they are not careful, they can spend all their time staring at it and not putting it to good use to make better business decisions.
Businesses from all sectors recognize that knowing your customer well leads to improved and personalized service for the buyer and this results in a more loyal customer. In the effort to know their customers better, businesses have traditionally employed advanced analytics systems such as Google Analytics to segment their customers into groups based on demographics, geography, and more.
A better approach is to classify by the customer's choices, preferences and tastes based on all his interactions with the business. A rich set of additional information about customers can be found in customer interaction like emails, call transcripts, chat, SMS, social media and more. Businesses should have the ability to understand the meaning in customer dialog, and can do so automatically through newer types of analytics systems.
Big data has the potential to fundamentally change how marketers relate to their customers all of them, not just the small percentage that actively participates in a loyalty program. Business can leverage the vast amounts of information available in their customer interactions and online marketing paths (such as social media, blogs, and websites) to finely segment, maintain, and grow relationships with their customers.
It is commonly known that big data is both a critical challenge and an opportunity for businesses. Having technologies designed to address the explosive growth of the volume, variety, and velocity of information is critical for their success. In the end, the big story behind big data may be very small the capability to create and serve very small micro-segments of customers with significantly higher accuracy and achieving more with less. Segmenting is the mere tip of the big data iceberg and the strategies that companies have already formed and will continue to form in order to leverage it is incredible.
There are currently four main strategies companies use to leverage big data to their advantage: performance management, decision science, social analytics, and data exploration. Performance management is where all things start. By understanding the meaning of big data in company databases using predetermined queries, managers can ask questions such as where the most profitable market segments are. It can be extremely complex and require a lot of resources however, things are beginning to get easier. Most business intelligence tools today provide a dashboard capability.
Data exploration is the second strategy that is currently in play by businesses. This strategy makes heavy use of statistics to experiment and get answers to questions that managers might not have thought of previously. This approach leverages predictive modeling techniques to predict user behavior based on their previous transactions and preferences. Cluster analysis can be used to segment customers into groups based on similar attributes that may not have been originally planned. Another popular use case is to predict what group of users may drop out. Armed with this information, managers can proactively devise strategies to retain this user segment and lower the churn rate.
The next strategy companies use is leveraging social media sites such as Facebook, Twitter, Yelp, or Instagram. Social analytics measure the vast amount of nontransactional data that exists today. Much of this data exists on social media platforms, such as conversations and reviews on Facebook, Twitter, and Yelp.
Social analytics measure three broad categories: awareness, engagement, and word-of-mouth or reach. Awareness looks at the exposure or mentions of social content and often involves metrics such as the number of video views and the number of followers or community members. Engagement measures the level of activity and interaction among platform members, such as the frequency of user-generated content. Finally, reach measures the extent to which content is disseminated to other users across social platforms. Reach can be measured with variables such as the number of retweets on Twitter and shared likes on Facebook.
Social analyzers need a clear understanding of what they are measuring. For example, a viral video that has been viewed 10 million times is a good indicator of high awareness, but it is not necessarily a good measure of engagement and interaction. Furthermore, social metrics consist of intermediate, nonfinancial measures. To determine a business impact, analysts often need to collect web traffic and business metrics, in addition to social metrics, and then look for correlations. In the case of viral videos, analysts need to determine if, after viewing the YouTube videos, there is traffic to the company web site followed by eventual product purchases.
Many of the techniques used by decision scientists involve listening tools that perform text and sentiment analysis. By leveraging these tools, companies can measure specific topics of interest around its products, as well as who is saying what about these topics. For example, before a new product is launched, marketers can measure how consumers feel about price, the impact that demographics may have on sentiment, and how price sentiment changes over time. Managers can then adjust prices based on these tests.
The future of strategies is hard to predict, however, based on how things are growing, companies are betting that it will be in new types of technology leveraged within analytics systems with a focus on big data. As a founder of a company that focuses on web and data analytics, we are betting the future is in big data processing. By creating an analytics platform accessible online, with an emphasis on beautiful design and a simple interface that is easily used, we are combining powerful analytics with beautiful results. By leveraging all four current strategies and adding our own technology to the mix, the results should push the boundaries between nonfiction and science fiction.
Big Data is changing the way we live our lives, from running businesses to shopping at the grocery to buying movie tickets. Every piece of collected information is being segmented and used to analyze the way consumers think and behave. In order to take advantage of this opportunity, we need to move away from outdated, less innovative solutions. Instead, we can leverage up and coming technology being offered by new startups that change the way we can identify trends in data and insights into consumers thought processes. By knowing the current strategies that businesses use to take advantage of this massive amount of data, we can use that information to make better-informed predictions about where this phenomenon is taking us.