What is Big Data? This question is likely to come up in the mind of any company that has an IT department and also the budget for expensive data analysis tools. Big data is basically a field which deals specifically with methods to analysis, extract data from, or in a manner to deal with large and/or complex data sets. The popularity of such tools is growing because data is used to make decisions about marketing strategies, customer service, product research, and many other facets of business.
Data Mining is the process of finding trends from large consolidated databases, then analyzing it using new technologies like Artificial Intelligence (AI), Natural Language Processing (NLP), Computer Graphics (CGI), and Data Analytics to create new products, services, and insights from re-structured or aggregated data. With a good Big Data Analytics tool, one can analyze and extract quantitative data and images at a much faster speed than what would be possible using traditional tools for data mining like database management systems (DBMS), data mining tools, and other programs for analytical computing. However, even though this might sound promising, it is important to understand that the present hype around big data tools is not based on hard facts or technology, but is actually a marketing stunt by companies to sell their software.
The companies selling these tools for data mining are actually not providing true analytics solutions. These companies are providing what is termed as “stream processing”. What is meant by stream processing is the ability to easily analyze massive amounts of unprocessed data and then provide insights from the analyzed data.
For example, consider how various organizations use sophisticated analytic tools to monitor the social media engagement of their customers. These social media monitoring tools collect data on the activities and conversations of their users, and then use it to generate insights about target markets, advertising campaigns, customer demographics, and product brand loyalty. While this may sound like a sophisticated form of analytics, it is actually just another way to aggregate large volumes of data and focus on uncovering the micro interactions between consumers and businesses on the Internet. Big data analytics provides insight that traditional techniques miss.
While large amounts of unstructured data may seem daunting, unstructured data analytics presents opportunities to discover new trends and insights at a low cost. In addition, big data analytics offers the potential to enable better optimization for search engine crawlers and for web directories. Traditional data mining methods take too many samples and too much time. Traditional approaches also require the knowledge and expertise of a group of technical experts in order to produce good quality insights. At the same time, traditional approaches have major limitations with respect to representing the complex structure of the Internet and the enormous amount of data flowing through it. This means that optimization results may be invalidated by changes in the way that the Internet works with respect to the sample.
Big data analytics is typically implemented in conjunction with traditional approaches. The two work together so that marketers can understand and exploit the relationships among consumer behaviors, interests, purchasing habits, and other common characteristics. The primary advantage of using traditional methods alongside big data analytics is that these models and techniques are well suited to analyzing a range of discrete, aggregated, as well as combination of sources of data. Data from social media, for example, can provide an impressive amount of detail about individual customers, while using a diverse range of techniques that combine traditional and social media analytics together.
The key problem with traditional analytics is that they lack the ability to deal with the rapidly changing characteristics of the Internet. When traditional techniques are used alongside online data sources, marketers can easily miss the marked improvements that their efforts have made. As an example, changes in Internet search engine algorithms over time can have a marked effect on how search terms and queries are displayed to Internet users, while changes in social media sites can impact consumers’ content preferences. Despite the marked differences in domain and technical expertise among online marketers, they all face the same fundamental challenges: how to understand, analyze, and act upon emerging market trends. The inability of marketers to evolve and adapt has made the field more competitive than ever, and it is becoming increasingly difficult for professionals to stay ahead of competitors.
Marketers need to develop new perspectives in order to continue to succeed in a changing environment. Big data analytics brings this ability to marketers in a format that allows it to directly address these needs. With this unique perspective, organizations gain a unique opportunity to evolve its business models to better align with its target audiences. In this way, these companies can continue to take advantage of insights from the most relevant sources while making relatively minor modifications to its existing processes. This flexibility makes big analytics more than worth its investment.