What is Big Data: Understanding Large Amounts of Data

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Data is streaming from all aspects of our lives in unprecedented amounts; never before in the history of humanity has there been so much information being collected, studied and used daily. In this article, we discuss 1)  what is Big Data and what it does? 2) everything you need to know about big data, 3) industry uses of large amount of data, 4) challenges associated with large amount of data, 5) big data analytics versus warehousing, 6) consumers and large volumes of information, and 7) how to capitalize on Big Data.


If there ever was a revolution in business, the large amount of information streaming in from our phones, computers, parking meters, buses, trains, and planes is truly it. Not only are businesses collecting large amounts of data, but they are also using this data to improve customers’ experiences and their business decisions and processes. If unconvinced about the power of this changing and expanding mass data, one needs only to peruse the changing capacity of computers and the speed at which such changes occur, there is an upgrade every eighteen months. Though used in the past to analyze various aspects of public health and medicine, the information gained from the data streams is helping to make life on planet earth a lot better if not arguably simpler. So, what is Big Data and how we can use it for improving a company’s performance? As mentioned in our Complete Guide about Big Data, the term Big Data is defined as “Big Data is a term that refers to the data gathered by businesses and organizations, stored digitally.” Gathering of information helps companies to achieve higher performance by:

  • Faster problem-solving – With the improvements in computer algorithm and capacity, problems that used to take years to be solved are now being solved much faster. By being able to plug information into a computer with the capacity and set of rules needed for problem solving, a complicated and expensive problem can be rectified within minutes, thus saving the company numerous dollars and man-hours.
  • Generating new insights – There is a new wave of database links that make bridging the gap between certain disciplines easier. And so is the approach that helps incorporate visualization techniques into data collection and use. You see, before there was a strict dependence on human input for the recognition of patterns. However, the new data analytics tools that allow for the computers’ software to identify patterns is making the process faster and generating many new ideas through the knowledge received.
  • Recommendation Engines – If you have ever been on a website or social media site like Amazon or Twitter respectively, you have seen the recommendations that have been offered based on your online behavior and choices. These are not generated by human effort, but instead by recommendation engines. Further than just tracking habits, the data collected have been used to make links that are not readily visible to the human eye. According to Harvardmagazine.com, one such example is of Target’s use of data collected on women purchasing unscented lotions to determine that they were probably pregnant and for this reason targeting pregnancy marketing to them. A human cashier would have missed the connection, even if she had served these women numerous times in the last few months.
  • Improving lives – From the beginning data collection and the vast amounts of data were used to help to improve lives through medicine and health care predictions and innovations. Innovative practices such as the study of the billions of human genes to discover various disease traits and possible cures are only a few of the wonders that the correct use of this amazing amount of information can help us to perform.


Big data facts

According to a fascinating Slideshare presentation, every day we make more information than was ever created in all the days up until 2003. And that amount doubles every 1.2 years. While Google is busy processing over 3.5 billion searches every single day, companies like AT&T, holder of the world’s largest volume of data in one database, are tasked with storage and use to improve our lives and experiences.

[slideshare id=39489862&doc=bigdata-25ntkfacts-140924143333-phpapp01]

Big Data technologies

Traditional information technology infrastructures would be hard pressed to handle the large amounts of data streaming into companies daily. However, there are technologies suited to the management and storage of these voluminous amounts of information and using them to garner information, called analytics. Two major players in big data analytics are Apache Hadoop, the programming framework that runs applications on systems with many, as a matter of fact thousands of nodes (connection points) and MapReduce, a software framework that has a map function which distributes values to different nodes and a reduce function that gathers the results into a single value.

The Science of Managing big data

Digital data advances allows for data management and hence more knowledge and improved decision-making ability. Data science is the study and incorporation of business analysis, computer science, and mathematics among other subjects. It involves automated methods used to collect massive amounts of data and then taking knowledge from them to make new discoveries.

Developments in large data

In the beginning, the challenges of big data were simply how to collect and use it. Now there is more focus on how to extract useful information and gather powerful new insights. The increase in the amount of unstructured data and the need to store it all and have it processed created a problem for many businesses but also created the need for companies with their open source framework and knack for processing data on a larger scale.


Big Data application are already used in Marketing, Sales and Recruiting departments of companies. According to Mongodb.com, big data databases help to save companies money, grow their revenue and help to achieve other company objectives.

  • Construct new business applications – Through the collection of information on products, resources, and customers, companies have many real-time data values to use to optimize customer experience and package and use their resources successfully.
  • Improve existing applications – The outdated and overwhelmed infrastructures of yesteryear can be replaced by less expensive hardware because the use of open source technology allows for more data to be stored in one place.
  • Increase competitive advantage – One of the most telling traits of future business success is the ability to adapt to changes. And with the use and implementation of the knowledge gained from big data analytics, companies can adjust their practices, repurpose resources and reinvent themselves faster than their competition.
  • Increase brand loyalty – Probably the most important use of the large volume of information gathered and the knowledge extracted is the update speed. Consumers want answers and solutions, and they want them as fast as the speed of light. So in order to meet the rapidly changing demands of the customer, businesses, and all organizations must be able to update their information and respond to that which the customers requires quickly enough to keep them happy.


With the exponential growth of information being harvested, Information Technology leaders and Business Intelligence Executives face the challenges of managing amounts, the speed at which it comes in, gets processed and goes back out in the form of solutions.

  1. Volume Challenges – The amount of information that is streamed daily is tremendous and continues to be record breaking. Without the proper system to store, categorize and process these amounts of data, companies will be missing out on the insights and information provided directly or indirectly by their customers. This means a company that has the needed data processing platform in place to quickly respond to their customer queries using the resulting analytics will outrank them.
  2. Velocity Challenges – The constant change in information and needs of the customer could mean there needs to be a structured record creation process that turns data into desired information that meets needs with speed and efficiency. Businesses need to be prepared to deal with this to have the edge that savvy consumers are attracted to or lose out on business.
  3. Variety Challenges – Variety is the spice of life but can be hell for the IT leaders trying to translate the humungous amounts and types of data daily. There is tubular data from databases, hierarchical data, structured and unstructured and even transactions all needing to be analyzed, categorized and correctly utilized by the company.
  4. Management Challenges – Because we are tracking and recording basically everything that happens in the world, there could be problems of gathering and optimizing the use of the information gathered. And when one considers that the average amount of data collected by an average firm will grow by 50% in the coming year, of course it becomes apparent that there needs to be in place a very strategic data management process and initiative to lower inefficiencies. And this system has to be equipped with means of managing all varieties, volumes and speeds of data entering the company and be manned by a team of customer conscious information specialists and analysts to interpret the information that has been collected into dollars and sense.


Most people outline the distinguishing characteristics of analytics and warehousing using the three V’s. However, there are various other traits that can be looked at to understand the difference.

One such trait is the time information process, also called time to information trait. In traditional data warehouses, information is collected and stored and waits to be processed overnight, while modern day analytics perform tasks and have responses to queries in seconds.

Another defining difference is the content processed by both traditional data warehouses and analytics platforms. The former platform is geared toward the performance of structured data while analytics can process data from unstructured sources like the Internet and mobile devices.

Cost is another key component that distinguishes warehousing from analytics in that the overall cost to store and process complex data is much less now thanks to the performing qualities of open source, low cost platforms that can handle large data sets quickly and efficiently.


Business intelligence or the proper use of the copious amounts of data available does not just apply to firms and businesses. With everything categorized and readily available at their literal fingertips, consumers are benefiting from the advances in the information industry. Consumers now can use this information to help them make decisions on investment, health care, choosing companies to do business with among other things and all in real time. This streamlined system and way of living where a hop on a search engine can teach a person everything they need to know in no time, makes life more convenient and decision making a lot faster. Plus the ease of finding the necessary information allows the consumer to feel empowered and informed, which helps their process to be smoother and more defined.


The first folks that come to mind when thinking of how to make money from these large amounts of information are stock market traders and investment specialists. Before big data analytics, the manual processing of all the documents needed to make great stock decisions was great and tedious. Banking associates, therefore, would simplify assumptions and use simplified files to process their work. This behavior would increase the risk of purchasing stock and make forecasts untrustworthy.

  • Investment forecasting – Companies like Amazon Web Services use big data for investment forecasting through cloud servicing. And the available unstructured public information such as company news, reviews and price list is usually processed quickly and in large amounts, so forecasting becomes easier, faster and less risky.
  • Manufacturing – An internal system created to use analytics to determine the cost of material at various times and determine the best time to purchase raw material helps these manufacturing industries to save on material costs. Through analyzing a combined database of suppliers, they had enough information to model all the suppliers and, therefore, save money.
  • Human labor Assistance – Companies that combine data analytics with human labor and never underestimate the need for humans or overestimate the use of big data are clearly going to make the most. Despite the many advances in technology, human input is still necessary and vital so one should not overlook this key element.
  • Understand big data limitations – Though it is great at processing copious amounts of information and turning it all into readable notes using its natural language processing ability, a company should not attempt to push beyond what it can truly do. Natural language processing is limited, because any form of artificial intelligence has only gone so far right now and in order to see advances in the company through use of these analytics, Information Technology specialists must respect the limitations that are set on big data processing and not depend on it for all their solutions. The managers need to ensure that this platform will indeed help the company profit long before deciding to invest in it.

In conclusion, the large amounts of information that stream into our data storage and warehouses are great to help companies make decisions faster and with more knowledge and clarity. And may even help with forecasting and problem-solving and even turn out to be cheaper than the traditional methods used to collect, store and process the data for companies. In this century, we are exposed to more data than from the beginning of time and so there has been the need for systems like Apache Hadoop and MapReduce to help us traverse the confusing and sometimes overwhelming pools of information. But as great as the process and insights are that are provided by the analytics from the large amounts of information, there are limitations we must be aware of, to ensure our companies are profiting from the wave of knowledge. An article in Wired.com warned that if companies start too big and believe a big investment will yield a big return, they would end up losing money. The best approach the author cautions is to analyze whether a smaller budget will help the company achieve the intended effect on a smaller scale. Like anything else, careful research and planning will be necessary to implement the right data analytics software into your business.

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