Pros and Cons of BigData

The term “big data” refers not only to large data sets, but also to the frameworks, techniques, and tools used to analyze it. It can be collected through any data-generating process such as social media, public utility infrastructure, and search engines. Big data may be either semi-structured, structured, or unstructured.

Typically big data is analyzed and collected at specific intervals, but real-time big data analytics collect and analyze data constantly. The purpose of this continuous processing loop is to offer instant insights to users.


Now let’s discuss some of the advantages of real-time big data analytics.

  • Quickly recognize errors — Let’s assume an error has occurred, and needs to be resolved ASAP. With real-time big data analytics, this error can be recognized immediately and quickly remedied. This can help prevent more numerous and/or more severe failures. In the long term, this also helps a business’ reputation — rapid error corrections could help in gaining more customers.
  • Savings — Even though implementation of real-time big data analytics can be expensive, the high value of immediate data analysis can make up for this expenditure.
  • Progressive services — Monitoring products and services through big data analytics could lead to higher conversion rates for customers, which in turn could lead to higher profits. Imminent errors and issues can be easily predicted with analytics, which could also help in focusing more on customer needs.
  • Real-time fraud detection — The team managing the security of the systems and servers can be quickly and easily notified of fraud, allowing them to take measures in real time, as soon as the fraud is detected.
  • Strategies toward competitors — Competition scares many people in the market today, and big data analytics assists in providing a detailed picture of competitors, such as launching a new product, lowering/increasing prices for a particular duration or focusing on users from a specific location.
  • Insight — Sales insights are vital for knowing where sales stand. These insights could lead to additional revenue, such as not losing a customer in the long term, checking the bounce rate and finding optimal ways of increasing sales through analyzing real-time big data analytics.
  • Trends — Decisions by analyzing customer trends can be done with real-time big data analytics. This could include offerings, advertisements, customer needs, offers available for a particular season and others. Therefore, it can also improve long-term decisions.


Now let’s have a look at the cons.

  • Tools not compatible — As mentioned earlier, the most widely used tool for big data analytics are not currently able to handle real-time data. Therefore we have many expectation with Hadoop that in future Hadoop will add functionality for a real-time approach.
  • New approach required — Some organizations are used to receiving insights once a week. However, with the constant inflow of real-time big data, a completely different approach is required. This could be a challenge for some organizations and could lead to remodeling of some decisions and plans.
  • Possible failure — Some organizations may see real-time big data analytics as a shiny new toy, and want to implement it immediately. However, if not implemented properly, this could cause a multitude of problems. If a business isn’t used to handling data at such a rapid rate, it could lead to incorrect analysis, which could cause larger problems for the organization.

The Big Data trend is sweeping across every industry – and companies everywhere are keen to learn more about the subject so they can better understand what it can do for their business.

Gartner, as you might expect, is very much at the forefront of bringing fresh insight into the subject. And it seems from a survey report they released that there is good reason for this. Even though the Big Data concept has really only taken hold in recent times, their research suggests that 84% of organizations are now planning to invest in Big Data solutions within the next two years (news that is almost certain to keep Big Data in the headlines for several years to come!).

Although actual adoption of Big Data technologies in the here and now is still in its infancy (8% according to the Gartner research), there are a wide range of business challenges that have the potential to be transformed. In this post, we take a look at the top 5 business problems that are highlighted as areas which companies are looking to address through Big Data solutions.

1. Enhancing Customer Experience
There’s a growing body of research which suggests that businesses who invest in understanding their customers better can outperform their peers by a significant margin – and Big Data technologies can play a pivotal role in this understanding.

The entire Big Data concept as it relates to customer-centricity lies in taking multiple sources of information, aggregating it, and using it to produce real-time business insights that deliver improved insight into customer behaviors. One of the early adopters of Big Data is the retail industry. As an example, by using Loyalty Card data combined with other sources of information, retailers can quickly track and record what their customer’s habits are. This helps them to predict which discounts or promotions would have the most likelihood of enticing them back to their store. The result? Improved customer retention, increased add-on sales and improved brand awareness. Retailers with more advanced mobility enhanced systems can even run these types of discounts and promotions when the customer enters their shop, helping to increase average transaction size with real-time offers.

2. Process Efficiency
Achieving process efficiency is the Holy Grail for both manufacturing and service organizations alike. However, this has been difficult to achieve in the past since there was no way to capture huge, disparate data sets for processing and analyses. A simple activity like month end closing used to produce financial statements is a great example of an onerous, iterative challenge experienced by many organizations. This is simply because of the lack of tools and technology available to process huge sales data multiple times, from many different sources and data feeds. This leads to an inability to aggregate and allocate costs being incurred through various sources and to crunch the numbers to produce figures under the various heads in financial statements. Appropriate use of Big Data technologies can reduce the time taken to perform month end closing activities from days to hours!

3. New Product Development
In their 2013 Innovation Monitor report (*subscription req’d), the British Manufacturers’ Association – EEF – revealed that 75% of manufacturers believe that speed to market is more important than it was in the past.

A key factor that’s driving this need for speed is that in today’s global marketplace product life-cycles are shorter. Competition from previously low cost manufacturing bases has started to intensify, with companies in these countries increasing their own levels of innovation in order to move up the value chain. As these competitors start to innovate more so the product lifecycle is shortened as technical edge is lost to other, newer ideas.

Deploying applications that can analyze Big Data sources can help build an overall picture of consumer demand and help identify market gaps that might be filled with a new product or business service innovations In addition, having an iterative, constant flow of real-time data means companies can be increasingly responsive in adapting their product developments to the needs of the market today – and they can gain that all important first-mover advantage when taking new products to market.

Another key advantage is that product development also requires a certain amount of time in research labs – be it a physical product or software. Big Data technology can potentially help in simulating various outcomes during the development phase or in analysing various test results quickly which of course allows for course correction actions before it’s too late.

4. Targeted Marketing
Gaining the tools needed to analyse Big Data stores means companies can more quickly and efficiently segment and analyse patterns, trends and sentiments that help them better understand buying behaviors. For example, banks and financial institutions are using insights gleaned from daily transactions, market feeds, customer service records, location data and click streams to create new business propositions and improve their go to market strategies.

Access to this kind of information means an opportunity to make better targeted marketing approaches – much faster than ever before. In a recent Guardian article on the subject of Big Data, Matthew Bayfield, group director of data for marketing agency Ogilvy EMEA said: “The new way of thinking about [data] is more like trying to read the river, you’re trying to spot patterns. There are numerous pots of information that exist in a digital ecosystem that [companies] can tap into to try and understand more about the consumer and what the consumer wants.”

The greater the visibility of data, the greater the opportunity to market successfully. And, with more and more consumers using digital technologies, the more important a solution that addresses that challenge becomes.

5. Cost Reduction
We normally think of Big Data solutions as expensive, so you may be surprised to see cost reduction at 5th on the list. Even though the initial outlay for a solution can seem expensive, the benefits of deploying such technologies can help to reduce cost in other areas of the business. For example, eBay use SAP’s latest Big Data innovation – the SAP HANA Platform – to manage foreign exchange and improve the hedging process. By using analytics solutions powered by the SAP HANA platform, eBay gets a complete view of cash across its entire organization – meaning they can drive proactive currency management, increase profitability, and improve operations. This has resulted in estimated savings of $40M per quarter from better decision making on currency hedges based on real-time data and trend analysis.

Real-time big data analytics can be of immense importance to a business, but a business must first determine if the pros outweigh the cons in their particular situation, and if so, how those cons will be overcome.

This is still a relatively new technology, so it is expected to evolve in the future and hopefully resolve some of its current challenges.

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