Understanding the Pros and Cons of Big Data

According to a report by the Tech Jury, the big data market is set to grow by 20% in 2019. Indeed, by 2020, every person in the world will generate a mind-boggling 1.7 megabytes of data every second. In total, internet users will generate about 2.5 quintillion bytes of data in 2020, bringing the total amount of data generated from computer-enabled devices to date to over 40 zettabytes (equivalent to 40 trillion gigabytes).

Even more impressive is the fact that 90% of all the data available today was created in the last two years. 

What is Big Data?

The data generated by users around the globe is critical to understanding not just humans but also the things we interact with. 

For example, data collected over the years is facilitating medical research by unearthing patterns vital in finding cures for challenging diseases such as cancer. Data is also helping people in commerce understand what consumers want and ways to keep customers happy. And, in transportation, data has been central to the development of driverless cars. 

Big data refers to the technologies put in place to capture, collect, and process the tons of data generated each day. It is the collection of frameworks, techniques, and tools used to acquire and process data in search of useful information. 

Typically, once data is captured, the big data sets are placed in semi-structured or unstructured databases for further processing.

What are the Pros of Big Data

Big data comes with multiple advantages, including improving organizational performance, empowering the digital information age, and real-time updates. The following is a summary of these advantages.

• Better decision making

A recent survey by New Vantage Partners shows that 36.2% of organizations consider better decision making as the number one goal of big data analytics efforts. Up to 84.1% of the respondents said they had already started working towards that goal, with at least 59% saying they had seen some level of success. Overall, the success rate in this area is 69%

In short, big data analytics give decision-makers the data-driven insights they need to compete and grow. Whether it is corporates turning to Facebook to find out the demographics of their customers or an advertiser using Instagram analytics to find the right influencer, the answers to today’s big questions lie in the vast chunks of data generated every day. And Big data makes it possible to extract the hidden patterns. 

• Increased productivity

Big data is also playing a starring role in boosting worker productivity. A recent survey by SyncSort shows that up to 59.9% of organizations that use big data tools such as Hadoop and Spark are motivated by the technology’s ability to increase business user productivity. 

The large companies, for instance, generate tons of data every day. To gain useful insight from the data, they need to sort through all of the data generated within the organization. And this is only possible using big data tools. The insights gained from these analytics also help them increase productivity to enhance their competitiveness. 

• Reduced operating costs

Every organization strives to keep costs down. Both the New Vantage and SyncSort surveys referenced above found that companies that leverage big data to break down and gain insight from the data they generate significantly reduce operating costs. 

In the SyncSort study, nearly 59.4% of the respondents said that big data tools have helped them increase operational efficiency, resulting in reduced costs. In the New Vantage survey, meanwhile, up to 66.7% of the respondents said they had started using big data to reduce the cost of doing business. On average, about 13% of the respondents in the two surveys chose cost reduction as their primary reason for using big data analysis. 

• Improved customer service

Finally, in the consumer age, knowing what the customer wants is vital. To remain competitive, you must anticipate the customer’s journey rather than wait to see where they go. Big data analytics is enabling organizations to better understand and predict customer needs, thus allowing brands to position themselves for growth.

The New Vantage survey shows that after improving decision making, bettering customer services is the second most common goal among organizations using big data analytics. Already, as many as 53.4% of the respondents said they had seen some level of success in this regard. The majority of the respondents also say they mostly use big data tools to analyze customer relationship management (CRM) data. 

Cons of Big Data

Despite the many advantages, however, big data also comes with a few downsides, with data privacy and cost barriers topping the list. Here are the pitfalls of big data;

• Data privacy and security risks

All organizations that intend to gather data to gain insights cannot just collect the data without permission from the sources. Instead, the data collection process must be done within acceptable legal and ethical frameworks. 

Unfortunately, this hasn’t always been the case, a classic example being the Cambridge Analytica saga. Cambridge Analytica is accused of having conspired with Facebook to harvest the data of billions of people around the world without proper consent. 

Many organizations continue to do the same, even if they haven’t been caught. The personally identifiable information (names, identification numbers, social security numbers, and so forth) collected by these companies is sometimes passed to third parties without permission. Doing this is not only unethical but also illegal and could attract a huge fine and even legal sanctions for the involved organizations. 

• Technical problems and limitations

The other major challenge in big data implementation is technical complexities. First, data analytics is a technical subject that requires special talent. AtScale recently found that there’s a severe lack of the required expertise in data analytics. Indeed, the talent deficit is currently the most significant challenge when creating a data lake. 

Secondly, even where there’s sufficient talent, data quality issues persist. In the SyncSort survey, participants voted the need to address quality issues as the number one disadvantage of working with big data. Ensuring that the analyzed data is accurate, relevant, and in the proper format is a tedious process that considerably slows down analysis.

Finally, integration with legacy systems is a headache that makes big data implementation in some organizations near impossible. Most large enterprises have been around for decades and, over that period, siloed data in different applications and systems. Bringing together those data sets and moving them into a single data lake can be a monumental task. 

You Don’t Have to Do it Alone

Owing to the challenges mentioned above, implementing big data can feel overwhelming. And that’s before you account for costs. NIX is here to help. Boasting a vastly experienced team and the requisite tools, we can help you break the barriers as you strive to reach the big data promised land.