20 Biggest Pros and Cons of Big Data

Almost every enterprise-level organization makes use of big data today. The analytics available through this information creates a gold mine of potential benefits to use. There are also some significant challenges to consider that might offset some of the gains that small- to medium-sized businesses could achieve, which is why an individualized look into this asset is necessary for each entity.

NewVantage Partners found that over 97% of firms in their annual executive survey had an interest in using big data, artificial intelligence initiatives, and other forms of technology integration.

All of this interest is leading to a booming market for big data. It could be worth over $210 billion by the end of 2020, and then over $500 billion at the end of 2030. The only problem for today’s C-Suite leaders is that spending a lot of cash on these analytics won’t guarantee that an organization can get the results they want.

That’s why each leadership team must complete a thorough examination of the pros and cons of big data.

List of the Pros of Big Data

1. Big data provides opportunities to make better decisions.
The primary goal for most businesses is to improve their decision-making by investing in big data. When there is more information available in a usable manner, then it is easier to see what customers want or don’t need. Data-driven insights make it possible for smaller companies to compete or grow, while larger organizations can use this information to stay on top of different trends and behaviors. That doesn’t mean there’s a guarantee of success from this advantage, but it can get the debate started off on the correct foot.

2. Productivity increases can occur when using big data.
Big data tools allow analysts to look at more information at faster speeds. That means their personal productivity levels rise, creating a tide that lifts all boats. This advantage also gives individuals more data about themselves so that they can recognize areas where they could be more productive in their activities. That’s why an investment in this technology often creates a slow rise in results that starts from the bottom up.

The insights gleaned from big data can also help organizations find new ways to be efficient at current productivity levels. That means there are broad, positive implications that are possible to achieve while individual results can find more narrow paths to walk.

3. Big data helps to reduce costs for organizations.
The use of big data analytics helps organizations begin to decrease their expense profile. In a Syncsort survey of companies that worked to implement policies in this area, almost 60% of them reported that they experienced increases in operational efficiencies and cost reduction. Two-thirds of respondents noted that a decrease in overall expenses began to develop.

This advantage might not be one of the primary goals that leadership teams have in mind when they start the implementation processes for big data, but it certainly becomes a happy side effect when everything starts working as it should.

4. Big data helps companies improve their customer service approach.
One of the most-cited goals of a big data implementation effort is to improve customer service interactions. AI, machine learning, and similar systems can analyze information from CRM systems, social media, and email interactions to provide a wealth of info about how people think and feel. Having access to the analytics from data gathering operations makes it easier to serve customers when something unexpected happens.

5. It can help with fraud detection.
Another common advantage that businesses discover with big data is that it can help with fraud detection. This benefit is one that is most commonly cited by the financial services industry, but any business can take advantage of these opportunities. AI and machine learning can detect anomalies or transaction patterns that are not part of the regular routine for individual accounts.

This ability gives credit card companies, banks, credit unions, and many other merchants the option to spot stolen identification materials, account information, or access products to prevent losses. From a financial services perspective, this advantage is so profound that the detection often takes place before the customer even knows that something is wrong.

6. Big data leads to more innovation across each industry.
When there is more information being reviewed on behalf of customers, then there are more opportunities for innovation that develop for organizations. Investing in analytics provides a way to become more disruptive in markets because it changes how pain points get addressed to the consumer. Insights become available that aren’t in the hands of the competition, leading to new paths that help to get a company ahead of the rest of the market. Many of today’s best products and services in every industry are available now because of the processes that big data provides.

7. Companies can reach the market faster with new products or services.
Another advantage to consider with big data is its ability to get new ideas to the market faster. It might not be the primary goal for many companies, but most can achieve some level of success in this area without much effort. The analytics that become available through this process make it possible to achieve higher revenues, bigger profits, and faster growth in almost any area of the agency when enough attention is provided to the process.

8. Big data provides more agility where it is needed the most.
Companies that use big data can discover weaknesses in their approach to a variety of different areas of their industry. One of the most lucrative spots is in IT agility. It is easier for organizations to align their IT department with their ongoing business efforts because of this approach, using analytics as a way to support efficient change or faster updates to their operations. It is one of the most effective ways to manage corporate tactics or strategies from an ongoing perspective.

9. It helps you to structure your incoming information.
The analytics approach allows companies to structure themselves better using their existing data resources. Businesses acquire information from a variety of data streams that often remain unorganized and unused because no one knows what to do with this asset. Implementing big data solutions gives you more structure and support before housing it in your lake or silo so that faster access to the product is available when decisions need to get made.

The analytics might unearth new patterns that you can use, but it also creates more foundations to build upon throughout the organization. When everything starts at the data lake, then each department has more common ground to find with each other to support the vision and mission of the organization.

10. Big data works to keep your other information safe.
Big data requirements include the need to deploy immediate real-time verification whenever information access occurs. Using these techniques with the company’s system allows for the other info within the organization to become more secure. Organizations must follow their protocols to the letter to achieve this advantage, but it allows for an investigation into any threat that might be present and its eventual eradication.

11. It helps to build trust with potential customers.
The risk of a potential security threat often overshadows the idea of big data implementation for many companies. By administering new techniques to protect customer privacy through analytics implementation, big data provides more security that can build trust and loyalty with potential customers. Organizations can identify small discrepancies in any account because of this process, creating relationships that may eventually lead to repetitive purchases and higher overall revenues.

List of the Cons of Big Data

1. There can be a lack of talent in this area for most businesses.
Big data analytics isn’t an asset that the average IT personnel can look at to glean useful information for decisions. Companies need information scientists who know how to glean results from this approach. That makes this position one of the highest paid IT areas available around the world right now. Most small- to medium-sized businesses can’t afford this expense, which means they’re forced to implement structures with the internal talent they do have – or rely on outsourcing.

If you don’t have skills available or staff who understand this information, then creating a data lake is an almost worthless endeavor. It can take quite some time to hire or train the right people to maximize processes.

2. The quality of the data is sometimes questionable.
Another significant disadvantage to consider when using big data as an asset is the quality of the information being collected by the organization. Before any of the info is usable for analytics, analysts and data scientists must ensure the accuracy of what they receive. Then they must determine the relevance of each data lake and format it correctly for review. These necessary tasks can slow the reporting steps considerably.

It also creates a problem where the insights gleaned from the analytics might be worthless. It might even be harmful to proceed if acted upon in some situations. That means the investment might not provide returns for several years, if ever at all.

3. Big data creates a need for cultural change.
Most organizations that decide to embrace the idea of big data want to change the culture internally so that the entire company begins to see the benefits of using analytics. The amount of investment needed to get this process started means that a small gain in reporting isn’t going to be a good enough result. In the NewVantage survey regarding the pros and cons of big data, almost 99% of executives said that their firms were in the process of creating a new culture for their teams, but only one-third of them were having success.

What makes the change to big data so intimidating to the average person is a fear of AI and machine learning. Any job that offers repetitive activities is one that could be replaced by computers in the near future. Who wants to embrace the idea of using analytics when those activities might generate future layoffs?

4. Compliance issues can be costly when managing big data.
With legislative updates happening in Europe and the United States, the use of big data analytics creates storage concerns that companies of every size must consider. The information collected from consumers must meet or exceed the current industry standards or requirements for protection. If that level of security is not available or obtained if a breach occurs, then the number of liabilities could be massive.

Compliance with big data standards is one of the most significant barriers that organizations of every size face when considering analytics. Since politics can cause alliances to shift at any time, organizations must be on their toes to update systems. That means there is a cost always associated with this activity, so the profits must grow to help offset the overall expense needed to implement this system.

5. Big data provides a bevy of security risks to manage.
Most of the information that companies collect in a data lake includes sensitive info that requires a specific level of protection. Having access to these analytics can make an organization become an attractive target for a potential cyberattack. A data breach is often the single greatest threat that a company faces when trying to create this culture.

Preventing a data breach starts by keeping only the information you need. Reducing the volume of what you collect will stop some of the attention from cybercriminals. Then lock physical records away and destroy before disposal to ensure your company remains in compliance.

6. It creates rapid changes in corporate culture that are not always readily accepted.
Big data is not something that everyone will accept. Some workers don’t like the idea of change at all. Others face the possibility that investment into this technology will be out-of-date only a few months later. The costs associated with training new workers, updating policies and procedures and similar challenges can be enough to offset the gains that many organizations discover when analytics can provide new ideas or approaches.

7. Companies must meet specific hardware needs to have a successful experience.
Another significant problem for organizations that want to embrace big data is the need to establish the correct level of IT infrastructure. Analytics efforts will not be as efficient or useful if the foundation of the system is slow, lacks storage space, or doesn’t offer enough security. Networking bandwidth is another consideration to review, and then there are the computing systems and servers that might require installation.

Some of this disadvantage can be offset when cloud-based analytics become a priority, but even that option doesn’t eliminate potential infrastructure issues. The smallest companies tend to experience this disadvantage most often.

8. The cost to implement big data systems can be significant.
Most of today’s big data tools rely exclusively on open-source technology. That fact means the software costs virtually disappear from this effort to glean insights, but it also creates an issue with hardware, maintenance, and staffing issues. Most projects in this category go over their allotted budget because the expense ratio from the software is expected from a labor standpoint, but that just doesn’t happen. It usually takes more time to deploy additional IT managers than what people originally anticipate.

9. It can be a challenge to visualize data.
The goal of analytical procedures with big data is to convert it to a usable format as soon as possible. That work makes the information more readable, but it is challenging to manage it when a significant amount of it comes in to decipher. All data might be interesting, but very little of it is usually useful. Out of a hundred pages of content, you might have a couple of paragraphs that provide actionable substance.

The speed of collection is often rapid, which means even more data begins to get collected and stored. Having an influx of streams can lead to delays or hinder the entire process for an organization if left unchecked.

Conclusion

The pros and cons of big data are essential to review before an organization of any size decides to use analytics to their advantage. Numerous advantages are possible when there is more information available to review, but companies need people trained in this area to create usable results.

Enterprise-level organizations can immediately benefit from this process since their ability to adapt to changing laws and regulations isn’t as impactful to their bottom line. SMBs and SMEs can struggle in this area because changing mindsets could mean the difference between a profit or a loss during the year.

That’s why the use of big data should be approached with relative caution. Some companies may not have a need to invest in this area because they provide niche products or services. When you evaluate the benefits with the potential disadvantages at an individualized level, then it will become clear if this process is worth pursuing.