A List of 15 Issues With Big Data That Need Your Attention

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Over 90% of the world’s data was created in the last two years, and with 2.5 quintillion bytes of data being created every day, it’s clear that the future is filled with more data, which can also mean more data problems.

While it’s obvious that businesses can benefit from this increase in data, executives should proceed with caution and be aware of the obstacles they’ll need to overcome, particularly in the following areas:

  • Information gathering, warehousing, transmitting, and protecting.
  • producing actionable insights from their data, and acting upon those insights.

Companies can succeed in today’s data-driven economy by implementing some of the many practical strategies available to them for dealing with data issues.

In this article, we’ll examine some frequent data issues and the approaches that can be taken to resolve them.

Table of Contents

  1. Impairment of Comprehension
  2. Solutions to data problems are prohibitively expensive.
  3. Infinite Variety Is Overwhelming

4.Management of Data Complexity

  1. Problems with Security

6.Poor Information Quality and Reliability

  1. Problems with Compliance
  2. Discovering Meaning in Data
  3. Keeping Pace with Data Explosion
  4. Accessibility

11.Technological Rapidity

  1. Shortage of Qualified Employees
  2. Integrating Data
  3. The Handling of Massive Data Sets
  4. Variable Information
  5. Final Thoughts

1. Impairment of Comprehension

Companies can improve in many ways by utilizing data. Data can be put to good use in many ways, including cutting costs, generating new ideas, introducing novel products, increasing profits, and enhancing productivity. Unfortunately, businesses have been slow to adopt data technology or develop strategies for fostering a data-centric culture, despite the obvious advantages. Of the 196 companies surveyed by Gartner, 91% said they were not yet at a “transformational” level of maturity when it came to their data and analytics.

Solution: Introducing and training your organization on data use and procedures from the top down is one strategy for overcoming slow adoption. If you don’t have the necessary personnel on hand, it might be worthwhile to hire outside IT consultants or experts and host training sessions for employees.


2. Solutions to data problems are prohibitively expensive

Once you have a firm grasp on how data solutions can best serve your company, you may discover that the upfront costs and ongoing upkeep can be prohibitive. The price of servers, storage, and software is only part of the equation; human resources and time are also significant factors.

Solution: Knowing the why and the how of your data use will help you choose the most cost-effective data solution. Next, you’ll want to make sure your thinking matches up with your company’s objectives, look into potential solutions, and put together a strategy for implementing the findings.

3. Infinite Variety Is Overwhelming

Psychologist Barry Schwartz claims that less is more often the case. Schwartz calls the phenomenon of having too many good options (the “paradox of choice”) to actually make a purchase decision. Instead, stress and anxiety can be reduced by limiting a customer’s options. Choosing the right solution for your business, especially one that will likely affect all departments and hopefully be a long-term strategy, can be difficult because there are so many options available in the world of data and data tools.

Solution: If you’re having trouble with something like data comprehension, consulting an in-house expert like a chief technology officer can help. If that is not possible, consider employing a consulting firm to help with the choice. Take advantage of online resources like message boards to learn more and get your questions answered.


4.Management of Data Complexity

Making the switch from an older data management system and implementing a new one is a difficult task in and of itself. Further, systems can become complicated rapidly due to the presence of data from various sources and the fact that IT departments often generate their own data during data management.

Find a solution with a centralized control room, automate as much as you can, and make sure it’s accessible from anywhere, at any time.


5. Problems with Security

Security of sensitive information is a crucial issue that must not be ignored. When implementing solutions, it can be difficult to maintain a clear focus on data security due to the many moving parts involved. Furthermore, data storage must be done correctly, which begins with encryption and regular backups.

You can take some simple measures to significantly improve the safety of your data, such as setting up automatic security updates and backups, updating to the most recent version of your operating system (which typically includes improved security measures), employing firewalls, etc.


6.Poor Information Quality and Reliability

The value of information increases when it is reliable. Since it serves no purpose, low-quality data takes up valuable storage space and can hinder the ability to draw conclusions from high-quality data.

Some indicators of poor data quality are:

  • Errors in formatting (which can be difficult to fix because they occur when similar elements are spelled differently, such as “US” and “U.S. “)
  • A lack of information (i.e. a first name or email address is missing from a database of contacts),
  • Lack of accuracy (either the information is incorrect or it hasn’t been updated).
  • Multiple instances of the same information (i.e. the data is being double counted)

It is as useless as having no data at all if records are not kept or recorded accurately.

Start by outlining what information is crucial to collect (again, align the information needed to the business goal). Make it a habit to regularly clean data, and always properly organize and normalize data from multiple sources before importing it into an analysis tool. Data segmentation is possible after standardization and cleaning have been applied to the collected information.



7. Problems with Compliance

Concerns about privacy and compliance with laws must be taken into account during any data collection. The General Data Protection Regulation (GDPR) was only recently implemented, making it all the more crucial to comprehend the prerequisites for data collection and protection, as well as the repercussions of noncompliance. When using customer data for purposes such as determining which customers to prioritize, businesses must exercise caution and conform to applicable regulations. Big Data outcomes must be checked against traditionally applied statistical practices, which necessitates the following conditions to be met: data is a representative sample of consumers; algorithms prioritize fairness; there is an understanding of inherent bias in data.

The only way to ensure compliance with rules and laws is to acquire extensive knowledge on the subject. As the saying goes, “ignorance is bliss,” but in this case, it’s anything but that, as it can cost you both money and your company’s reputation if you don’t know what you’re doing. You should seek the advice of specialized law and accounting firms if you have any questions about compliance with any regulations.


8. Discovering Meaning in Data

It’s possible that you already possess the necessary information. It is precise, well-structured, and easy to read. However, the question remains as to how this data can be used to actually benefit the business. Companies of all sizes are increasingly relying on sophisticated data analysis tools that allow them to both get a bird’s-eye view of the situation and drill down into the details to find insights that can be turned into real change.

Solution: Be sure to implement a system to convert your data into actionable insights, whether that’s through a standardized reporting framework or a devoted analytics team. This involves analyzing data and turning it into steps the company can take to achieve success.


9. Keeping Pace with Data Explosion

Growing with data presents similar challenges to scaling a company. For the sake of maintaining competitive pricing and high standards as the business grows, it is important that your solution be able to expand in tandem with the company.

To solve this problem, it is necessary to introduce data and data management tools from the very beginning and to make projections from the outset. Choose a data solution carefully and find out in advance if it can handle the features you may need in the future. An alternate strategy for handling growth could be to rely on internal teams and support structures. You can set goals for your team to keep in mind, such as waiting to upgrade to a more advanced system until you’ve reached a certain point along the way.


10. Accessibility

In some cases, companies hide information from all but one employee or division. This not only places a heavy burden on a small group of people, but it also prevents the data from reaching the parts of the company where it could have the most positive impact. The value of data collection is undermined when it is kept in isolated silos.

Solution: Even though it may seem obvious, data integration is often overlooked. Establish measurable goals and develop a streamlined process that can accommodate the needs of all divisions. If you can’t find a single, unified system to meet your needs, APIs can help you centralize your data for easy access.

11.Technological Rapidity

Ray Kurzweil, inventor, author, and futurist, best described the rapid pace at which technology is evolving. As technologies improve in efficiency with each iteration, they become better equipped to guide subsequent developments in the field. Think about how quickly things like cloud computing and AI are evolving.

You don’t want your data tools to become obsolete, especially if you’ve put a lot of time, effort, and people into developing them, given how quickly technology and systems are evolving.

As a remedy, while you may not be able to halt progress, you can get ready for it. Firstly, it’s important to keep up with developments in IT and know what to look out for in terms of security, new features, and products.


12. Shortage of Qualified Employees

While artificial intelligence and data analysis tools are rapidly evolving to meet market demand, many businesses are finding it difficult to hire enough qualified workers to keep up. Due to a shortage of qualified new graduates, many companies are now asking their existing staff to take on additional responsibilities.

If a solution does not occur to you, then you must make one. Even though you may not be able to influence the annual graduation rate of data scientists and data analysts, you can still make use of your current workforce by investing in their education and giving them the training they need. A broader pool of less specialized analysts can be recruited if you look for more potent data tools that simplify the analysis work.


13. Integrating Data

In data integration, disparate data sets are brought together to form new insights.

Several methods exist for solving the problem of data integration, such as:

  • Data consolidation is the process of collecting information from multiple sources and storing it in a single, unified database.
  • Spreading: Using programs to move information from one place to another
  • Data from various systems can be matched using a model built on top of a virtual database, a process known as “federation.”
  • In computer science, the term “virtualization” refers to a technique that allows data to be viewed in a centralized location while physically

14. The Handling of Massive Data Sets

It is difficult to process and make sense of large amounts of data. In the world of big data, volume, velocity, and variety are the three V’s. Data can be thought of in three dimensions: volume (how much there is), velocity (how quickly it’s being created), and variety (how many different forms it can take, like pictures, videos, and written words).

Solution: Large data sets, in whatever form they may take, present unique challenges, but this article has shown that a combination of human and technological efforts can solve these issues. Making sure data is accurate, integrating data, and cultivating a company culture that recognizes and appreciates the value of big data in making educated decisions are all necessary steps in the data processing process.


15. Variable Information

There is no such thing as a “set it and forget it” approach to establishing the necessary infrastructure and data management. Data is inherently dynamic because it is ever-evolving. Your customers’ orders and information, as well as their interactions with your business, are always evolving.

Solution: Incorporate data systems with sophisticated machine learning and interoperability to adjust to the ever-evolving landscape of data inputs and, in turn, outputs. It is possible to model future trends and gain insight into the causes of data changes by using systems that store both new and old data.


Wrap Up

Your data management is crucial in today’s information-heavy society. You must take the initiative to learn about and implement data solutions that support your organization’s objectives. If you follow these steps, you’ll be able to effectively deal with any issues involving massive amounts of data.

It may be necessary for some businesses to put together a special data management team. However, with the help of today’s data tools, it’s easy to supplement and leverage existing staff to help the company gain insights from data.