Big Data is not the latest jargon that has crept into executive meetings, it’s becoming an essential business practice used by most organisations today. Over the years, businesses have become aware of the insights that they can gain from data analytics and are collecting increasing amounts of data. Yet, many businesses do not have a proper data strategy in place and are simply collecting data in a frenzy. There is a difference between Big Data and having lots of data. Collecting data just for the sake of it in hopes of using it in the future is not only bad business practice, it leads to potentially costly problems for your company.
Here is a list of issues that companies without a proper data strategy may face. If your company is experiencing any of these problems, it is a tell-tale sign that you need to review your company’s data strategy:
1. Storing data is starting to cost more
Even though the price of data storage has plummeted over the years, a poor data strategy will lead to high data storage costs. According to Experian, an information services company in the US, “77 percent of CIOs believe data is a valuable asset in their organisation that is not being fully exploited and 70 percent believe they have underutilised data in their business that is costing them money to store.” With the price of cloud storage as low as $0.03 per gigabyte per month, many organisations may be tempted to store all the data that they can get their hands on. Initially, things may seem under control. But as your organisation acquires more data over the years, your database will soon to be filled with legacy data that has long past its usefulness along with redundant data that are merely duplicates of others. Backups of backups become increasingly expensive and the cost of maintaining your database threatens to make a considerable dent in your IT department’s budget.
2. Restructuring your database is nearly impossible
Data restructuring involves changing the way your data is logically or physically stored. There are many advantages why data restructuring should be performed. For instance, data restructuring is done to improve performance and storage utilization or to facilitate data processing. When you collect data from different sources, there comes a point in time when restructuring your database becomes necessary to align data collection with business objectives. The problem arises when you have so much data that database restructuring becomes nearly impossible to perform. With entire departments relying on both current and legacy data, database downtime bears too much of a risk for data restructuring to even be considered as an option. Being unable to make the difficult yet necessary decision, your data is trapped in limbo and is unable to be utilized effectively.
3. Data analytics is becoming harder to implement
In order to perform data analytics, you need a good understanding of the data that is available to you. This includes knowing what type of data you have collected, where your data comes from and how it is stored. For a small database, a comprehensive audit can be performed fairly easily. But when you are dealing with terabytes of data, this process can become complicated. Due to the large volume of data involved and depending on how your organisation is structured, obtaining a complete picture of your data assets might not be possible. Without this knowledge, data analytics becomes harder to implement which often leads to conflicts between IT departments and managers who expect results without fully understanding the capabilities and limitations of Big Data.
4. Insights become muddled
Data that is generated through business activities or research can be used to gain invaluable insights into consumer preferences. While it is tempting for companies to believe that gathering more data and crunching even more numbers will lead to better results, this is far from the truth. When it comes to data analytics, more data doesn’t mean better insights. In fact, the more data that is available, the higher the chances of using wrong or inappropriate datasets for analysis. Many organisations succumb to this base rate fallacy when they collect more data than necessary.
5. You experience analysis paralysis
Sampling your data is necessary to decrease the time and costs involved for data analysis. For a small dataset, sampling may be as easy as including all of the records in your database. The difficulty arises when you have large amounts of data at your disposal. With a large database, a multitude of sampling techniques become available to you. Many businesses find themselves spending a large amount of time weighing the advantages of each technique and second guessing their decision. As a result, management teams experience analysis paralysis, which stalls the discovery and implementation of insights.
6. You risk losing everything if your data is compromised
Businesses have long been a favourite target for hackers bent on pilfering private data for identity theft and exploitation. While breaches at big corporations such as Target and Yahoo make the headlines, small and medium enterprises are still very much targets for hackers. Smaller enterprises have digital assets that are equally valuable but have less security as compared to larger enterprises. Without a proper data strategy, you may risk losing more than just your data. If security measures are not in place, inadvertent data loss could be a highly likely result. If your organisation deals with sensitive or private information, your reputation and relationship with your customers could be on the line.
Implementing a proper data strategy for your business is critical to prevent potential costly problems from arising. Failing to do so may lead to serious consequences that would negatively impact your business. If your business faces any of the problems above, consider reviewing your existing data strategy today.
Do you experience similar problems in your company? Let me know what you think in the comments below.