5 Big Data Failures & How To Overcome Them
Data has become a major driver of business success over the past decade. Many firms are appropriately working to revamp their business processes and structures to incorporate data-driven strategies. While these businesses may stress and claim to implement more analytics and data-oriented policies into their organizations, many of them suffer from big data project failures.
Here are 5 big reasons why big data projects fail companies that try to implement them, and some ways to overcome such failures.
Top 5 Big Data Failures
1. “Bad Data”
Possessing and managing data is a worthy organizational goal, but it’s practically useless if the data isn’t being used effectively to make a profit. These pieces of data can be ineffective for a number of reasons. Their overall quality can be found lacking, or the costs of collecting and maintaining the data can be too expensive. Poor data management can also result from data being held in departmental silos that do not communicate well with each other and with business leadership.
2. Not Monetizing Data
One of the prominent big data failures is an organization that does not prioritize and use data analytics at every opportunity. When leadership fails to create an actionable data plan for a business’ departments to work with, they tend to work out such procedures by themselves. This can lead to a lot of data-related business activity, but no united efforts or large-scale benefits for the organization as a whole.
3. Lackluster Defense
Some businesses fail to use data to track their customer relationships, how they compare with their competitors, and their compliance with state and national laws and other regulatory policies. This also can be caused by ineffective data security measures that can cause increases in data thefts or losses. Inadequate defense can quickly add up to a lot of wasted funds and valuable time, as well as possible legal issues.
4. Poor Organizational Capacities
The capacities center around an organization’s talent selections, their organizational culture, and their structure. Some businesses fail to hire the right individuals to properly handle and utilize data. They place the wrong individuals into positions that deal with data management and quality assurance. Departmental silos prevent the efficient spreading of data throughout the organization. While their leadership may claim that they prioritize data, their day-to-day actions do not measure up.
5. Ill-suited Technologies
Some companies have not invested in the right technologies to make the most effective use of their data systems and processes. Big data failures in technology can stem from simple data processing and storage, or from more complex processes such as analytics and data architectures.
How to Avoid These Big Data Failures
For Bad Data
Ensure that the organization’s data strategy has several important characteristics. Enterprise data needs to be assembled and stored in a way that it is simple and intuitive for data users to locate and to comprehend. This data must also be high-quality enough that it is deemed reliable to base assessments and executive decisions on. Possessing some proprietary data ensures that the data is not available to competitors and will not be easily misused.
For Data Monetization Failures
Organizations need to create business-centric data strategy plans that make their data work for them, gaining real monetary value from utilizing it. These plans could involve incorporating the data into the organization’s services and products, adding it as informative input for business analysis and assessments, or even selling off the data to interested third parties.
For Insufficient Defense
Good defense takes a variety of forms when it comes to data. Investing in a high-quality security suite for the organization’s data is a key part of this process. Some other actions that minimize business risks are maintaining privacy commitments, complying with regulations and regional laws, and staying away from actions that could be perceived as monopolistic in nature. Using data to keep tabs on a prospering rival company, finding ways to stay ahead of larger, big-name companies, and maintaining superb customer relationships are also recommended ways to practice effective defense.
For Inadequate Organizational Capacities
Data professionals are necessary to avoid big data failures. Companies need to hire specialists in reworking old technologies to integrate with modern tech, who can comprehensively explain business processes, and construct predictive business models to test out those processes. Those companies must also create a culture that knows which data is important to business operations, and is aware of how their data will create tangible value for their organizations.
For Bad Technology
A firm’s technology has to be efficient at delivering on data-related capabilities such as storage, access, and security. This technology must also be scalable with the firm’s business demands.
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