7 Most Common Big Data Blunders Every Business Should Avoid
As more companies invest in big data and analytics, there is growing confusion around the best data practices and how businesses should leverage their new investment. While it is essential for businesses to understand how to maximize big data’s potential, it’s equally as important to know which mistakes to avoid. While big data is a worthy investment for businesses, it can easily be a wasteful one if they fall prey to the biggest big data mistakes.
Here are seven of the most common big data blunders every company needs to carefully avoid.
Failing To Move Data To The Cloud
When dealing with massive amounts of data, cloud storage is absolutely necessary. Businesses must move their data into a public or private cloud. There are plenty of firms that provide cloud storage for relatively cheap. Many firms offer top-tier infrastructure, excellent security, and knowledgeable staff. Storing your data on a cloud also allows for greater flexibility. Companies should take advantage of all the resources offered by cloud storage in addition to their on-site storage.
Ignoring The Potential Of Artificial Intelligence
As automation continues to sweep every industry, companies can no longer afford to ignore the potential of artificial intelligence. Artificial intelligence, and specifically, machine learning can significantly streamline your data processes, detect potential security threats, and provide more accurate insights. The applications of machine learning on big data are endless. Since artificial intelligence is the future of data, it’s a good idea to invest early in AI and machine learning capabilities.
Lack Of Data Quality Control
It’s important for businesses to prioritize high data accuracy and quality. Failing to do so could waste the valuable time of your data scientists. This a common mistake for a lot of companies investing in big data. As a result, far too many data scientists spend the majority of their time cleaning data errors instead of other tasks. Businesses can avoid this problem by assigning someone or several people to oversee data quality. Practicing good data hygiene is crucial to getting the most out of your big data investment as a business.
Relying On Data Warehouses To Solve Issues
While data warehouses are capable of solving a wide array of technical data problems, they are not intended for resolving numerous big data problems. Data warehouses are great for structured data, but cannot be relied on for less structured data. Use data warehouses for customer-focused data in a structured format from a limited number of data sources, not for resolving every issue that arises.
Dismissing Unstructured And External Data Sources
In addition to data from spreadsheets and databases, businesses must pay also attention to less structured forms of data. To dismiss unstructured sources of data is to waste the majority of the data you actually collected. Focusing solely on structured data is highly unproductive as a company. It would be wise to implement a strategy to sift through the unstructured data and find valuable insights.
On the other hand, it would be a mistake for businesses to not take advantage of external data sources. Government sources and data repositories should not be overlooked. These sources often provide valuable data and insight.
Solely Using Traditional Data Integration Techniques
In an increasingly agile world, traditional data integration techniques are simply not enough on their own. Techniques such as extract, transform, load (ETL) and master data management processes require a great deal of human effort. Therefore, it can be costly in terms of labor to implement as a company. Moreover, these techniques are old, inelastic, and will not scale. Companies would benefit more in investing money on cutting-edge machine learning technology.
Not Prioritizing Data Security And Governance
Businesses who invest in big data analytics must be mindful of data security and governance. The safety and governance of your data should be main concerns for every company. Ensure there are safeguards in place to protect the data your collect. Besides regularly auditing your data to check for inconsistencies, users who have access to the data must be carefully vetted. Meanwhile, effective data governance generates more accurate analytics. As a result, companies can gain more valuable insights to base their business decisions on by establishing good data governance.
Conclusion
There are a lot of benefits to investing in big data as a business. In order to reap those benefits to their fullest potential, it’s essential for companies to avoid these common big data errors.
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