- Collecting data
The first step in any big data science project is finding and gathering data assets necessary for the project. However, there is a challenge identifying relevant data by organizations and data scientists, considering data coming from different sources are diverse. Data collection is an important step, considering the data collected impacts the ability to build robust ML models. Businesses collect massive amounts of data, sometimes without doing anything to determine if the data they gather will be useful or not. Without this ability to distinguish useful data from the rest, there is a general concern that key insights that could be gained from it can be missed.
- Too many data sources
Companies are now collecting data about their customers, sales, staff and markets. They use various tools, software and CRMs to achieve this, and data come from various sources. While gathering data from different sources and with diverse tools is good, consolidation and management often present a huge problem. As the number of tools and apps available to the organization to gather all the data rises, problems of data consolidation increase. This results in disparate and semi-structured sources. This is a challenge for big data.
- Security and privacy
Getting the right datasets is not the end of problems in a big data project. With such data in your possession, the next challenge is accessing them. There are growing privacy concerns and compliance requirements that make things hard for data scientists to access data sets. Furthermore, the increasing transition to cloud environments has increased the number of cyberattacks in recent years, leading to another headache for data scientists. This has led to increased security and regulatory requirements, affecting access to data needed for specific use.
- Finding the right talent
The rise of big data and its importance in helping organizations make tangible decisions has increased the demand for professionals in this area. It has led to a huge skills gap and shortage of talent in the data science space and the entire tech sector. With such a shortage, organizations struggle to find the right people with the right knowledge and expertise for their teams. Furthermore, finding people with the right domain expertise and business perspective on data science is a problem.
- High entry barriers
Starting your big data project is an expensive undertaking. It involves building a team, managing projects, and developing and deploying tools. These are just the bare minimum requirements that can be more in bigger enterprise-level companies. Smaller companies may struggle to achieve this, especially when the big data projects are not delivering results faster than one might expect. This problem has seen many companies suspend or cancel their projects midway.