Big data has become invaluable in driving smart business decisions and strategy-building activities. Through big data, companies have been able to redefine their business by increasing operational efficiency, discovering new market opportunities and regulating risk and compliance.
Organizations are willing to shell out huge chunks of their annual budget on big data projects, but the risk that comes with these investments can’t be ignored. According to research, 92% of organizations are still deciding whether to invest in big data, in light of the high failures their peers have experienced.
According to a 2016 report released by Gartner, 60% of big data projects have failed. However, this failure isn’t attributed entirely to technology and we’ve highlighted some of the other reasons below.
One major reason behind failure lies in integrating individual data from numerous sources to generate meaningful insights. Since data is being pulled from various systems into one common pool, i.e. a data lake, it’s difficult to determine what each value means and how it contributes to a meaningful conclusion. As only some of the data will be correlated or matched during integration, the data lakes don’t yield accurate results.
It may come as a surprise to learn that often big data projects do not have an end goal. Instead, companies go about accumulating data without identifying what business problems need to be addressed or how the data collected can help them.
Another major reason for failure is the lack of understanding of data analytics at the organizational level. The C-suite may not be equipped with digital leadership skills for driving business strategy. On the other hand, managers may not understand the data nor what it implies. Hence, the lack of an organization mind-set can result in the failure of identifying crises and introducing important measures.
Other than organizational dynamics and lack of adequate knowledge, other reasons for failure of big data projects lies in technical and non-technical database design errors.
Building a good database requires careful planning, a well-thought out process and execution. Poor planning could lead to structural problems which are difficult to troubleshoot once the database is designed and functional.
To avoid a bad data design, it’s important to understand the purpose of creating a database. To ensure success, you must consider the type of data that will be processed, how it will be procured and stored, the volume and which applications will make use of it. You should also check the design for high data efficiency, viability and security.
A major challenge faced by designers and administrators is that they bank upon system resources to stay secure, updated and to remain backed-up. Duplicate or redundant records can unnecessarily bulk up the database, decrease its efficiency and place it at risk of data corruption. Hence, it’s crucial for administrators to maintain clean systems.
As there are numerous databases, it’s vital to select the right one for database designing. A database type can be categorized in two different ways – the query language and data models.
For example, databases which leverage SQL are popular for handling structured data. While, NoSQL databases are a good choice for machine learning and web analytics due to their scalability and speed.
As the information in databases is confidential and privacy needs to be maintained, it’s vital to maintain the database on a different server than your application, and control access to it through authentication.
Overall, database design is an important aspect which should be planned for by selecting the right database, nomenclature, documentation and testing.
For a deeper insight into the complexities of database design and data analytics, pursue a Diploma in Data Analytics from Toronto School of Management.
