One of the most prevalent uses of Hadoop architecture by enterprises is to create business intelligence and analytics tools that can be leveraged to identify areas that could be improved to foster greater efficiency and productivity. According to a study jointly conducted by Gartner and the Financial Executives Research Foundation, business intelligence and analytics were top areas of focus among surveyed CFOs. Overall, 15 of the top 19 processes that were identified as needing improvement by the study's participants could be addressed through the use of these resources. In addition, 59 percent of the survey's respondents cited the ability to facilitate operational decision making processes as an area that required more technological advancement. Furthermore, half of all participants stated that the capacity to effectively monitor business performance was an investment need as well.
The report indicated that enterprises could improve their business analytics deployment by facilitating communication between data scientists and the C-suite executives who make operational decisions. Specifically, executives should be aware of how these tools work and how to best utilize them to maximize their effectiveness.
An IT executive recently presented several steps companies can take to avoid common analytics pitfalls and optimize their business intelligence initiatives, including:
- Enterprises should broaden the focus of a business analytics program to the entire enterprise to find new connections and relationships. This includes expanding data collection efforts and taking a holistic view of analytics projects.
- Companies should look inward to find their data analytics leader. Many executives may be tempted to find a high profile hire who will jump-start operations, but an existing employee will already be familiar enough with the company's needs and culture to foster a successful data-driven culture.
Enterprises can attain significant benefits from their Hadoop analytics and business intelligence programs. However, getting the most out of these processes requires a broad vision, internal communication and a strong business culture dedicated to the pursuit of data analytics.