by Angela Guess
A recent article offers helpful advice regarding how to successfully implement a Big Data strategy. The writer states, “Companies often turn to the traditional methods of adding more hardware, pushing data transformations elsewhere, such as down into the database, or custom coding when addressing data performance problems that arise as data volumes grow. Though these methods are common, they are typically not the best way to tackle big data and can actually hinder an organization’s ability to quickly adapt and respond to changing business demands.”
“For example,” the article continues, “adding hardware may shorten the elapsed time for data processing tasks, but it is costly at all stages, including the initial implementation and ongoing maintenance costs. Moreover, hardware alone can no longer keep up with the data growth rate many organizations are experiencing today. Pushing all heavy transformations out of ETL platforms and into the database creates other problems for the organization such as the inability to maintain data lineage and hindered agility. In many cases, companies find that they cannot deliver reports in an effective manner or cope with new requests for information. Custom coding can quickly become riddled with problems given its complex upkeep, ongoing labor costs and manageability issues.”
The article adds, “Since data warehouses are no longer economically or physically capable of managing big data within today’s commercial databases, new technology frameworks like Hadoop are emerging to track and manage these unprecedented volumes of data. Therefore, when devising a big data strategy, companies need to account for not only enterprise data, but also new sources of data, and then determine the best way to integrate the two for timely, accurate access to information as a basis for making business decisions.”
photo credit: Wim Vandenbussche
