Big data is the talk of the town in business offices today. It’s big – and only getting bigger. Though big data can provide valuable insights, it is still seen by most as a daunting behemoth; it’s difficult to accurately and efficiently determine big data’s best usages simply due its sheer volume. The key to optimizing this influx of information lies not only in processors such as Hadoop, but also in other tools that ensure a company’s ability to glean the information it needs from big data.

Bad data obstructs a system’s efficiency. It takes up space that could be better used for relevant, valuable data. In the age of big data, tackling data quality issues manually requires much time and money. Luckily, platforms and accessory tools like Hadoop allow companies to eradicate this problem and quickly and accurately filter big data. These auxiliary instruments are amenable and exist to meet the diverse and tailored needs of singular firms.

However, mere incorporation of such tools is not enough. There is much left to learn about big data. As such, it remains a questionable unknown. For those not at the forefront of the technology movement, it may be difficult to embrace big data and the innovative capabilities it affords. To those pitching to such audiences, it is important to identify specific issues directly related to big data. Utilizing big data isn’t just about quantity – by correctly incorporating big data filtering tools like Hadoop, one can also optimize the quality of big data.