“You can have data without information, but you cannot have information without data”
-Daniel Keys Moran
The explosion of data available today is both a gift, and on the other hand, cost enterprises too much in many aspects. The ability to collect, store; and analyze huge quantities of data has changed the way companies do business, providing a competitive advantage to those that can best handle their big data. After each big explosion, there are many big problems:
The first challenge with big data is, that it is so vast and unsorted, that organizing it for analysis is a tricky task. A lot of big data today is biased and with missing context, as it’s based on convenience samples or subsets.
This leads to a second problem: the absolute amount of data — big data may draw the wrong conclusions – a signal error will appear, if large gaps of data haven’t been looked in detail by analysts. As a result, people have a false sense of security in the reliability of the data, which increases as the data sets get larger.
The third threat comes from unorganized big data with which we could make major miscalculations and broadcast them universally: we trust the statistics way too much, and fail to examine the data with a critical eye. Too often, we are quick to conclude that the data presented to us is factual, which is entering the risky depths in the context of big data.
In order to deal with big data’s faults, we must first understand its nature.
Big data is a mixture between technology and analysis. Initially there is extreme computational power required to gather, link, and analyze large data sets. Afterwards we need to analyze and draw patterns to make claims which are not limited to technology, society and politics.
How to fight the challenges of big data?
To start – every set of data must be analyzed. It should be clear that all data is originally wrong so when something seems right-statistically it doesn’t mean it is. Secondly, that data is not a course of action, but a tool to accomplish it. And thirdly, the major need is to interpret and analyze the data in order to use it – one can never sacrifice the common sense of data – it can’t be allowed for the data to make the decisions instead of you.
Here is one technical solution which can help you – Hadoop
Hadoop is a fully capable open source technology that supports and manages one of the organization’s greatest asset: its data. Its flexibility enables the handling of multiple data sources and reading data from databases. There are several different applications, but one of the top use cases is for large volumes of constantly changing data, such as location-based data from weather or traffic sensors, web-based or social media data, or machine-to-machine transactional data.
No matter for what purposes you need your data, here are some common scenarios where Hadoop can help you best:
Data staging: data is growing, and it will grow even faster with time. It’s also getting too expensive to extend and maintain a data warehouse – Hadoop’s low-cost processing power facilitates to free up your rational systems and let them do what they do best.
Data processing: Organizations are having a lot of trouble analyzing and processing normal data so that dealing with big data becomes secondary. Since Hadoop runs on commodity hardware that scales easily and quickly, organizations can now store and archive a lot more data at a much lower cost.
Data archiving: Businesses must keep their data for more than five years for compliance reasons, but would like to store and analyze decades of data – without breaking the bank (or the server).
Big Data Requires Big Experience
Techniques mean nothing if they are not being leveraged by people who are asking the right questions. Big data, emerging storage technology platforms and the latest analytical algorithms are enablers to business success — not a guarantee of it.
Companies need to look at the broader set of related project implementation risks, incorporate more data sources, and use better tools to allow them to move to real-time or near-real-time analysis and increase data volumes. They need to ask the following key questions when assessing their readiness to truly start benefiting from big data:
- What are the goals of the project – what does the company want to accomplish through a Big Data project?
- What current resources can the company build to develop a reliable and useful data management strategy? If this is outsourced, who is the right partner?
- How will the company avoid scope-creep?
- What are the criteria for success and how will progress be measured along the way?
- Can the company manage the structural and process changes that will inevitably result?
ScaleFocus, Big Data Team