The history of Big Data and the latest considerations
Even though “big data” is a relatively new term, the concept of collecting and storing data in considerable amounts for analysis exists for ages. The concept of data analytics gained momentum in the 2000s when the industry expert, Doug Laney first articulated the mainstream big data definition of now, ‘the 3 V’s.’
V1: Volume: As we can see, organizations now collect their data from various sources, merely from all points of their business transactions through the website, social media, sensors, and also M2M (machine to machine) data. In the past; however, storing such a huge volume data could have been a real problem with the limitations of the relational databases; however, newer technologies like Hadoop have made it a breeze.
V2: Velocity: The next need is for the live data streams flowing in at an unprecedented speed should be handled in a timely manner. The sensors and RFID tags etc. are posting the need to handle torrents of data in a real-time manner.
V3: Variety: Unlike the strictly structured relational database models, modern data now comes in all types and formats. You may get them structured or unstructured or in the form of email, document, audio, video, or from the financial transactions from the stock ticker.
When it comes to the changing needs of big modern data, we may have to consider two additional dimensions also with the above 3 V's as:
Variability: Along with increasing velocity and variety, the current-day data flow may also be largely inconsistent in terms of periodicity. There could be a peak as well as lean periods, i.e., something like social media data. There may be some event-triggered or seasonal peak time data loads which may be difficult to manage, especially with data in the unstructured form.
Complexity: As discussed above, data can be from various sources, which further makes it difficult to match, link, cleanse, and transform the data across various systems. But, when it comes to gaining proper data insights, it becomes a mandate to correlate data relationships and hierarchies. Otherwise, the data may go out of control and become useless.
Why is Big Data important?
Even though many are adopting Big Data technologies, there are others who are confused about the relevance and importance of Big Data in their business. In fact, as RemoteDBA.com suggests, the relevance of Big Data is not specific to the data to handle, but also to the tasks you can do with it. With Big Data, you can drag in data from many sources and customize it to analyze and find better business insights in terms of:
1) Cost reduction
2) Time-saving
3) New product development
4) Marketing optimization
5) Smart business decision making etc.
If you successfully combine the benefits of big data with high-level analytics, then you can better accomplish the business tasks like:
- Determining the root causes of business failures and address the defects in real time.
- Automatically generating coupons right at the point of sales by understanding the buying habits of a customer.
- Recalculate the risk portfolios in real time and respond.
- Identify any fraudulent behavior at the first point and eradicate it before affecting eh organization.
Now, let’s have a look at some big data and database predictions by experts and how likely they are into practice.
Growth in data volume
There is no doubt the fact that data volumes may grow exponentially in the coming years. The number of a variety of handheld gadgets and internet-enabled devices is going to increase in the future, especially with technologies like IoT. This will contribute to more and more volumes of data, and the ways to analyze data also will increase. Even though SQL is there is practice as a standard, many NoSQL technologies like MongoDB and Spark, etc. are emerging in terms of data management and analysis.
Tools for big data management
There are also more tools coming out for data analytics without the need for an expert analyst. Salesforce and Microsoft both announced the latest featured apps to let the non-programmers also to create their apps for business data management. We can also expect prescriptive analytics also to be built on to the business management software. An IDC prediction says that almost all software for business analytics may include the needed intelligence by 2010.
Adding to it, real-time data streaming and business insights will also be made the hallmarks of the data winners. Users may be able to use this live stream of data to make business decisions in real-time with custom-built programs like Spark and Kafka etc.
According to Gartner, Machine Learning also will be a top trend for the coming days. This will be a primary element for data management and data-based predictive analysis to help businesses move forward. In the coming days, Big Data may also face many big challenges on privacy matters, especially as the information privacy regulations are changing in many countries. An organization may have to address an ‘elephant in the room' type of approach related to their privacy procedures and controls. In the next couple of years, about 50% of the ethics violations related to a business may be based on data. In terms of database protection, more companies may appoint a CDO (chief data officer) in the coming days.
Sooner or later, actionable data and faster data may replace big data also according to the experts. There is also an argument that big shouldn't necessarily be better in terms of the current pace of data growth and businesses may not be compromising to use the only a fraction of data they have access to. Instead, the whole idea lingers around the companies' need to focus more on asking the right questions and optimally make use of them.