‘Big Data’ can be defined as the sets of data that are very difficult and too large for old-style data dealing out applications. In addition to this, the word is sometimes utilized to refer to other methods of digging out value from data or predictive analytics. In order to gain advantage from big data, companies need processing power, strong analytical skills and capabilities and raw storage.
Yearly revenue from the worldwide big data market was put in the section of $22 billion with the estimation that it would increase twofold after the next 4 years, in 2015. The biggest chunk of big data revenue branches out from professional services. This represents nearly forty percent of the market including big data storage and computing sprawling some way behind.
Currently, big data is most expected to be used in the following areas:
- Marketing
- Sales
- Finance
On the contrary, as the dependability of big data progresses, companies look for additional, long term opportunities for big data in the areas of logistics and risk management. Even so, some thoughtfulness is obligatory because data is not without its challenges.
Cloud computing can be named as another industry that is taking advantage of the growth of big data. The amount of storage and processing power needed to utilize big data is such that a lot of companies have taken to processing and hosting their big data sets in the cloud.
In a survey, sixty-nine percent of respondents expressed that their company used cloud technology for backup and storage of data. Whereas, fifty-six percent of the respondents expressed that they require cloud app development solutions in data analytics.
Nowadays, no one can disagree with the prospective value of big data. This is because it has provided an opportunity to ask analytical questions that people were not able to ask before.
This is only possible as the result of the combination of large, new and incongruent data sets in ways; not economically possible before. The main challenge is that it is getting ambiguous day by day to show business value.
Focusing on the Business Value
Big Data technology is passing through an amazing phase of innovation. Every day a new innovative idea or technology is announced. The main challenge is the rate at which innovation is being introduced in the big data; it is getting difficult to deliver the expected business results.
Here are some common examples:
— MapReduce is used for coding. When it came into the market, people immediately adopted and began coding with it. However, the success was short lived when Spark was introduced. Spark was so interesting that developers started opting for it. In other words, all coding had to be re-written, which meant a loss of thousands of programmer-hours. There is a high probability that there will be more tech innovation in near future? Who knows what will replace Spark?
— On another hand, imagine that you are managing a big data stack. In order to keep an enormous sized big data environment productive, you would require six to twelve various technologies for high-level analytic, storage, data warehouses and computing. Not to mention data detection, preparation, safety, quality, monitoring, and visualization. So much time is spent on keeping technologies integrated with each other. There is a general perception that analytical companies do not want to a part of system integration business. They always want to deliver workable and practical insights for their organization.
— A more interesting thing to notice is that there is a great deal of big data and analytical innovation beginning in the cloud. For example, Google Deep Learning on-premise service is not going to be offered anytime soon. There are unarguable benefits of using cloud app development solutions. However, there is a great amount of challenge in managing and designing a hybrid data management architecture that links on premise systems and data in clouds.
The example mentioned below demonstrates few of the common big data technologies. When you move from left to right, there is a progression from old to new technologies.
Embrace Change: The Only Constant In Big Data
Have you ever thought that how would you leverage the best technology while optimizing the gain on technology investment in big data for your company? You will not get your desired outcome if you spend the huge chunk of time on the big data technology change race. You only require a data management platform that best fits your business needs.
In the example mentioned below, a data management platform splits the analytics layer and data visualization from the radical big data technologies; storage, compute, data warehouses and distribution.
Element to Look For In a Data Management Platform
Hand coding will not scale to the enterprise-class problem or to the greater number of developers for companies that follow data centric strategy. In such environment, data must be used as a mutual resource process, data elf service or system. A different approach is being taken adopted by thought leading companies. Data management platforms are being used by them that deliver:
Abstraction: The platform creation environment must give a layer of abstraction between the underlying data technology and development layers. After beginning coding, use must use platform perceptively to determine the most suited engine for code running. The outcome will be positive if the platform supports the available current engines.
Modularity: You do not have to purchase the whole platform at once. You must be able to begin where it makes sense for you. In addition to this, you should advance your data management competencies at your comfortable speed.
A complete solution (End-to – End Solution): Full data management comprises of data integration, data security, master data management, data governance, and data quality and data discovery. This all must be incorporated.
Hybrid: The platform must ensure data management somewhere it resides, on-premise, something entirely different, cloud or big data.
Self-service: IT plays an imperative role in data delivery ready for business usage. However, after some time, it makes sense to permit the business analysts, the subject matter specialists, to do data visualization and preparation for their own use.
Intelligence: There has been an upsurge observed in IT budgets in 2017. However, that’s adequate to scale the needs for companies that are speculating to contest on the basis of analytics and data. The platform should fast-track efficiency by providing intelligence to give endorsements, and mechanize jobs like relating and parsing latest data for more understanding.
Data management is being reimagined to convey faster and greater business value.
