Cloud computing will become the core driving factor for the development of enterprise big data business

Nowadays, business managers will inevitably see words such as "big data" or "cloud services" almost every day. To stay competitive in today's marketplace, companies must make smart business decisions that will produce real results, whether those results are helping to increase revenue, retain customers, or improve the quality of their products. Big data analytics projects are a key factor in achieving these goals.

IDG defines big data as “the large amount of data information collected by enterprises from various sources, including transaction data from enterprise applications/databases, social media data, mobile device data, unstructured data/documents, machines Generated data, etc. "IDG said: A variety of high-volume, high-transmission data information assets can provide enterprises with better insights and help them make business decisions. "

Big data enables enterprises to gain a deeper understanding of their business and make strategic decisions in real time. In fact, according to IDG's "Big Data and Analytics Research Report": 1/3 of the respondents said that due to the implementation of big data projects in their companies, the quality of their decision-making has been improved and helped to make better decisions. planning and forecasting.

However, just like any emerging technology, there are also challenges brought by it. The first challenge is the massive data volume and transmission speed. Massive amounts of data that change in real time mean that existing tools and methods will no longer work. Businesses also need to consider where the data is coming from: in some cases, big data comes from millions of places – including customers, sensors, websites and social media.

The method previously adopted by enterprises was to handle the workload brought by big data by building or expanding enterprise capacity. This is a resource-intensive initiative that is costly and time-consuming. It requires a lot of IT staff's time and skills, and it won't be able to migrate your business quickly enough. Your business may end up spending more time and money on infrastructure instead of building great products and services.

Cloud services can help solve many of these problems. Not surprisingly, cloud services and predictive analytics will be one of the technologies most likely to have a disruptive impact on businesses over the next three to five years. If your business is going to leverage big data for predictive analytics, with its many capabilities Advantages, cloud services may be a key enabler.

Although there have been a large number of successful cases, it is not easy to actually implement a big data project. In fact, it presents many challenges, any one of which could derail a project before it even begins. In its Big Data and Analytics survey report, IDG identified the following five challenges:

1. Shortage of data analysis and management talents

The development speed of big data systems is so fast that it is almost impossible for ordinary enterprises to keep up with its development. New tools, features, and frameworks can develop and mature in a matter of months, leaving enterprises with a large gap in emerging big data skills, which can easily hinder the development of enterprise big data projects.

In fact, 48% of business respondents believe that the shortage of talents in data analysis and data management skills is the number one challenge facing their companies. Demand for big data skills, especially in the analytics space, is so high that 70% of respondents said they plan to hire people with big data analytics skills in the next 12 to 18 months.

Using cloud services, enterprises can take advantage of the latest technology without investing a lot of time and resources in ongoing setup, maintenance, and upgrades. Cloud services also allow businesses to use the skills they already have, while managed services can perfectly complement the skills they lack.

2. Budget restrictions

47% of respondents stated that budget constraints are the second biggest challenge facing enterprises today when implementing big data projects. This challenge is evidenced by the fact that cost factors have been the number one concern for most businesses for many years.

Most big data technologies require large server clusters, which require long configuration and setup cycles, resulting in large capital expenditures and maintenance overhead. To further complicate matters, as diverse data volumes continue to grow for existing applications or new business needs, data transfer speeds continue to increase, potentially leading to unsustainable IT costs. Businesses need to know how to get as much value from big data as possible while minimizing expenses.

They must be able to scale infrastructure to manage big data while reducing IT costs. This is exactly what cloud services can help enterprises do. Cloud services eliminate the need for businesses to purchase and maintain hardware and software infrastructure, and the significant capital expenditures associated with it. This in turn allows companies to reallocate limited funds to their core innovations.

3. Unpredictable market

Big data comes from a variety of sources, from traditional enterprise legacy applications and transactional systems to data generated by machines, mobile devices, web logs and social media. This makes predicting required capacity more difficult and inefficient. A single event can cause sudden changes in data volume and workload. For example, a financial services institution may experience a 10-fold fluctuation in volume on any given day, and the exact fluctuation depends on market conditions and is difficult to predict.

One in four businesses is challenged by Big Data’s growing demand for storage capacity/infrastructure. Not only do enterprises need to plan their infrastructure, they must also determine how to easily scale to meet changing storage and computing requirements. It would be extremely inefficient and cost-effective for almost any enterprise to scale its infrastructure capacity 10 times to support peak demand, only to have that extra capacity sit idle 90% of the time. Other issues include rising infrastructure and maintenance costs due to data growth, the need for experimentation to ensure sufficient bandwidth to support innovation, and the cost of data acquisition and analysis.

With cloud services, enterprises do not need to size their infrastructure for maximum capacity. Its elastic properties allow enterprises to dynamically scale infrastructure up or down as needed.

4. Security and compliance challenges

As enterprises collect, store and analyze more and more data information from new and existing sources, the security of data becomes more of a concern. Nearly 35% of respondents were unsure or did not believe that their organization's existing security solutions and products provided adequate data security. Enterprises are working hard to control data access, protect data assets and protect infrastructure. Ultimately, enterprises need to decide how to ensure compliance, data management and security requirements without compromising agility and performance. For example, essentially all data created or used by financial services companies is regulated and may be sensitive or private data. Companies need to consider whether their financial information has strict management and compliance requirements.

Big data also means that your business’s information does not sit idle, this data is constantly being generated, processed and analyzed by multiple users and systems to obtain better business results. Even big data security challenges can be solved by choosing a vendor with strong data privacy protection and security controls. In fact, it's not uncommon for cloud services to be more secure than a company's own data centers. Since cloud service providers are providing robust computing infrastructure, it is in their best interest to maintain a secure environment. To this end, many cloud providers have accumulated best practices and experience from multiple enterprises and have the most stringent security requirements.

5. Create a business case

In many cases, IT departments need to create a business case for big data. According to IDG, enterprise IT leaders are more likely than non-IT leaders to be responsible for identifying business needs in terms of requirements and solutions. They need to recommend and select vendors, approve and authorize purchases, and sell solutions outside of the IT team. But leaders of corporate business units cannot stay out of the situation. IDG said 45% of respondents said their CEO was involved in the development and implementation of big data projects. CFOs and line-of-business executives are also increasingly playing key roles in big data projects.

If you haven’t built a solid business case and gathered input from powerful allies, such as key business stakeholders, then you likely won’t get approval for the resources needed for your big data project. In order to experiment with specific project initiatives, companies must do undifferentiated heavy lifting, which takes a lot of time and effort. This will undoubtedly slow down the pace of innovation and ultimately reduce the value of big data projects.

In many cases, the easiest way to demonstrate return on investment is to reduce total cost of ownership. Re-architecting existing workloads using cloud services can help companies significantly reduce costs. In addition, using cloud services can also accelerate the pace of innovation by reducing the cost of experimentation. Successful experiments will show measurable benefits and, once in place, will spark additional demand.

The right approach to cloud computing can help minimize or even eliminate some of the barriers to deploying big data applications. Like big data, cloud services are a highly disruptive force that is changing the way businesses operate and do business. And if the cloud and big data are combined, the impact will be even greater.

But deciding to adopt cloud services will not solve an enterprise’s big data problems overnight. Many cloud service providers only provide a part of the services that enterprises need. Enterprises still need to carry out a large number of integrations, which often face some trade-offs: price or scalability? Performance or ease of use? Flexibility, agility or security? Therefore, when evaluating cloud providers, enterprises need to look for solutions that directly address these challenges.

Skill shortage:

Your enterprise needs a wide range of capabilities to build, scale and securely deploy big data applications. These capabilities should cover all the different aspects of big data, from data collection to storage, analysis, and data visualization. Enterprises should look for a cloud provider that offers managed services that minimize management overhead and are fully compatible with the wide range of technologies in big data. This will allow your business to make the most of the skills you have and get help.

Cost and Business Case Development:

Enterprises migrating to cloud services will eliminate the need to purchase and maintain hardware. To help build the business case, choose a provider that can help lower TCO. Flexible pricing models: From Reserved Instance to On-demand Instance, and even Spot Instance can provide huge savings opportunities and reduce the cost structure of managing and processing data.

Unpredictability of data:

Your enterprise's cloud service provider should allow you to quickly and easily scale up or down in response to changes in demand. For example, decoupling storage from compute capacity allows companies to choose only the type and size of resources they need and pay only for what they use.

safety:

Look for a cloud computing infrastructure that is designed to be secure and frequently audited for compliance with various industry standards. Make sure your cloud provider offers audit-friendly services and compliance programs to help your business meet security and governance requirements. And make sure the provider offers data encryption at rest and in transit for all services, as well as a wide range of data encryption options.

The nature of cloud services makes them ideally suited for big data. Due to the scalability, elasticity and economic model of cloud computing, enterprises can scale as needed without having to build and invest in an environment with peak capacity. Cloud computing enables businesses to reduce costs associated with heavy lifting and reinvest the savings in projects that provide value to the business. Measurable savings will help secure additional sponsors, and these savings can be used to fund other big data projects.

Looking to the future, big data will play an increasingly important role in helping enterprises make smarter and faster business decisions. But businesses don’t have to be held back by skills shortages, limited costs, unpredictability of data, security concerns or difficulties in creating a business case. Cloud services can address many of these requirements. It enables enterprises to iterate on big data analytics and focus on business needs without worrying about the IT infrastructure required to collect, store and process big data. With solutions provided by cloud service providers, enterprises can analyze data faster and at a lower cost to achieve business goals faster.

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