Cloud Computing Big Data

Meta description: To support the business success with effective data analytics is a big question to all entrepreneurs. Our article will give you the exact answer.

Introduction

Today, most of the businesses are aware of the importance of data analytics, nearly 90% of the business leaders think it will change their businesses in a right direction to success.


Most of the investors are purchasing Big Data projects to gain competitive advantages in developing customer relationships, redefining product development, and changing the way the business operation.

However, to make an effective investment in data analytics is not just easy like that. If your goal is to apply data analysis to your business, you should have a clear understanding of Big data and data analytics to make use of them.

After having a good understanding of these essential things, it’s time for you to apply them in running your business effectively. There are various tips that you wish you knew before to drive your company in the right way to gain the achievements.

Use data analytics to support the business success

Making use of data analytics is never an easy issue to the entrepreneurs, especially the leaders. Here are some suggested ways for you.

Always be ready to attract the customers 

The very first step in exploiting the effective data analytics to improve the customer loyalty is to identify the business model of your company. Two main formats include registered and non-registered members.

Membership who attended as the registered members can be easy to identify as a target customer group. For the unregistered business model, the entrepreneurs need to have the plans to identify the target customers among them regularly.

Ways to attract the loyal customers include customer services to such as offering special offers through phone care, phone applications, or sending discount offers.

Control or evaluate the customer satisfaction through customer surveys in the business websites, poles or questionnaires. Moreover, you should have long-term plans to reduce the obstacles for the customers in the easier purchasing process.

Analyze the suitability of the products or services with the needs of customers to understand what the customers are interested in [1]. This tip will help you find the right path to adjust and produce the amazing products that fit their needs and tastes.

Understand the decision-making process

While your ideas and emotions are easy to change, data analytics is the basis for empowering the leaders to make rational decisions. However, a business does not have to need data analytics to be perfect and successful.

It is extremely crucial to learn to ask the correct questions and then, get into the problems well to find the right solutions that can be supported by effective data analytics.

The simplest way to identify the right problems and questions is to identify the most important decisions of a leader. Based on the available data, you can analyze the wrong operation in the company to adjust the new plans for your business.

Understand the wrong strategies in business can help an entrepreneur create the more careful plans for the next projects and a more suitable rational business operation.

Never complicate the data analysis

A talented data scientist will not handle every business problems. They know clearly how to choose and focus only on the main issues that influence greatly on the value of the business and think of the effective solutions for that problems.

Think simply on the data analytics to support the successful activities of a business. The key point is to simplify the analysis to make those data usable and provide full advantages for you to plan for the right way of the business management.

Find the relevant factors

To solve a problem of a business, it is essential to find out the dependent variables to solve the problem and analyze the independent variables.

Independent variables are not always clear while doing any researches for data analyzing. The initial requirements of a data analysis project can cause the misidentification of the business goals or business metrics to be evaluated well.

For example, if the directors asked you to increase the number of customers, you should understand that the leaders want to increase the total revenue. And, the rate of return is the main indicator that the director cares about.

Therefore, to find the important index that needs for the data analysis, a discussion with all the related people of the project should be organized. During the discussion, try to identify the right things concerned by the business leaders.

Set suitable standards

Set the standards that help to show team effectiveness and the goals for the whole team to work on. To do this, you need to collect the old data, internal and external impacts to find out an accurate standard.

First, you need to search for the current benchmark of the peer companies to compare yours with that one. Combine it with the information of your business resources to get an overview of your business effectiveness to others.

Thanks to that activities, you can set the most suitable standards that fit your company.

Make plans for periodic reports

Once you set up the measurable standards and objectives for your company, you need to plan your implementation for periodic reports to achieve the final goals. This activity supports the leaders in accessing the working effectiveness of individuals.

Periodic meetings allow you to have the right adjustment for the plans to help increase the efficiency of your work operation. Each team or department in the company with high effectiveness will boost the business success faster

Monthly reports illustrate the whole performance of all departments not only helps all the staffs use the data more in their work but also uses data analytics more wisely to increase the team effectiveness.

Conclusion

Using data analytics to aim at success for your business is not very easy, but you can learn to improve it gradually. We hope that you can have the best methods to keep your business going on the right track with our suggestions.

Big data is becoming a rising trend in a huge number of industries, especially in the healthcare field. If you guys are healthcare data analytics who are struggling with raw data, this article will help you.

Introduction

Optimizing data analytics’ effectiveness and value isn’t an easy task for healthcare leaders because most of them don’t access to the proper tools. As an experienced and advanced healthcare data analyst, I used to deal with various difficulties and obstacles when managing and analyzing tons of data to give out certain useful results.


After a long time of working hard and researching continuously to look for a satisfactory answer, I assume that the root of my mistake was not using the suitable tools to analyze data and find out insights which could speed up care and process enhancement initiatives. Hence, in this article, I will show you four secrets that I sum up in many years to transform your raw data into meaningful results.

Key stages of transforming raw data

Before learning strategies to turn raw data into meaningful analytics, let’s go through some basic knowledge on 3 key stages that all data analytics have to understand thoroughly.

Data capture

Data capture is the most important stage and decides greatly on whether the output result is trustworthy or not. The way devices, people, and processes produce and capture data are in charge of the data’s appropriateness (did data analysts capture the right data?), ease of data extraction (whether data is picked up in an accessible way or not) and also discreteness (did they capture data in the proper format?).

Data provisioning

Analysts are in need of data from various source systems through the organization to generate meaningful insights. For instance, an analyst helping clinicians team on the quality improvement issue requires a load of data from numerous source systems: EMR data, cost data, patient satisfaction data as well as billing data.

Combining data manually and pulling them into one location, in a mutual format and making sure datasets are interacting with each other is impossible and highly time – consuming. Also, it makes data more liable to errors. There are more efficient and fast ways to gather data.

Data analysis

After capturing proper data and pulling it into the appropriate place, the data analysis begins.
Data quality evaluation: Data analytics have to take a lot of time and effort to evaluate the data. Plus, they have to note their way of evaluation in case they share their results with the audience.

Data discovery: It is another pivotal component of professional data analysis. Before answering a particular question, analysts tend to explore the data and to search for meaningful trends and phenomenon. From my observation and experience, acted – upon analyses accounts for at least 50 percent in the discovery process.

Interpretation: When it comes to analyzing data, interpretation step comes up to most people’s head, but in fact, it is the smallest step in the whole process (about the total time that analysts spend on it)
Presentation: This is also a vital step as a data analysis couldn’t be recognized and appraise highly if the analyst couldn’t explain the result in an easy – to – understand and simple way.

These three stages of analyzing data will drive improvements. However, it isn’t adequate to generate meaningful and sustainable healthcare analytics. It is equally important to concentrate on analyzing data, not just picking up and provisioning data.

Optimize your data analytics’ value with 4 simple ways

Empowering data analysts to furnish insights necessary to make value-added improvements:

A data warehouse

The most efficient way to encourage analysts to drive improvements is by carrying out an enterprise data warehouse (EDW). The EDW becomes a stop shop for data aggregation. Analysts could access into all data through the health system by the login.

Several people assume that EDW is waste as it can put data together manually on a basis. This may sound acceptable in theory, but in fact, EDW offers various critical attributes such as security, common linkable identifiers, metadata, and auditing.

Full access to a testing environment

Keeping a tight rein on analysts’ access to EDW can vastly restrict their effectiveness. Give analysts plentiful opportunity to build and rebuild data sets. Analysts should be proficient in using the data warehouse in which they could store everything they consider useful.

Data discovery tools

Data discovery tools are as important as business intelligence tools, make it easy and simple for analysts to investigate the data and search for meaningful oddities or trends. However, BI tools are adequate for depth data analysis. BI tools feature graphs and charts that help analysts understand what the data is expressing. But they are still important to help analysts drill into the data, find trends and useful correlations. The proper data discovery tool should make it possible for analysts to generate insightful and intertwined reports that drive system improvements.

Direction

Healthcare data analysts are in need of direction, not detailed step – by – step guidelines about what the reports contain. Detailed instructions lead to one-off reports which are linked to very exact requests. On the other hand, direction results in deeper and more useful insights that could solve problems and drive improvements. A high – quality report requires providing sufficient direction to keep the analysts on the right track and enough flexibility to boost analysts to ask and exploit more questions.

Providing analysts with the right direction, enough time to shed light on the problem and a forum to ask more detailed questions is also needed. The final product will be much better as it includes both what the requester initially required and extra insights when going deeper into the data – which could be exactly what the requester needs.

Conclusion

In conclusion, analyzing data is not an easy task, and there is a lot of knowledge that analysts have to put their effort and time into. So keep calm and learning, I’m pretty sure that you guys will be excellent data analytics in the near future.

As the cloud goes mainstream, the technology will become crucial to executives handling projects powered by analytics. Besides the ongoing issues of scale, speed, and cost squarely addressed by cloud-based big data analytics, their new issues around privacy, latency, and veracity are also things to take into consideration. Let’s get this out of the way: you need to move your data analytics to the cloud. The business case for moving your company's information technology (IT) applications, including data analytics to the cloud, is compelling but it is not as easy as you might expect. Here’s why.

Reasons to move your data analytics to the cloud

When compared to building or expanding an in-house analytics infrastructure, migrating analytics workloads is relatively fast but unfortunately unquestionably cost-efficient and also carries risks of delays, service interruptions, and project failures.

Firstly, the transfer of data from enterprises to the public cloud is a bigger chore than what you might expect. If you think that it is possible to transfer data to the cloud at a push of a button, then you might have got a mistake. The real story is somewhat of a surprise. The cloud lets you weigh the pros and cons of a system without committing the resources, however, depending on how you use cloud services and the cloud providers themselves, the cloud services might provide great performance or not perform well at all.

Containing not only the database itself but also metadata which defines and describes the data and relationships between tables in the database, a database system is reasonably referred to as self-describing. Do you think that it is easy to go through the processes to move several petabytes of data?
Second, data integration is still an issue in the cloud; moving the data does not magically solve all of your integration challenges. Also, a big question for many chief information officers—and perhaps the biggest barrier to making the move: Can our company’s data be secure in the cloud?

Whilst some public cloud providers are starting to provide customers —your company with more data ownership and control over their data by providing ample tools and guidance to help you secure and manage the data, the information stored in the cloud is often not within an organization’s control. Thus, a lot of IT executives consider moving workloads into the cloud a potential loss of control.

A lot of IT executives consider moving workloads into the cloud a potential loss of control

Instead, it is the security practices of third parties that completely control the integrity of your data. Unfortunately, access to cloud storage can also be affected by bouts of unpredictable data throughput over the internet. This is out of your control and impossible to guarantee. The best practices may not always be applied to your organization’s security.

It does not make it inherently secure to move your data to the cloud. In order to protect your data, you must use your existing infrastructure as well as the tools and processes established by the cloud providers. Facilities, infrastructure, and architectures that employ some of the world’s most sophisticated physical and virtual security measures have been built by the top cloud service providers. It cannot be denied that cloud providers are experts at running large-scale data center operations, but it does not mean that they will never suffer equipment failures and other outages.

In addition, with the massive threat of cybercrime, the data breaches becoming more commonplace, and especially the average cost of a breach now a massive $4 million in 2018, who dare to consider public cloud security threats an afterthought?

Last but not least, cloud-native businesses and startups take advantage of the public cloud storage to achieve an incredible amount of data storage capacity and advanced storage management capabilities. But the cloud-based analytics databases themselves - the only solution that brings high-performance SQL analytics to big data - are complex and difficult to configure. Some of that complexity comes from the security subsystems in the database. They are, of course, good at what they do but there are always trade-offs and they should be figured out in the context of the database and data analytics. To launch you on the path that works best for your company, it is advised to understand the qualifications, migration patterns, key decision points, best practices and tools that are essential for success.

While the advantages of the premise-based IT software and tools are popularly recognized, cloud-based applications, which offer more connectivity and functionalities than legacy systems, are the global trend. Different companies might have different needs, but they all have to face common challenges on planning, scoping, and executing an analytics migration to the cloud. Why are data-driven decisions now an essential, daily, and valuable activity for all types of business people — not just marketers, data analysts, and IT architects? It is due to the emergence of cloud, the abundance of data, and the advances in analytics. And as controlling a big data is a complex, tricky thing, propelling the pairing of the cloud with big data requires companies to learn to master some key steps.

What do you know about data analytics? Can we move it to cloud easily? My today article will answer these questions.


It is obvious that data analytics seems to be a familiar technological term to many IT users. This method brings a lot of benefits for us in the modern society. Some people think that data analytics can be moved easily to the cloud system without encountering any hindrance. However, this is a wrong thought. My today article will help you insight into this issue.

Benefits of data analytics

Undoubtedly, the way that our data was analyzed in previous times did not attract attention of IT users as it caused many difficulties, wasted time and money, ineffectiveness, and high cost.

Nowadays everything related to data analytics has changed profoundly; it gradually becomes a helpful tool in the modern technology. Data analytics based on cloud system has the ability to make all things in a real time.

Therefore, at present many companies and corporations even small and medium size ones also take advantage of data analytics. They focus on using the prediction ability as well as machine learning mechanism.

Why moving data analytics to cloud is not easy

Some IT users think that when data analytics is applied successfully, its way to the cloud system will certainly become easier and faster. However, it is not true. This path requires a long process and time.

Even in some cases, it is impossible to happen. In other words, it is quite difficult to move data analytics to the cloud system.

Consequences of this problem vary in different fields. Technology may not fill the user’s expectation and data can reveal some unexpected issues. Thus, you need to understand why the way to transfer data analytics to the cloud system is so difficult.

There are four main reasons explaining why the way to move data analytics to the cloud system is not easy.

The first reason is transferring data analytics to cloud is an overwhelming task that requires a lot of power than we normally think. To deal with this problem, some large companies have adopted their own ways. Nonetheless, going through this cumbersome process seems to be a burning question to many people.

Another plausible reason for the above issue is data integration. In reality, transferring your data analytics to the cloud system does not solve all problems related to integration issues.

Besides, systems in your computers may still exist on the premises. This indicates that when you try to move the data analytics to the cloud system, it will result a bad situation where old and new transferring cannot converge together. This undeniably causes many negative effects on the structure and transformation.

As we know, data analytics which is based on cloud system is very complicated. To some extent, it is really difficult to move data analytics to the cloud system. The security mechanism inside cloud system is also very complex.

Link:
Why Is Your Business Data Treated With Such Little Regard?
NoSQL Database In The Modern Technology
Big Data Security Is Heading Toward Security Breaches

Cost also creates a certain hindrance to the path of data analytics to the cloud system. Of course, you need to take this point into consideration carefully before deciding to implement it.

According to a recent research of the Forecaster in 2017, you you want to transfer data analytics to the cloud system with a huge amount, you have to pay more or less 1 million US dollars. Obviously, this is a too high cost for IT users. Therefore, it is not easy for IT users to transfer data analytics to cloud in an easy way.

Additionally, this work also is involved with restructuring the organization inside the cloud system. To do this, you have to spend a lot of time and money also.

Competitiveness is another obstacle to moving data analytics to the cloud system. This work requires a team of IT professionals to cooperate together. Of course, companies have to pay high salaries for them to fulfill this task.

Conclusion:

In general, data analytics has been more and more prevailing in our modern technology. It will certainly play an important part in this field. However, how to transfer data analytics to the data cloud seems to be a hard question to many IT users. This work is too difficult because it is involved with high cost, high competitiveness, complex security system, and other surrounding factors. I hope that my today article will help you understand why we have difficulty in moving data analytics to the cloud system.

What do you know about the cloud database? How to choose a right cloud database? Keep reading my today article.


As we know, cloud database has become more and more popular in the modern technology. However, I am sure that many users still do not understand the nature and benefits of cloud database as well as know how to choose a good cloud database. Therefore, in my today article, I will provide you helpful information about these issues, especially the way to pick a right cloud database.

2 main kinds of the cloud database

Nowadays there are two choices for you to make when you decide to pick a cloud database. You have to determine that your cloud database is run on cloud only or on premises.

In reality, this is a difficult question for many users since each option has its own strengths and weaknesses that you need to take into consideration. Both of the above choices are related to cost and effectiveness of the operation.

Many IT users prefer to take advantage of their old database system rather than setting up a new one. Nevertheless, there is surprising news to them as if their database is run on premises; it also can run on the cloud system as well.

At the meantime, some kinds of the database running on cloud only like AWS Redshift and AWS DynamoDB appear to be an alternative option for the database which just runs on the traditional system.

It is obvious that databases running on the cloud system only have superior capabilities in terms of cost and effectiveness. It can also solve some issues in a very fast way with high accuracy. Besides, the cost of applying the cloud database is also affordable. Therefore, it is a good choice for IT users at present times.

However, there is a certain disadvantage of the database running on the cloud system only. If you want to move information in your data back the premise mechanism, you have to change the data into premise system. To some extent, this can waste your time.

Of course, the benefits of both cloud databases which run on cloud system only and on-premises help IT users to save up their time and money as well. These two kinds of the cloud database also ensure that you do not need to overcome a period of converting the structure.

Criteria to choose a right a cloud database

Cost is one thing you should consider carefully before deciding to choose what kind of the cloud database you need. Apparently, it is not difficult to determine the real cost of the cloud database running on cloud only. Nonetheless, this is not easy to the cloud database running on premises.

Additionally, I think you also should focus on the management mechanism of the cloud database. There is no doubt that if the platform of your database is not easy to use, you may lose the ability to control it anytime. This is very dangerous to the whole controlling system as well as to your computers.

Link:
NoSQL Database In The Modern Technology
Big Data Analytics In The Global Market
Cloud Hosting And The New Generation of Data Storage

Security is another criterion that you have to consider when deciding to pick up a cloud database. Undoubtedly, no one wants his or her system to be destroyed by virus or outside attacks. Thus, you need to ask the provider about this feature carefully.

Now it is time for you to search and find well-known database providers on the present market. In fact, there are various kinds of database providers which are different from services and management. If having a good service from the provider, you will have more chances to enhance the management and save up your money and time.

Searching for famous database providers will help you have more choices before deciding to pick up a cloud database. Remember that if you want to concentrate on the cloud database that is good at management or easy to use, you should only choose a good product for these points. Do not waste time seeing other irrelevant products.

I highly recommend you to choose some types of open database such as Apache Cassandra, CouchDB, MongoDB, HBase, and Hypertable.

Conclusion:

General speaking, the cloud database has been more and more prevalent in recent years. If IT users can apply it successfully, the cloud database will bring many benefits. However, before deciding to choose a cloud database, it is necessary for you to know some criteria to pick up a right cloud database. I hope that my today article can help you understand more about this.

What do you know about big data analytics? Why is it important in the world market? Keep reading this article.

Introduction

It is undeniable that public clouds will become a promising method for analyzing a huge amount of data in the near future. Applying it in today technology ensures a concrete platform for the value. Admittedly, the work of analyzing big data at present time is so far different from that of previous days. Many specialists do hope that there will be a significant change in this field in coming years. I will help you understand more about this issue in my today article.

A significant change of big data analytics

In a recent report conducted by the Global Industry, the analytics of big data in 2017 tended to increase by more or less 25% compared with the previous year. This figure shocks a lot of people as it happened faster than others predict.


Furthermore, many companies and enterprises are investing their money in analyzing big data. Wikibon has just predicted that there will be an 11% growth of global data analytics in 2027. This number can peak at around 103 billion US dollars at the global level as well. Some specialists strongly believe that the global market may be kept stable thanks to the application of big data analytics.

There is no doubt that a big shift will happen in the upcoming decade. In this field, the public cloud is expanding its influence in the world big data analytics. Nowadays, it appears that 3 main big data analytics providers including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Their revenue and fame have pervaded to all corners of the world in recent years.

These large providers are changing their own organization and enhance the technology by applying more and more updated applications. They can account for roughly 60% of global big data market in the future.

Additionally, benefits that public cloud brings for users apparently overtake those that private one does. The reasons for this are public clouds always updating their systems and they also solve all problems in a quick way and exactly.

Link:
Big Data Revenues Hit $46 Billion In 2016
Why Is Your Business Data Treated With Such Little Regard?
Big Data Vs The Cloud

At present times, users prefer to use multivendor big data analytics rather than traditional methods in some ecosystems. In fact, they are applying superior features of big data analytics to enhance the effectiveness and avoid commercial risks on the global market.

Databases also are being fixed to be suitable for new applications. Of course, the old form of database in previous times seems to be out of date. And, it can become a menace to our systems. To avoid being affected negatively, big data analytics providers are experimenting new applications.

In some cases, vendors also implement feasible approaches to deal with this weak point. They try to restructure principal databases’ abilities. If successfully, the data analytics will open more new opportunities.

Challenges to big data analytics

Even though a noticeable change in global data analytics in the coming decade, there are also some challenges to this field.

One of the biggest hindrances to global data analytics is complexity. It is undeniable that the nature of data analytics and its applications seem to be still very complicated. This creates a long process for resolving stemming issues. Therefore, vendors have to concentrate on simple applications such as interface, architecture, and other essential elements.

Another weakness of global data analytics is that this filed has a cumbersome process. To some extent, it is still expensive, inconvenient, and ineffective. Thus, vendors should have their own solutions to reduce some difficulties.

Long time for pipelines also is a certain obstacle for the global big data analytics. There is no doubt that this service is important for current trends in the modern global market. However, the way that it analyzes big data wastes a lot of time.

Wikibon highly recommends users to take advantage of public clouds in analyzing big data. This will certainly help you ease some problems of global big data analytics and save up your time as well as money as much as possible.

Conclusion:

By and large, global big data analytics has been changing so fast in recent years. It is foreseen that in the near future, this area will account for a large proportion of the world market. If you know how to take advantage of big data analytics, you can avoid some risks and difficulties of the global market. I do hope that my today article can help you insight into this field.