What is the value of Big Data in Health?

In general, Big Data applications tend to help in the design of protocols, optimization of resources, or actions related to demand prediction, which in the Health sector support hospital management:

• waiting list optimization
• patient segmentation
• needs prediction
• detection of patterns related to hyper-frequency
• detection of patterns related to hyper-prescription

And on the other hand, the actions of Big Data in Health are also oriented to support decision-making:

• early diagnosis of diseases
• treatment planning

When applying these techniques, it must always be considered that they are recommendation systems and that the person with the last word is the specialist, with whom we work with great respect since they are the experts in the socio-sanitary sector.

Big Data in Health Will reality meet expectations?

We cannot ensure that expectations will be met; we can assure that we have much potential. Patient care will be more efficient, and we will cover their needs better in less time.

There must be a data source to apply Big Data techniques, and there are huge amounts in the social and health sector. The data that can be collected in Health is heterogeneous, comes from different sources, and is compiled in different repositories, different silos. And I say silos because they are often isolated, and one of the functions of Big Data techniques is to communicate this information. And from there, the next step is to exploit it to respond to all these needs.

Is Big Data in Health more attractive for patients or for doctors?

We do not work with patients but rather support medical specialists and healthcare managers. The doctor and the expert have validated all the information we use.

It is also not advisable to work on Big Data in Health for the patient since they can try to use this type of means to be able to skip the doctor and opt for self-diagnosis, which a specialist must give.

Also Read : Big Data To Optimize Telecare

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In telecare, analyzing information using Big Data techniques helps to reveal situations that were not previously perceived, such as people at risk of dependency due to falls, common behaviors among patients, or once knowing the need for a service or when it will be demanded.

Advances in telecare services

The telecare service is aligned with other advances, such as new health procedures, food, and the use of technology, together with the social awareness of adopting healthy lifestyle habits to make our life expectancy longer and of higher quality and in good health. Thanks to these advances in society, the population’s life expectancy increases from year to year.

One of the great challenges that telecare faces is its sustainability. The public powers push them to adjust costs while providing the social and health care that society demands. This necessarily happens by optimizing your service. In this challenge, Big Data-based technologies have proven their usefulness.

Telecare relies on Big Data technology to better specify the conditions in which help is necessary and what that help consists of. In this way, it is possible to improve its provision, which is enormously vital for administrations and social services.

Big Data technology uses algorithms that allow the analysis of data from various sources that until now had not been used together, to which are added those obtained from social networks, helping, for example, to determine critical situations such as unwanted loneliness.

Improvement of services and personalization of telecare

The application of Big Data to telecare allows social services to develop new actions aimed at improving socio-health services and thus anticipate the needs of dependent people.

In addition, they allow the detection of new needs, issuing alerts that help control the evolution of diseases in services patients such as telecare, which is one of the most demanded services according to the State Observatory of the Dependency.

The increase in personalization that the application of Big Data technology will bring to telecare will make it possible to ensure that the costs of care are directed toward the right people and in the right way. This supposes the optimization and improvement in the provision of services.

Therefore, Big Data and Artificial Intelligence are useful technologies in the healthcare sector. The application of predictive models based on the information provided by the data is a great advance, both for clinical management in treatment and patient care and for optimizing times and resources in healthcare with the possibility of alerting and predicting needs and recommending actions.

Also Read : Big Data Is Increasingly Used For Competitor Analysis

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Data that measure user experience

In the Retail sector, a purchased ticket is a treasure. It constitutes one of the most essential data inputs available to the business. It is an indicator of the number of customers and sales volume, but also of the user experience.

Analyzing the data of a purchased ticket provides the opportunity to make personalized recommendations based on previous purchases or inquiries from the customer himself or from customers who have made similar purchases. Likewise, knowing the type of details included in the purchase receipts is also essential to develop a purchasing assistant (or chat box) that promotes online commerce.

The ticket, as an example of the data-centric approach, evidences an act of purchase but also an event on which a personalized coupon can be generated, a signal to personalize online marketing, an opportunity to feed an algorithm that detects anomalies at checkout, a proxy to control the stock in real-time, etc.

Process optimization in the retail sector

Sales prediction models, personalized customer segmentation, abandonment prediction, and fraud detection are some of the practical applications of Big Data and Machine Learning techniques that can optimize processes and boost the Retail sector.

When the business handles data from millions of products (low-turnover, high-turnover, mass consumption, fresh products…), segmented by sections, the smart thing to do is to use it to improve service by optimizing processes. They can be used to detect internal fraud, crossing them, for example, with weather data to improve sales forecasting, demand planning, or customer profiling.

Analyzing the data and applying techniques such as the decision tree or random forests can help the Retail sector to find out how much more of a low turnover product.

Thanks to the data, we can analyze customer behavior and establish levels of internal fraud to understand what happens at the cash desk and what these events translate into. This same analysis and the use of neural networks would also allow for personalized segmentation using a model of propensities or recommendations, allowing us to establish similarities between purchases to know what I can recommend to my clients.

Retail, Big Data, and geolocation

In a time of change that seems more like a change of times, it is essential to find out how things are changing, where trends are moving, and how customers are evolving. For this, we can apply geolocation techniques and data analysis or Big Data to the Retail sector.

Geolocation can help segment stores, optimize routes, or plan new openings. The seminar presented examples where geolocation techniques and Big Data have helped the Retail sector to find out where it would be best to open a new store, what opening hours are the most productive, what type of purchase is the most common in a given area or how to moves people in and out of my store.

Data Science to Boost the Retail Sector

The basic elements were presented to understand how to translate the different indicators and the many variables existing in the sector so that the business understands them.

The ecosystem of existing tools and the need to develop customized solutions to find a way to make algorithms profitable was discussed. In addition, opinions were shared on why having multidisciplinary teams, how to retrain models to refine the results, and how we can control the data and its security to improve management in the retail sector.

Also Read : The 6 Basic Principles Of An E-commerce Marketing Automation Program

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Today the information to analyze is more and more numerous, and it often becomes complicated to make the most of it all.

Since companies have recognized the importance of Big Data, a problem has arisen regarding the use of these data streams.

Today’s emphasis is on how they have evolved and how you can do competitor analysis with Big Data.

The evolution of Big Data in recent years

The study of Big Data is quickly becoming the starting point for market analysis and the consequent growth of individual companies.

Data has always been taken only as a consequence of Digital Transformation, but today, these same data, increasingly precise and voluminous, represent a very important asset for companies from an economic point of view.

Therefore, every company must focus on Big Data if it wants to become competitive in the market and outperform the competition.

How important is competitor analysis?

Whatever market you want to enter, you cannot hope that this is without competitors.

The biggest mistake you could make would be to ignore them.

The moment you enter a market, you need to know exactly who the players are: this will allow you to set up your next strategies. This is why competitor analysis with Big Data is so important.

How to take advantage of Big Data to obtain information

Big Data can be leveraged in many ways for competitor analysis. First of all, Big Data solutions allow you to take information and make it easy to read.

Furthermore, as data is collected, the information becomes more and more accurate, allowing you to segment customers and products.

This kind of analysis is used to improve one’s strategies in light of data that otherwise would not have emerged.

Ultimately, what we learn from Big Data allows us to improve the production of products or services.

Monitor the position of competitors with Big Data

Competitor analysis is essential for any type of business. In fact, business decisions are taken, for the most part, following an analysis of the market and competitors.

What Big Data does is provide an innovative tool to monitor your company’s position in the market compared to competitors. This is a significant step forward compared to traditional systems.

The competitors’ actions are analyzed through algorithms that study the method, approach, and graphic form with which they are shown on the web.

Why prefer Big Data to traditional methods of analysis

Big Data can be exploited by companies in a much more productive way than was done with traditional techniques.

First, those who invest in Big Data must focus on predictive analytics, i.e., intuitive information based on the probability of a future outcome.

In addition, they allow for continuous learning. This means that the growth of Big Data is moving towards deep learning, which aims to create artificial neural networks that can discover new Big Data patterns.

Now let’s see how all this is possible.

Real-time monitoring

Companies that sell products or services have already started using data to segment customers some time ago.

Today, Big Data allows personalization in real-time, and this is a great revolution compared to the past.

If before, the company had to collect the data and then develop the strategy, today, behavior tracking takes place instantly. This allows us to model probable behaviour as well.

Lower costs

Thanks to the automation of Big Data, the appropriate tools take care of reading the data that is sent from different sources.

After that, these are analysed by groups of computers, which send results, always in real-time, to the company’s operations centres that optimize production.

This process not only increases productivity but significantly reduces personnel and operating costs.

More precise information

Thanks to the diffusion of Big Data, new types of companies are emerging that base their business model on the most precise information they are able to collect.

This is possible thanks to the capillary segmentation of customers, capable of bringing more competitive products or services.

Conclusion

Competitor analysis with Big Data should be gradually adopted by companies that want to make the most of the large amount of data that passes through their organization.

If you want to receive advice on applying Big Data technologies in your company, contact us using the form on the page. Our experts will be happy to guide you into the future.

Also Read : Business Intelligence Solutions: Here Are The Most Sought After

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This is a job that typically requires some experience in programming, statistics, math, social science, psychology or economics. Data analysts may be required to have knowledge in SQL databases as well as good communication skills. Data analysts can be paid well and work in a variety of industries.

For example, if you want to become a Data Analyst then we recommend that you learn Python programming because it is one of the most demanded CA fake ids languages by companies such as Pinterest or RStudio. Other skills like HTML/CSS will also come in handy so make sure to include them on your Data Analyst resume.

If you want to make Data Analysis for your company, then it is important that you have a Data Analytics degree because this will help give potential employers more confidence in hiring you for the job. A Data Analytics degree can be obtained from many schools around the world including Stanford University. If data analysis isn’t available at your school, then you can study Data Science or Data Engineering.

If you want to become a Data Analyst, make sure that your resume looks professional and has all the relevant information needed for employers like company names, positions held at these companies etc… Make sure also to include on your Data Analyst Resume what programming languages are more important in this role such as Python, SQL or Java. Data Analysts usually work in the offices so make sure to include this information as well on your Data Analyst Resume.

These are some of the most important Data Analyst skills that you should mention if you want to get hired for Data Analysis jobs . You can also list more specific data analyst skills like “data processing” or “data analysis” if you want. Data Analyst jobs can be found on many job sites so make sure to search for Data Analyst Jobs and apply to the ones that interest you.

If there are any data analyst positions open at your company, then it is important that Data Analysts have good communication skills because they’ll need them in order to communicate with Data Scientists and Data Engineers. Data Analyst candidates should also have good math skills because it is an important part of their job.

Data Analysts are responsible for many things in the company including making reports about data analysis, analyzing business trends, determining which strategy will bring more success to a product or service etc…

There are different kinds of Data Analyst jobs. Data Analysts can work in big companies like Pinterest or Apple but they can also work for small businesses. Data analysts usually have Data Analytics degrees because it is important to know how to analyze data and make reports out of them .
Data analyst positions are available on many job sites so you should definitely apply if you want a career as a Data Analyst.

As Data Analysts, it is important that you use good Data Science Skills in order to become successful at your Data Analytics jobs. Data Analysis involves a lot of different skills and knowledge so make sure to list them on your resume if you want to get hired for Data Analyst Jobs. You can also mention specific data analyst skills such as Data Processing, Data Visualization or Data Analysis.

Data Analyst Jobs are available on many job sites so make sure to apply for the ones that interest you.

Data Analyst jobs can be found in big companies like Apple but they can also work at small businesses. Data Analysts usually have Data Analytics degrees because it is important to know how to analyze data and make reports out of them. Data Analyst positions are available on many job sites so you should definitely apply if you want a career as a Data Analyst.

Also Read : Top 4 Benefits Of AI And Cloud Technology

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This occurs many times when companies have their data located in locally installed software, generally connected to your POS. If your computer system is not secure enough, the risk to your data is very high.

Given this, you have several options. We are going to ask you how you can do to protect your business data.

Ways To Protect Your Business Data

There are several ways to protect your business data. Let’s see the different methods you can use.

Go To The Cloud

One of the most popular solutions today is moving to the cloud. If you go to the cloud, all your data will be stored on the software provider’s servers. Therefore, you no longer have to take care of security at such a demanding level, since you do not have to have your own server.

This way, you save a lot of costs. But it is also true that you are in the hands of the provider with whom you have contracted and that to go from there to another provider you would have to do a migration, which can be complicated and expensive.

Protect Your Servers

The servers you have in your company need maintenance. If you have everything installed locally (your POS, your management software …), your servers need to be properly maintained, and measures are taken to protect the security of your data.

In this way, you will be able to prevent viruses from entering your systems, or computer attacks that may generate problems for you. To prevent any of these from happening, you can get a hand on IT Asset Lifecycle Management Services with XSi, and keep your assets safe.

Make Backup Copies

This goes for granted, whether you work with a cloud provider, or have your management software installed locally.

If you have automatic backups, you wouldn’t have to worry about losing data. You can always restore the information to the last day or even a few hours before so as not to lose any information.

Not all management programs provide you with automatic backups. The data and databases of your business can also be protected with an online backup system in the cloud.

Take Preventive Security Measures.

Unnecessary risks are often taken in business. For example, the same computer on which you have your management software installed and which is connected to your POS is also connected to the Internet and is the one you also use to print documents on the copier when a customer brings you a pen drive (very common in stationery and copy shops).

The result of this is that your main computer is taking enormous risks. You do not know what can be inside a pen-drive of someone you do not know, or even someone you know. Ideally, have everything separate. 

Have Antivirus And Firewall

Absolute computer security does not exist, and if someone promises you, it is most likely that they are deceiving you. But that does not mean that with a good antivirus and a firewall, you can protect your computer systems from possible attacks and viruses.

Having adequate protection will help your company to be protected against the possible risk of data loss.

This is vital especially in small businesses, SMEs and companies that have 2, 3 or 4 computers at most. If you have a server installed and you don’t use cloud programs, make sure that the data is protected, and the security tools are adequate.

Protect The security Of Your Company Data

We can help you with the computer maintenance of your company. We are a computer maintenance company in Madrid, and we provide you with the best conditions so that your data is protected and your equipment is safe.

We never know when a computer attack may occur, or you may have ransomware, and your computer is hijacked by a cyber criminal. In the face of all these types of problematic situations, before dealing with the cyber criminal, you should always contact a computer company to help you protect your data.

Contact us for more information and discover how your company can protect your business information in a professional way and to be calm.

Also Read : Tips to Keep in Mind For Your Next Business Meeting

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Big Data, Small Data, or data analysis, has carved a niche in many trades until it becomes a fundamental tool in our daily lives. But do we really know how to face all the challenges of Big Data and data analysis?You can learn more about Big Data by checking out ProjectPro Big Data Projects.

Undoubtedly 2019 has been a great year for Big Data  and will continue its development during 2020, new technologies have allowed the use of tools and strategies in this area to be consolidated, and we can see how Big Data Analytics is another practice of Business Intelligence and a differential value of the competition.

Big Data Trends And Analytics 2020

Before starting, I want to highlight a phrase from the Director of IoT and Digital Transformation at Cisco Spain, Antonio Conde, to understand the magnitude of value that Big Data has: “Data is ‘the new Technology,’ they are becoming a key part of society and economy.”  

This is so because the data is the new value to be managed by organizations of all kinds. Companies are looking for capabilities in terms of data capture, storage, and processing, and those that succeed will have achieved an advantage over their competition, which is called Analytical Advantage. Those companies that achieve the desired analytical advantages will be able to say, then yes, that they are true Data-Driven companies, companies focused on the value of the data.

Evolution Of Augmented Analytics

Many experts agree that 2020 will be the year in which Big Data Trends will achieve considerable technological evolution, but we still have a long way to go to see what Big Data Trends can do for us, and it will undoubtedly be a change in the economic and social context.

With augmented analytics, we will see the appearance of more important knowledge or changes that will help businesses optimize decision-making.

Augmented analytics makes insights available to all company profiles, and is justified in terms of the need for expert data analysis profiles and the limited talent available. So while reducing the reliance on analytics, data science, and machine learning experts, it will require increased data literacy across the organization.

By 2020, augmented analytics will be a dominant driver of new technology tools that make the data analytics process itself simple and accessible to much of an organization’s profiles. These tools will try to democratize data science and machine learning to an entire organization.

 We will undoubtedly see how the rise of digital technologies, lower-cost data storage, high-performance hardware, and embedded software will fuel change in both large and small businesses.

The companies adopting the IA part of their business processes will be increased, and it is logical, since the advantages of this technology are at many different levels, for example, at the level of processes, creating new models, business, customer interaction, and even interaction between the own people of an organization.

Also Read : What Is 5G Technology And How Will It Change Our Lives?

Machine Learning

Machine Learning or automatic learning is another trend in 2020 that will continue to grow and will do its usefulness for all types of companies and sectors.

When we talk about Machine Learning, we refer to algorithms’ ability to learn and improve their actions autonomously. The more we execute the algorithm, the better it will meet its objectives. Ted Dunning, MapR’s Chief Applications Architect, says in this regard: “More and more companies are treating computing in terms of data streams rather than data that is only processed and stored in a database. These data streams capture key events and reflect the business structure. A unified data fabric will be the foundation for building these large-scale flow systems. “

Increased Data Management

With data growing exponentially, organizations need to automate data administration and management tasks. Vendors are adding machine learning and artificial intelligence (AI) to make self-configured data management processes so data professionals can focus on higher-value tasks.

This trend is affecting all categories of enterprise data management, including data quality, metadata management, master data management, data integration, and databases.

Data Culture

Organizations do not yet have a data culture. We are not used to working on a database yet. This year we will see how organizations will be more concerned with informing workers to understand the data culture.

In this regard, you cannot create a data-driven company but invest first in instilling the data culture among the teams. You must learn to generate data, but you have not yet learned to use it in decision making.

Hybrid Clouds Will Rise Like Foam.

Hybrid clouds have become very popular in the past year, as a tool that enables companies to store data securely. During 2020, the use of this technology is expected to increase significantly among companies. The benefits of hybrid clouds are that you can transfer back and forth between local (private) clouds and IaaaS (public) clouds, allowing for greater flexibility.

There will be data that will need to be uploaded to a public cloud, but nevertheless, there will be others that can be managed in local environments without having to be transferred, with the problems that this generates in terms of security and protection of the same.

Data Storytelling

This will influence both personal and business decisions. In addition, the Data Storytelling profile may gain importance next year.

The data, if they do not tell stories, are useless. The data must count, if a piece of information doesn’t tell you anything, you have to look for the story behind it, what can it do for.

Increased Use Of Virtual Assistants

2020 promises to bring considerable changes in the way we interact with companies through messaging apps like WhatsApp. But, above all, it poses the settlement of voice interfaces thanks to the growth in the use of smart speakers and virtual assistants it has experienced throughout 2019. 

Chatbot Chocolate CEO Angel Hernandez affirms that “we are experiencing first-hand this increase in interest in settlement of both text and voice interfaces by companies.”

For this reason, the expert believes, the rise of conversational technology becomes one of the indisputable trends of the next year, both in-text interfaces such as WhatsApp and voice interfaces with virtual assistants.

“These channels allow us to interact with our current and potential consumers in a simple and automated way, or with mixed service models in the case of WhatsApp,” adds Hernandez.

In this sense, and after a “timid introduction” of the WhatsApp Business API in 2019 and the arrival of the first cases of use of well-known brands such as Iberia or Atletico de Madrid, 2020 will be the first year in which the use of this channel between corporate’s. At the same time, Amazon, with Alexa, followed by Google with Google Assistant, will continue to surprise companies and consumers by offering more and more services through this new channel in a phase of rapid adoption by users.

Natural Language Processing (NLP) And Conversational Analysis

Just like search interfaces like Google made the Internet accessible to everyday consumers, NLPs provide an easier way to ask questions about data and get more accurate information. Conversational analytics takes the concept of NLP one step further by allowing such questions to be asked and answered verbally rather than through text.

According to Gartner, “By 2021 NLP and conversational analytics will drive the adoption of analytics and business intelligence from 35% of employees to more than 50%, including new classes of users.”

The voice seems to succeed in establishing itself as the main channel of interaction between machines and people, and in a way, it makes perfect sense. Technology must be transparent to people in its use, and therefore it must be natural, and what is more natural than using the voice to communicate? This technology is in very immature phases, but it is only a matter of time.

Big Data Ecosystem

The need for companies to perform data analysis will foster technological innovation (IT) within it while integrating into the Big Data ecosystem. These advances will be mandatory for all companies that do not want to be left behind in the market. Therefore, the Business Intelligence model will begin to be a reality from large multinationals to small startups.

The Big Data ecosystem offers great machine learning capabilities that will include exhaustive calculation, artificial intelligence, and graphical algorithms. Another benefit of this ecosystem is that they will unify analytical technologies; therefore, there will be better compatibility of data types, and sources and any programming language can read them.

Big Data Trends Claims In 2020

In 2020 we can see all the advances that Big Data Trends has promised us this year. One of the priorities for companies is data and resource manegement integrating multiple base technologies.

Artificial Intelligence has been created to facilitate decision making through complex algorithms that will give multiple answers about the decisions to be made.

Data Strategy is the name of the strategy that companies are adopting to start their actions on data. You have to start with the strategy and not with the data, that is, you have to have a vision of the future about what companies want to become, and you have to ask yourself the right questions, about which we have no answers and that the data can help get them.

Another business demand is that Big Data be able to anticipate the needs and demands of consumers. Data collection opens up a wide range of possibilities in personalizing the product or service to the customer.

And finally, the Blockchain that is rapidly evolving, this technology offers a great capacity for transaction and record-keeping that can serve as the basis for other potential applications such as health, media distribution, Public Administration, and supply chain.

Big Data,The Most Demanded Profile

Currently, the professional expert in Big Data Trends has become one of the most demanding professional profiles, but it is also one of the most difficult to cover. This is because they represent 10.11% of the total, according to the EPYCE 2017 report: Most demanded Positions and Competencies. On the other hand, we also find difficulties for companies to fill the positions of computer engineers. These represent 5.85% and exceed the Big Data on-demand profile.

The study has also revealed that, alongside the demand for Big Data profiles, there are also factory operators with certain digital capabilities. Commercial profiles follow these; however, companies have trouble finding digital commercial profiles, and they will be increasingly in demand.

But the report not only includes the profiles most in-demand in Big Data  but also the skills most in-demand by companies. These are; initiative, teamwork ability, and flexibility. With regard to more senior profiles, those in charge of recruiting and selecting talent are looking for people with leadership skills. Another fundamental capacity and which we cannot forget are languages, especially English

Also Read : Big Data And Cloud Computing As A Starting Point In The Business Model

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But it also poses a new challenge: take advantage of the enormous volume of data, detect those that are useful from the great variety that exists, control them at the necessary speed, and have knowledge about their veracity. Surely you know what Big Data is, but do you know the 5 V’s of Big Data? We explain them to you!

Big Data, From Data To Valuable Information

Big Data is one of the fundamental keys to improving corporate governance. And it is that more data is generated in two days than in all our contemporary history. According to the consulting firm Gartner, in 2020, there will be more than 25 billion devices connected to the Internet, which suggests that the volume of data contained in Big Data will grow exponentially.

The digital transformation of structures, processes, and tools allows the Big Data environment to grow by leaps and bounds every day. Due to the rapid evolution of technology and the habits and behaviors of society, companies are now in need of collecting, managing, and analyzing large amounts of data that, thanks to Big Data, can convert into information.

A precious source of knowledge about clients, competitors, the environment, etc., with which you define better strategies, to achieve your objectives, and obtain competitive advantages.

The 5 V’s of Big Data 

Big Data is made up of five dimensions that characterize it, known as the 5 V’s of Big Data. Let’s see what each of these aspects consists of:

# 1 Volume

Traditionally, data has been generated manually. Now they come from machines or devices and are generated automatically, so the volume to be analyzed is massive. This feature of Big Data refers to the size of the amounts of data that are currently generated.

The figures are staggering. And it is that the data that is produced in the world for two days is equivalent to all that generated before 2003. These large volumes of data that are produced at any time pose significant technical and analytical challenges for the companies that manage them. 

# 2 Speed

The data flow is massive and constant. In the Big Data environment, data is generated and stored at unprecedented speed. This large volume causes data to become out of date quickly and to lose its value when new data appear.

Businesses, therefore, must react very quickly to collect, store, and process them. The challenge for the technology area is to store and manage large amounts of data that are continuously generated. The other fields must also work at high speed to convert that data into useful information before it loses its value.

# 3  Variety

The origin of the data is highly heterogeneous. They come from multiple supports, tools, and platforms: cameras, smartphones, cars, GPS systems, social networks, travel records, bank movements, etc. Unlike a few years ago, when the data that was stored was extracted, mainly, from spreadsheets and databases.

The data that is collected can come structured (they are easier to manage) or unstructured (in the form of documents, videos, emails, social networks, etc.). Depending on this differentiation, each type of information will be treated differently through specific tools. The essence of Big Data resides in, later, combining and configuring some data with others. 

Each type of information is treated differently, through specific tools, but then the essence of Big Data lies in combining and configuring some data with others. It is for this reason that the degree of complexity in the data storage and analysis processes increases.

# 4  Truthfulness

This feature of Big Data is probably the most challenging. The large volume of data generated can make us doubt the degree of integrity of all of them since the great variety of data causes many of them to arrive incomplete or incorrect.

This is due to multiple factors, for example, whether the data comes from different countries or if providers use varying formats. These data must be cleaned and analyzed, a continuous activity since new ones are continuously generated. Uncertainty regarding the veracity of the data may cause certain doubts about its quality and availability in the future. 

For this reason, companies must ensure that the data they are collecting is valid, that is, that it is adequate for the objectives that they intend to achieve with it.

# 5  Value

This characteristic represents the most relevant aspect of Big Data. The value generated by the data, once converted into information, can be considered an essential aspect. With this value, companies have the opportunity to make the most of the data to introduce improvements in their management, define more optimal strategies, obtain a clear competitive advantage, create personalized offers to customers, increase relations with the public, and much more.

To be aware of all the opportunities that can be extractedicle through the application of Big Data, it is necessary to understand what are the main elements that add value to it and make its implementation at the business level a safe bet. And you, where do you think the success of Big Data lies? Do not hesitate to comment and give us your opinion.

Related Article: Small Data: Big Data For SMEs

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What Is Small Data?

Before entering the definition of Small Data, it is essential to emphasize that Big Data gave the key to making smart and correct decisions. He minimized risks and, most importantly: thanks to him, we can predict consumer behavior and be at the exact moment when they need to satisfy a need.

If we Google the term “Big Data” we will find different types of concepts, in which we will see words such as: “massive”, “large scale”, “large data sets”, “huge amounts of data”, “Petabytes”, “Exabytes”, among others. 

Do you know the amount of information an Exabyte contains?

The answer is a trillion gigabytes, an unimaginable amount of data for us mere humans, but not for a machine. Large companies such as Google, for example, often talk about Petabytes and Exabytes of information very frequently, and it is normal for the amount of data they collect. On the other hand, if we lower the scale and start talking about SMEs, the common thing would be to speak about Gigabytes and Terabytes.

The needs of giants like Google increased over time, so at one point they considered what to do with so much data and how they could take advantage of it, leading them to understand that if they analyzed all the information they collected They were able to understand the market better and create customized strategies based on that data to meet demand better.

It sounds great what can be achieved with Big Data’s concept, but SMEs can be overwhelmed. They come to think that they do not have the necessary tools to obtain and organize this enormous amount of data. But let’s do a little exercise: let’s change the term from Big Data‘s to Small Data; the concept of the 5V’s fit correctly for an SME, the only thing that changes is the volume of data and the tools that we will use. 

Applying Small Data?

1. Collect data from different sources
2. Analyze the data obtained and give value to them
3. Interpret the data until you have a clear vision of who our customer is and what their needs are.
4. Design personalized strategies based on what we know about our consumers. Strategies that help us improve our processes, products, services, etc.

What Tools To Use In Small Data?

In the cloud, there are many tools to collect and analyze data efficiently and at a low cost or even for free. For example: 

• Google Analytics
• Heatmap
• Mailchimp
• SurveyMonkey
• Alexa
• Similarweb
• Adwords
• SEOSiteCheckup

All the above tools collect and analyze data, although we must choose them based on business needs. We must investigate them, find in them what works for us and discard those that will not be useful to us.

For retail businesses, for example, GFK is a company that is in charge of studying the market of major technology brands such as Sony, Samsung, LG, among others, and offers a market analysis and statistics service, free of charge, to change to be provided with a sales report on certain product categories with a specific format; They give you different options, and you choose the one that suits you best.

Small Data Specialized Tools

There are other specialized tools to integrate the data collected from different sources. These tools are of great help when the time comes to consolidate and analyze the information; you can do it from a single platform. 

Differences Between Small Data And Big Data

We can conclude that Big Data and Small Data treated the same, only varying the amount of data and infrastructure necessary for the treatment thereof, being synonymous with the Business Intelligence, and this is where comes a specific phrase: l or not Measure cannot be improved, if you are an SME, it is time to start taking this concept seriously and creating an action plan for its implementation.

And if you want to train and be a real Big Data’s expert to differentiate your professional profile. You can become a real data scientist!

Related Article : Big Data And Cloud Computing As A Starting Point In The Business Model

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Cloud Computing The Starting Point

By sector, companies specialized in Marketing and Big Data / Analytics occupy a privileged position in the development of information systems to improve sales, access to the analysis of large amounts of data, and the interpretation of data through the allocation of a series of values.

Among the evaluations, the workers and employees highlight the company’s management, the value of its CEO, and the environment created to get the tasks going, regardless of the future of the solutions described. In the new work environment, flexibility, dynamism, and confidence in taking responsibility have led to the decentralization of day-to-day work and the consolidation of a smooth and efficient collaborative online work system.

A clear example is Asana, project management and communication tool for workgroups that occupies the first position highlighting the capacity of its leader, Dustin Moskovitz.

Cloud Computing And The Customer Experience

In the values ​​of a company we always identify a brand, the value proposition and design of a service or product with a specific path in a local, national and international context, but also the satisfaction of the people who are within the organization, since You can become prescribers within the new business model that has embraced technology trends to reach other markets. On the other hand, when a client searches for a Big Data, Analytics, or Business Intelligence (BI) solution, the first thing they look at are the following characteristics:

• Scalability. Analysis of large amounts of data (terabytes) according to volume and speed, as well as the multitude of information sources at origin and destination.

• Users. Ability to scale the same benefits of the tool to groups of people who are working in an organization, with the possibility of gradually incorporating more depending on access and connectivity needs. The visualization of the data and the response time is key for business managers to make the appropriate decisions based on the evolution of the information displayed in the viewer.
• Integration. When a client searches for Big Data, Analytics –already created– or Business Intelligence (BI) solution, they want to know what type of widget can be incorporated, embedded, or integrated to access a higher volume of information. Each company has its own system, although since the advent of Cloud Computing systems there are organizations that have preferred the “all-in-one” to “leave behind” the traditional and create, customize and design their own work cloud in an environment secure under the disposition of as much data as possible.
• Customization. Both the client (outside) and the worker (inside) want to personally access their information, data and be able to export what they are viewing to share with other users or people within a group. In Business Intelligence (BI), the concept of API access and the design of Javascript libraries is one of the most important keys to adapt to the company that demands a specific BI or Analytics service.
• Customer service. One theme is being a “creator,” and another is providing a service, so attention is one of the high values ​​in the new design of business models based on Cloud Computing that offers Big Data solutions through a platform.
• Security. The protection of data, information, documentation, and communications is part of the challenges when we request a quote from a cloud expert company. In addition, those responsible have to adapt to the privacy of the data, the communication protocol, and the way of reaching the worker to explain the new work environment. We are talking about solutions that are adapted to all devices with access to the network, regardless of the operating system and version. Information leaks are analyzed in detail.
• Value of an open platform. Access to space where programming, creating, personalizing, and executing actions represents the interest of the organization in continuing to work on the contracted solution, but analyzing the value proposition to adapt it to the organization from the horizontal perspective.
• Adaptability. The worker’s user-level since accessing the tool, system, or platform is not the same. The configuration possibilities are multiple. The programming or development environment is not the same for all employees in a structure, because each department has to analyze its data and draw conclusions.
• Minimum cost in Hardware. The arrival to access and personalization of data through the integration of a BI system has removed a headache in need to invest in own machines, highlighting the scalability and storage in other spaces, although with the importance of being able to reach agreements for performance improvement. 
• Minimum cost in technical support. The call, the argumentation to resolve technical incidents and the investment in a team that is dedicated to providing 24-hour assistance, has remained on a higher plane depending on the severity, so that conflict resolution, on numerous occasions, it is solved utilizing a forum, video, consultation of a specific page or programmed system.

Cloud Computing Sectors And Companies

” The 50 Best Private Cloud Computing Companies to Work For ” are highly rated companies worldwide. Above all, due to the substantial international expansion of some of them in Europe, for its adaptability to other markets, for being pioneers in the way of working and for allowing the most traditional sectors to carry out their digital strategy.

If Marketing / Sales, followed by Big Data / Analytics and CFO Tech, are sectors on the rise in creating cloud solutions, we see how collaboration tools are highly valued. The creation of a platform to improve worker selection processes ( Greenhouse Software ) has revealed three pillars in Human Resources: accessibility, monitoring, and contrast of information and measurement of results.

The outstanding American companies leave the evaluation of their own employees, highlighting the figure of their representative, the climate, and empathy with other colleagues. In addition to the design of a Roadmap to serve customers and to carry out the Business Plan and the configuration of the professional work team.

The lowering of costs means that sometimes more traditional companies encounter a problem: What tool do we choose? Is there a system that covers all areas of the organization? The Cloud Computing consultancy responds to the analysis of the current situation (work system, IT resources, and ERP employees). To cover what can be improved, tools for analysis of results and measurement, online work and collaboration, and project management are incorporated.

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