Data can transform society. Through advanced data, great knowledge in science, humanity and business can be acquired. Big Data allows this, integrating all data and converting it into information to make organizations evolve, and that is why data-centric companies clearly outperform the rest.

Large volumes of data are an opportunity to extract knowledge of new and emerging types of content to streamline the business and answer previously complicated questions. 2.5 trillion bytes of data are created every day and 90% of data worldwide has emerged in the last two years. It is estimated that by 2020 there could be four times more digital data. The information comes from everywhere: sensors used to collect details about the weather, publications on social networking sites, digital images and videos, records of purchase transactions or GPS signals from the mobile phone, among others.

Add value and reduce IT infrastructure costs

Big Data allows to grow, acquire and retain customers. The analysis of large volumes of data can help you discover ways to improve customer interactions, add value and build lasting relationships because it allows you to know your ideas, tastes or motivations.

In addition, it optimizes operations and the fight against fraud and threats. The adoption of a large data strategy and its analysis can help to plan, manage and optimize operations, supply chains and the use of infrastructure assets to reduce costs, increase efficiency and productivity and limit threats.

On the other hand, Big Data transforms the financial and management processes. The analysis of all data can boost business agility and provide information to make the best decisions about business strategy and human capital management.

It also facilitates risk management. Explore the strategic options for business growth using new perspectives obtained from the exploitation of large data, and maximize vision, ensure trust and improve the IT economy.

Today, big data technologies rely on the architecture of traditional technologies for data processing, which fails to meet all the demands of scalability, performance or storage. Ideally, a short and high-performance file system, database and software should converge in these technologies.

What are the trends?

When talking about big data trends, it refers to three main groups: storage, communications and software. You cannot have big data if we do not have or develop other technologies. Communications are essential, particularly if we are using or making a contract on several servers that are physically at different geographical points.

Experiences like Apache Hadoop will continue to support the development of other tools. This type of big data analysis is becoming a service that prevents a large computer cluster or a supercomputer in our own laboratories in order to obtain the corresponding data. We pay per event and that makes it a common practice. These kinds of needs are also causing that what previously corresponded to a very specialized area for the use of experts, is now permeating all sectors, even in small and medium enterprises.

Unrivalled Benefits

The data revolution is generating different benefits to health, science, business and government. This has allowed improving the quality of life of people and of course contributing to the development of the regions. However, it has also brought new challenges that are not contemplated in current methods, which range from data capture and storage to analysis and interpretation, which are topics to be investigated.

The main challenge today is to transform all that large amount of information that is being generated at this time, and the one we already have stored in different ways, in useful knowledge and bring these applications to organizations.

From here derive additional challenges such as the computational cost; aspects of computer security that are fundamental and that have become critical of other sectors; Integration with other systems and other aspects that each business or domain area has. In this scheme, rather than volume, speed and variety, we find the benefits for the big data application areas (companies, public administration, government, commerce, health, public services, etc.) due to the evolution of data and techniques for analyze them The goal of big data affects all these aspects as a development generator and we see in each of these areas specific examples of these applications.

Zazz, San Francisco and Data Science

Zazz has been transforming industries and businesses in San Francisco by implementing efficient Data Science solutions. Companies are not only getting more revenue but also implementing strategies to be future proof and plan ahead. If you are looking for productive Data Science development, do contact our team, we will show you the whole roadmap without any obligation.

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When to implement big data?

If a company feels the need to implement big data in its processes and units, it immediately associates the solutions with online models and high volumes of data processing, but this is not entirely true. Many organizations perceive the urgency of incorporating big data into their processes just because they are on the trend, but in a hurried way, if there is no mapping, there is a risk of misinterpreting data.

Brands are excited to believe that with the implementation of big data, they will have access to user databases. However, the reality today is that companies like Google, and in general social networks, only deliver information grouped with algorithms that are often unknown.

When you look at the term big data in broad strokes, it is understood as the ability to store and process, at extraordinary speeds, large volumes of data. In this first point, the concept generates emotion because you think of companies like Google or Facebook, which comply with that theory. However, in this regard it is worth asking if our business core really needs to capture so much information at such high speeds, or if it was already able to capture a minimum amount of information, integrate it and process it at admissible speeds. .

The most important question would be whether we have already managed to analyze it and turn it into valuable information for decision making. So, before getting excited about this concept of all the information and quickly, let’s first analyze the needs of the company and what it has done before with the available data.

The invitation is to ask ourselves why we want to implement big data and that the scope of the project and the investment in it be sized.

Digital metrics

Metrics are another type of data that tell how the performance of the brand’s digital assets is: a portal, an App or a social network. It details how many people enter the site, how many click on a content, measurements that allow us to know how the efficiency of our actions is, what we should improve, where we are making mistakes to correct them and reveal opportunities for online strategy.

Metrics allow us to make comparisons and analyze user behavior and the effectiveness of our digital strategy. If these measurements are not made, we will not know what we are getting right and what is failing.

Use the data to know your users

Companies should look for new ways to take advantage of the information that users generate in their transit through a website, to understand their behavior in order to design more personalized digital strategies.

Learn about two data analysis alternatives that give us a deeper understanding of what happens with users on the internet, but to make them easy to do and apply in the best way, it is necessary to have an adequate information structure.

Cross selling

Imagine we ran out of eggs for breakfast the next day; this is why we must move to the nearest market to acquire a new quantity of the product. However, at the time of purchase, there is a high probability that, in addition to the eggs, we decide to bring cheese, chocolate, butter and bread, which are usual companions of the egg during the first meal of the day.

Well, this relationship between this set of products refers to what in economics is known as complementary products, that is, when one is acquired, it is common that more are acquired.

Other examples may be swimsuit and sunscreen, as well as milk and cereal. That is why a good strategy to increase sales is to offer the complementary product when the user acquires one of the two, that is, to offer the milk when the cereal is bought.

This applies in various fields of economics, and not only in physical products but also in digital products (educational, musical content, etc.) or a combination of both modalities.

Advantages of Data Science

The implementation of Data Science decreases subjectivity in decision making, these will not be based on personal beliefs, assumptions or tastes but on real and interpretive data, which are analyzed at the exact moment.

The numbers are made and have no discussion, with that, we stop talking about generalities. Many times, brands are clear who their customers are, for example, that the majority are women between 25 and 35 years old. But what about the rest of the public? Having the great mass characterized does not imply neglecting the others. Precisely, it is about understanding the entire universe of users, that each one has a different need, so we try to look for microniches and microsegments, which allows us to treat and improve them.

The experience of data, in the end, is to understand that the interaction of a person X is different from that of a Y, the idea is that, that they communicate differently and that the brands treat them differently: that allows the data.

Using Data Science is to stay ahead of the competition and offer a better user experience, without making decisions from subjectivity and without shooting the air, on the contrary, you can be assertive and have a better strategy.

Data science will always be necessary, but under a conscious methodology, understanding what the need is, knowing what we want to solve, and from that, determining how big or small is the deployment of big data, metrics or any source around the Data Science

The post Top Data Science Development Companies in New York appeared first on Zazz.

• Zazz.io

   Zazz is a team of creative designers and developers building great digital products in Seattle and San Francisco. Our collective experience in the technology industry includes Mobile app development, custom Android app development, IOT application development, Blockchain development with a design first approach to product development.

• AppStudio

   AppStudio is a full-service Mobile App Development Company offering services in Native iOS Development (Swift 3.0), Native Android Development (Java), React Native Development. They have collaborated with Fortune 500 companies, Startups and Mid Sized firms across a spectrum of industries, ranging from Health Care & Finance to On-Demand App Development Services, to create Mobile apps that are actively being used by Millions of users across the globe.

• Neudesic

   Neudesic is the trusted technology partner in business innovation, delivering impactful business results to clients through leading-edge technologies, innovative solutions, and strategic alliances.  Founded in 2002 and headquartered in Irvine, California, Neudesic is a privately held company, serving clients globally from offices across the United States and India.

• Arbela Technologies

   Microsoft Dynamics AX CRM With over 100 years of cumulative ERP experience, we have Industry-Tailored Microsoft Dynamics AX Solutions to fit the specific needs of Consumer Packaged Goods, Manufacturing, Retail, Wholesale Trade: Durable Goods, Wholesale Trade non-durable goods and Distribution.

• Inspirage

   Inspirage is the integrated supply chain specialist firm solving business critical challenges from design to delivery. The company delivers end-to-end consulting and implementation solutions that link Product Lifecycle Management, Supply Chain Management and Logistics Management.

• CloudMoyo

   CloudMoyo is the partner of choice for solutions at the intersection of cloud and analytics. We help modern enterprises define their path to the Cloud and leverage the power of data driven insights.

• 47 Degrees

   47 Degrees is a global consulting and development firm specializing in helping clients take the next step in modernizing legacy applications to be real-time, secure, and responsive, by using battle-tested functional programming and cutting-edge technologies including Scala, Kotlin, Swift, Spark, Kafka, Akka, and Cassandra.

• Metia, Inc.

   At Metia, we create amazing experiences, ignite conversation, activate communities, inform customers and influence decision makers.

• Virtuozzo

   Virtuozzo is a leading hyperconverged infrastructure software provider with integrated container, virtual machine and storage solutions. Virtuozzo developed the first commercially available container technology in 2001, and today has more than 5 million virtual environments in production.

• Analytiks

   Hello! We're analytiks, a Seattle-based data company. And we want you to know that even though it's extremely cloudy here in Seattle, your data doesn't have to be. From long-term, end-to-end big data management, to jumping in and driving quick wins for your business - we'll provide you with the data solutions you need to drive more revenue to your bottom line. No matter where in the US you're located.

New Generation Analysis: Data Science

While Big Data focuses on providing tools and techniques to manage and process large and diverse amounts of data, it is not so focused on interpreting the results of data processing to support decision making. This is where Data Science comes in, focusing on the use of advanced statistical techniques to analyze Big Data and interpret the results in a specific domain context. Therefore, Data Science involves an intersection of several areas that include:

• Data Engineering
• Statistics
• Advanced Computing
• Display
• Domain and others

Within this context, tools and frameworks are required to:

• Statistics Programming
• Databases
• Import and clean data
• Exploratory data analysis
• Machine learning
• Deep learning
• Text Mining
• Natural Language Understanding
• Recommendation Systems, etc.

In general, Data Science focuses on providing a comprehensive solution to obtain valuable information to support decision-making in the fast and heterogeneous context of modern data management and analysis.

Current Panorama: Data Lakes

Thanks to social networks, personal mobile devices, sensors and other data intensive applications and devices, even small and medium-sized businesses had the opportunity to obtain large volumes of data about their businesses and customers. Here, Data Lake emerged as a typical solution to manage and analyze Big Data in that context.

When you have heterogeneous data sources that include unstructured (for example, social media) and / or semi-structured (for example, email), Data Lakes (usually an HDFS-based solution) It presents flexible data management where data can be ingested. Ideally, Data Lake can also include a metadata layer that describes data organization and semantics (for example, through the use of semantic technologies).

Once collected and stored, the data can optionally be prepared (for example, by creating tables and / or matrices) for data analysis (for example, visualization techniques or machine learning). Finally, the use of Data Lake supports batch data processing (following the complete arrows), that is, the data is stored and then processed for report support, and can also be coupled with the components for processing the data.

Benefits of Data Science

The generation, analysis and interpretation of data has a great potential for development in medicine, for example, since the use of Machine Learning algorithms for diagnosis and analysis allows to improve the prediction of conditions. From the hand of the digitalization of the available information and the crossing of data it is possible to learn about the diseases, to know in what conditions they can appear and even to detect their symptoms previously.

As in all cases, more information and data will result in better results and more knowledge in preventive health. In particular, interoperability between the different institutions would present great advantages and for that it would be necessary to generate common standards and a database with the information of all patients.

Education and Data Science

In education, the application of Data Science and Machine Learning allows students to explore their abilities and know their reactions to different stimuli to know which ones are more effective. The identification of learning needs from the analysis of performance and results also makes it possible to choose the optimal educational content and modalities to facilitate and promote the learning of each student.

Some companies are already making progress in this regard: Microsoft developed Azure Machine learning to help Indian educational authorities predict school dropout with information on student performance, school infrastructure and teacher skills.

Marketing, Advertising and Data Science

Another area in which tools for data analysis and machine learning are being successfully exploited is marketing and advertising. Big data allows you to customize the advertising that each user sees on social networks and makes streaming platforms suggest what content to keep watching according to the preferences of the person watching, but those are only the first steps.

Amazon uses Machine Learning technology to recommend new products to its users and its automatic systems have a high percentage of success in anticipating the purchases that their customers will make. Reading responses to an advertisement or a product, the prediction of the rotation of the clients and the selection of cases of better and worse results allows companies to know that are the most appropriate ways to achieve their objectives and improve the services. Having a better knowledge of the public and anticipating their needs also allows to personalize and improve the experience of each consumer in the exchange. Thus, data becomes an engine for marketing and advertising, but it also allows you to optimize the operation of each business by detecting what are the most and least used resources and what are the results obtained with each of them.

Retail and Data Science

There are several advantages of the use of Machine Learning and Data Science in retail, especially for those businesses that have a loyalty system that allows to have the data of what and how much each customer buys, know their tastes and preferences and improve and simplify your shopping experience.

Artificial intelligence has gone so far that it allowed Amazon to develop an automated store, Amazon Go, a path that was followed by Walmart: there customers take the products from the gondolas and withdraw without going through a box, since the process of Collection is made through the Amazon app with an incredibly accurate and reliable system. The generation and analysis of data also allows predicting the needs and behavior of customers,

Finance and Data Science

In the finance sector, data analytics and artificial intelligence are already used, which allows us to know the patterns of customer behavior, anticipate certain situations and improve responses to new conditions. In addition, data analysis is an opportunity to anticipate or reduce financial risks, not only for the company, but also as a service developed for customers. Having information about customer behaviors allows financial institutions to generate new business opportunities and anticipate the needs and demands.

On the other hand, working with other external databases may also allow them to increase the number of customers or discover opportunities for the development of new products. Further, Data Science techniques can be used to know, for example, where to locate branches or ATMs, how much money to enter in each of them or to know what areas of attention should be strengthened in each area. The development of new forms of communication with users is also an opportunity to use these tools to improve and expand their potential.

The post Top 10 Data Science Development Companies in Seattle appeared first on Zazz.

• Zazz.io

   Zazz is a team of creative designers and developers building great digital products in Seattle and San Francisco. Our collective experience in the technology industry includes Mobile app development, custom Android app development, IOT application development, Blockchain development with a design first approach to product development.

• AppStudio

   AppStudio is a full-service Mobile App Development Company offering services in Native iOS Development (Swift 3.0), Native Android Development (Java), React Native Development. They have collaborated with Fortune 500 companies, Startups and Mid Sized firms across a spectrum of industries, ranging from Health Care & Finance to On-Demand App Development Services, to create Mobile apps that are actively being used by Millions of users across the globe.

• SCI-BI

   SCI-BI works with some of the best Architects in the Information Technology domain, who not only design the system, but also act as Advisory for constant improvement of your IT practice. So when you choose to work with SCI-BI or one of its associates, you can be rest assured that your project is in the safest hands and the clearest minds who know how to get the job done.

• Dexlock

   We are a young and spirited team of IT specialists with a mission to reach the altitudes of professional excellence. Over the course of time, we expanded in forte and resources. We are individually specialized in the services that we offer which make us unique among the others in the market.

• 47Billion

   We are a product engineering services company specializing in big data engineering and visualization. We have a strong technical expertise in building highly scalable analytics on multiple cloud service providers. We have developed analytics based products in the telecom, logistics, agriculture, ad-tech, tourism, education, healthcare, IoT, and digital signages/kiosks industries.

• HatchWorks Technologies

   We are HatchWorks Technologies, a software solutions, and technology transformation company. HatchWorks software solutions fuel your digital transformation. We collaborate with companies ready to reinvent through technology. Our team of strategists, inventors, and advisors builds with you to disrupt the status quo. We work full-stack in nearly any platform and take projects full lifecycle from ideation to launch and beyond.

• Altoros

   Altoros is a 400+ person strong consultancy that helps Global 2000 organizations with the methodology, training, technology building blocks, and end-to-end solution development required to support digital transformation at scale. We turn cloud-native app development, customer analytics, blockchain, and artificial intelligence into products with a sustainable competitive advantage.

• Coherent Solutions

   Coherent Solutions is a software product development and consulting company that solves customer business problems by bringing together global expertise, innovation, and creativity to produce world-class technology solutions.

• Caserta

   We solve our clients’ toughest data intelligence challenges in weeks, not years. We are strategic data and analytics advisors first, and design and build what we reccomend. Our solid track record of client success coupled with our industry-leading tech expertise ensures your project gets completed with as little pain as possible, on budget and with minimum disruption.

• SmartCat

   SmartCat is boutique consultancy firm that focuses on Big Data systems - from Data Science to Data Engineering and DevOps. We use experience from each of our areas of expertise to offer both advisory and implementation services to our clients.

Data Science is closely linked to business, but in the end, it is a science, or it is in the process of becoming one, or perhaps not. I think it could be very useful for Data Science to be a science because if that is the case, every project in Data Science should be at least:

• Reproducible
• Fallible
• Collaborative
• Creative
• Complies with regulations

Big Data + AI + Data Science = General Artificial Intelligence

I am talking about General Artificial Intelligence (IAG) as the main objective of this revolution. IAGs are general-purpose systems with intelligence comparable to that of the human mind (or perhaps beyond humans).

We need Big Data as a catalyst to reach AGI, because with more data, more new ways of analyzing data, in addition to better software and hardware, we can create better models and a better understanding. We need the current state of AI, very close to Deep Learning, Deep Reinforcement Learning and its surroundings (more about Deep Learning here ), and then we need Data Science as the controller and the science behind this revolution.

Data engineering

What about data engineering, which is the first to deliver data to the data science team? As it is a sophisticated field, I prefer to protect it from the hegemonic aspirations of data science and, moreover, it is much closer to software engineering than statistics.

The difference between data engineering and data science is the difference between before and after. Feel free to see the difference between data engineering and data science as a before and after. Most of the technical work that leads to the birth of data (before) can be called “data engineering” and all we do when some data arrives (after) is “data science.”

Decision Intelligence (DI)

DI has to do with decisions, including making decisions at scale with data, which makes it an engineering discipline. Extends the application of data science with the ideas of social and management sciences. Decision intelligence adds components of the social and management sciences.

In other words, it is a superset of those pieces of data science that do not deal with research things, such as the creation of fundamental methodologies for general use.

Data mining

If you still don’t know what decisions you want to make, the best thing you can do is go out for inspiration to discover them. This is known as data analysis or analytical or descriptive analytics or exploratory data analysis (EDA in English) or knowledge discovery (KD in English), depending on tastes and colours. And contrary to what the saying goes, this is what the authors have written a lot about.

Start here, unless you already know how to structure your decision making. The good news is that this is easy. Think of your data set as a group of negative photos that you found in a dark development room. Data extraction involves using the equipment to reveal the photos as quickly as possible, so you can see if there is something inspiring or interesting in them. As with the photos, remember not to take what you see seriously. You didn’t take the photos, so you don’t know much about the stories behind them. The golden rule of data mining is: focus on what is here. Just draw conclusions about what you can see, never about what you cannot see (for that you need statistics and much more experience). Data mining experience is judged by the speed with which you can examine the data.

The dark development room is intimidating at first, but not much can be done about it. Just learn to use the development equipment well. Here is a tutorial in R and here in Python to get started. You can call yourself a “data analyst” as soon as you start work, and you can call yourself an “expert analyst” when you can reveal the photos (and all other types of data sets) at lightning speed.

Statistical inference

Inspiration is cheap, but rigour is expensive. If you want to go further with the data, you will need specialized training. Having a bachelor’s degree and a postgraduate degree in statistics, my opinion may be a bit biased, but I think that statistical inference (statistics for short) is one of the three areas, the most difficult and loaded with philosophy. Becoming good at this takes more time.

Inspiration is cheap, but rigour is expensive.

If you want to make important, high quality, and controlled risk decisions that are based on conclusions about the world beyond the available data, you will have to add statistical skills to your team. A good example is a moment when your finger is circling around the start button of an Artificial Intelligence (AI) system and it comes to your mind that you should verify that it works correctly before squeezing it (it is always a good idea, in serious). Get away from the button and call the statistician. Statistics is the science of changing your mind (when there is uncertainty).

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