Best Big Data Analytics Companies – July 2019 Reviews

Rankings as of July 15, 2019

In today’s online business scenario, big data is omnipresent! You can find it hiding beneath the surface of every communication, available to be collected and re-framed for purpose of profit-making decisions. Several top companies in the world have shown their interest in Big data analytics and have hugely invested in it. The best part of Big data is, it is not concerned about the presence of data volume but focuses on what we do with the data.

Whether you are a start-up or an established brand, Big data analytics service can bring about a 360degree change on how your business data is being handled and used. For this reason, the best option is to hire services from a good and reliable Big data analytics company. Now how do you select the best Big data analytics company for your business?

We have curated the best of data-analytic companies for you. These companies have cut through our research ranking analytical process and are the most promising and competitive contenders of the Big data industry. Read about them, shortlist the most suitable one, contact and hire the best-suited one for your business.

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Total Records: 10
RANKINGS
Rank Company Location Employees Score Website
1 CBIG Consulting Chicago, IL 50 - 249 99.9% Visit Website
2 Affirma Consulting Bellevue, WA 50 - 249 99.4% Visit Website
3 Denologix Toronto, Canada 50 - 249 99% Visit Website
4 ThirdEye Data Santa Clara, CA 50 - 249 98.7% Visit Website
5 Pragmatic Works Fleming Island, FL 50 - 249 98.6% Visit Website
6 cBEYONData Lorton, VA 10 - 49 98.2% Visit Website
7 Beyond The Arc Berkeley, CA 10 - 49 97.6% Visit Website
8 Fayrix Pitu'akh, Israel 1,000 - 9,999 97.5% Visit Website
9 Anthem Marketing Solutions Chicago, IL 10 - 49 97% Visit Website
10 PSL Corp Medellín, Colombia 250 - 999 96.8% Visit Website
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What is Big Data?

With the world moving on to digital platforms for almost all needs and firms investing millions of dollars in data capture, Artificial Intelligence, Internet of Things, Business Intelligence, Data Science and Analytics, future organizational strategies are bound to rely on data. The hype around Big Data is very much real, and data is now believed to be the new oil.
Big data isn’t something that confines its limits. It affects almost all the sectors and has the potential of changing them massively. Depending on the industry, Big data if resented effectively and analyzed correctly, it understands the targeted audience and anticipates their needs. And in specific sectors like that of Health Care, Big Data has helped overcome an issue like Fertility. Glow, an app is one perfect example of this. Further, in the article, we discuss how Big Data has affected different industries.

So, what is Big Data?
Big Data can be defined by 3 V’s

Volume: Terabytes of data produced at an hourly/daily/weekly/monthly cadence. A Boeing 747 delivers 10TB of logs every 30 minutes per engine, and it has four engines. So, you can imagine the volume of data produced per flight. Modern-day traditional database systems cannot handle that amount of data.

Variety: These are nothing but different formats of data that are required to be processed. Data in tables or logs format is structured and can be handled by a traditional database system. Since there is not much complexity, it cannot be classified as Big Data. However, tweets, social media data, data from sensors, etc. is unstructured and hence complex. However small the data, if it is complex, it falls under the umbrella of Big Data.

Velocity: Velocity is the rate at which data is generated and how fast it needs to be processed to make business decisions. Any real-time tracking and real-time decision making can be considered to fall under the umbrella of Big Data. Examples: High-Frequency Trading (Buying and Selling of stocks in milliseconds)

Big Data, even though is a blessing for industries, it does have certain challenges it faces. Big Data has large samples and highly dimensional as well majorly create three challenges:
Because Big Data is highly dimensional, it brings noise accumulation; which means it can’t reduce the unnecessary noise from the data, spurious correlations; which means your data might be interfered by coincidence or third unseen factor, and incidental homogeneity; which means it may assume that property of any one part of a dataset is same as any other part.

Since the humongous samples of data are collected from multiple sources and via different technology, it creates issues of heterogeneity; which mean every data will have a different value, experimental variation; which indicate variation of information is hypothesized to reflect the variation and statistical biases; which means it might over or underestimate a population parameter.

Highly dimensional and large samples together create certain issues as well like, a heavy computational cost which further causes algorithm instability.

Big Data can find its applications across industries including Retail, Financial Services, Insurance, Pharmaceuticals, Manufacturing and Supply Chain and a clear majority of today’s Fortune 500 companies use it to make more informed tactical and strategic decisions.

Applications of Big Data

Some examples of Big Data Applications:

Ad Personalization and Targeting (Using online browsing and buying behavior, customer demographics, customer psychographics etc.)

Anti-Money Laundering and Fraud Detection (Identifying transactions patterns and detecting anomalies)

Improve Web Customer Experience (Analyzing customer drop-off patterns, calculating Net Promoter Scores etc.)

Social Media Sentiment Analysis (Benchmark and track product reviews and performance using social media data)

Improving Product placement, Assortment Planning and operational efficiency of checkout counters for Brick and Mortar Retail Outlets, E-commerce companies
Improving Sales Force Effectiveness (Pharmaceuticals and Insurance companies)

Technology Used

Here are some of the top technologies used to store and analyze Big Data.

Map Reduce computation framework and Hadoop Distributed File System (HDFS)

Distributed Databases

No SQL Technologies

Hadoop is the most utilized technology for Big data processing and analysis. It works on the concept of distributed processing where it one name node controlling the data in the data nodes and keeps track of the data while data nodes that are scalable do the processing.
Hadoop is an open source technology and most products that come out today are using Hadoop in some way or the other to do Big Data processing and analysis.
The Hadoop file system is oblivious of the structure of data and can use any data as input and still process it. This is different from the traditional database systems where a schema needs to be defined and the data is inputted in the format as mentioned in the schema.

Hadoop utilizes Map-Reduce technology that was first invented by Google. It works on the concept of distributed computation framework. Few Properties of Map-Reduce:

Scalability (Add more data nodes if you are processing more data)
Fault-tolerance (There are always stand-by data nodes available if one fails)
Batch computation in parallel.

To query Big Data, the below technologies are the most popular:
Hive
Pig
NoSQL
Mahout
Impala
MongoDB
SPARK
Oozie
Sqoop
HD Insight

Why do you need a Big Data Agency?

While hiring an in-house data analyst has its benefits. There is no denying the efficiency of hiring an agency for Big data. The more employees the agency has, the better for your business as there is more than one mind working on your project which results in completing the project before or on time. By hiring an agency, one gets all the combined skills under one roof making it easy for you. A few reasons why one should hire an agency are listed below:

Access to Bigger Data: By accessing big data, the employees of an agency can help predict the future of your business and guide you the best way to go about your project to achieve your aim.

Risk Assessment: It falls on the agency to run the risk assessment for you if you hire them. For a secure future in the business, as the agency can access and analyze the credibility of anyone, you chose to partner with. The agency you hire will run the credentials of the company you plan to partner with to see if the partnership will be profitable.

Monetization of Data: Agencies can help you make your product visible. If agencies job to makes sure that audiences notice your products. For example, the various related product you see when you’re buying any product on an e-commerce site. It’s the agency’s job to highlight your product to benefit your business.

Unbiased predictive analysis: Though an in-house analyst can run a predictive analysis, the agency with gives you an impartial and dispassionate predictive. Based on this analysis the agency will be able to guide you the changes in your data model. They can run a more efficient predictive analysis of customer behavior.

Diversification: Agencies are more cautious when it comes to diversifying your business. They check the feasibility more rationally when you decide to expand your business.

Usage of Big Data in Different Segments

Big data affects isn’t limited to one single industry, it practically affects organization across all sectors. Underneath is a list of industries and how big data affects them:

Education
: The education sector has substantial data like syllabus, attendance, curriculum, and student progress reports. We can identify at-risk students by analyzing big data, and the student makes sufficient progress. Big data can make a notable impact on the school system and implement a better evaluating system help teacher.

Banking: Banks, as we all know, store vast information and from countless sources which is why they look for new and innovative methods to manage big data. Big data gives insight on how to minimize the risk and frauds and boost customer satisfaction. The financial institutions should be ahead in the game with advanced analysis even though big data provides insights.

Government:
 Government agencies cover significant ground; dealing with traffic, preventing crime, managing utilities, running agencies which result in humongous about of data. They can harness and apply big data which has a lot of advantages, but the government should also address the issue of privacy and transparency.

Health Care: 
Birth certificates, death certificates, prescriptions, patient information, and treatment plans are a few types of data in the health care sector. Everything needs to be accurate when it comes to health care. Health Care providers can improve patient care by uncovering hidden insist if the big data is managed effectively.

Manufacturing
: Big data in the field of manufacturing can solve problems faster and make agile business decisions like a lot of manufacturers are working in analytics-based culture. With the help of bid data manufacturers can boost their product quality and output and minimize the waste and make a mark in this competitive market.

Retail
: Big data helps to manage Customer relationship building which is an essential part of the retail industry. Retailers need to know the best way to market to their customer & handle transactions, and the most strategic way to bring back lapsed business; all of which can be done with the help of bid data quite effectively.

Trends for 2019

The enormous data that is generated every day in every industry is called big data. Initially big data was positioned by the big businesses who could invest in technology and could further analyze the data information, but today, every small or large enterprise rely on big data for essential business insights. Which is why the trends of big data have evolved massively. Here are the top trends of big data in 2019:

Open Sources: 

The small organizations and startups will benefit the most now that there will be more free data and software tools available. The analytical languages in open sources like GNU and R along with graphics and statistical computing is a vast adoption credit and revolution in open sources wave.

Edge Computing:
With Edge computing, one can store and handle the data away from silo setup closer to end users. The processing takes place in either edge data center, or fog layer, or the device itself making it easy to access. Even though Edge computing has been around for some time, the credit is partially dependent on bandwidth network to save data locally close to the data source.

CDOs in Demand:

Chief Data Officer is a relatively fresh concept to many companies, but the profiles have evolved and have made the HR be on toes scouting for them. The trendy job profile involves enterprise-wide data cleaning, studying and visualizing intelligent insights, and analyzing enterprise data.

Quantum Computing:
Quantum Computing permits seamless data encryption like solving complex medical problems, weather prediction, and real conversation & make better financial models for organizations to develop quantum computing components, applications, algorithms and software tools on Qubit could services. It’s quantum computing that has the big tech companies like Microsoft, IBM, Intel and Google competing with each other to build the first quantum computer.

IoT Networks:

Internet of Things will be an essential trend as with will generate around 300 billion dollars or more annually by 2020. The professionals estimate the growth of the global IoT market at 28.5% CAGR. Which further means that business houses will be depending on more data for detailed business insights and collection of information.

Dark Data:

The unused digital information for business analysis in technology is called dark data. This data is used to obtain insights into decision making via the various computer networks. With data and analysis being an essential part of an organization with is generated every day, there is a need to understand this unexplored data and analyze it as it may lead you to potential security risk.

Predictive Analytics:
This trend is used to customized insights that motivate the organization to generate purchases and promote cross-sell opportunity along with new customer responses. It helps technology to integrate into a variety of domains like healthcare, automotive, pharmaceuticals, manufacturing, hospitality, finances, retailing and aerospace.

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