The term “big data” was first coined by Roger Magoulas of O’Reilly Media in 2005, and it has since become a buzzword in the business and technology worlds. But what does it mean? Put simply, big data refers to data sets that are too large and complex to be processed using traditional data processing techniques.
The process of collecting, processing and analyzing data has evolved significantly since the advent of the first large-scale databases. The data explosion that began with the introduction of the personal computer has accelerated in recent years as the Internet of Things has expanded exponentially and as better, cheaper storage capabilities have evolved. That explosion led to a crucial realization: Businesses need to be able to manage and process data at ever-greater speeds. The ability to perform that function at scale and in real-time has led to the term big data, which encompasses a wide range of technologies and applications. We will discuss about big data in detail covering basics to more advanced topics for those who are in leadership roles. The post is really lengthy ( 6,500+ words), so use the below table if you want to just read specific sections and skip others. We will update this page from time to time to keep the information fresh and up-to-date. So bookmark it if you want to 🙂
Table of Contents:
Types of data that make up big data
How big data analytics is different?
Why data quality is considered to be the most important in big data analytics?
Big Data Buyer’s Guide: Introduction
Big Data in numbers and forecasts
Market Dynamics: What do buyers want from Big Data providers?
What to look for in big data solution providers?
Tips for choosing the right big data solution
Questions to get started with when choosing a big data platform
Top Big data companies/big data solution providers
Characteristics of big data
The characteristics of big data can be represented by the three “V’s”:
Volume
Volume is one of the key characteristics of big data. Data volumes that, in fact, may reach hitherto unimaginable heights. Terabytes and even Petabytes of data are already commonplace in large corporations’ servers and storage systems.
Velocity
Velocity refers to the speed at which data is generated and collected. Big data is generated at a rapid pace and must be processed quickly in order to be useful.
Variety
Variety refers to the different types of data that are collected. Big data can include everything from text to images to audio and video.
Types of data that make up big data
As explained above, big data comes in “various types with different characteristics,” but what kind of data does it specifically refer to? There are four main types of data that are typically considered to be big data:
| Source | Type of data | Explanation |
| Government/Administration | Open data | Public information that is owned by the government or local governments. There are a number of ways to access government open data, but one of the most popular is through the use of data portals. Data portals are websites that provide a single point of access to a variety of data sets. They often allow users to search for data sets |
| Enterprise | Digitalized information | Data that businesses generate in the course of their operations. This can include customer data, financial data, and employee data. |
| Industrial | M2M Data | Data collected from IoT devices at production sites such as factories, sensing data (distortion, vibration, type and weight of passing vehicles, etc.) from IoT devices installed on bridges, etc. M2M data is data generated by. This can include data from sensors, cameras, and other devices. |
| Personal | Personal Data | Personal data is generated by individuals and usually refers to things like social media posts, emails, health data, and financial data |
TODAY, DATA IS NO LONGER JUST INFORMATION — IT IS AN UNRIVALED SOURCE OF POWER.
How big data analytics is different?
The world around us is getting increasingly data-rich, and companies are scrambling to find ways to harness this business intelligence. Big data holds tremendous value, but it can also be extremely overwhelming to sort through. That’s where big data analytics come in.
The practice of evaluating enormous data sets to discover trends, hidden patterns, correlations, and other insights is known as big data analytics. Big data analytics differs from big data in that it is a process for analyzing enormous data volumes. The term “big data” refers to the actual data sets themselves.
Big data analytics involves applying specific algorithms and tools that can handle large volumes of data. This might include data warehouses, Hadoop clusters, and cloud-based solutions. The data generated from numerous sources is used to find solutions to complex issues in real-time.
Why data quality is considered to be the most important in big data analytics?
Big data is a huge industry. It is the force behind the insights that inform so many of the investment decisions we make every day. The choices we ultimately make often depend on those insights, which, in turn, depend on the quality of the data. This is why the quality of data is important: the insights we glean from data sets help inform our investment decisions, and the quality of data directly impacts the accuracy of those insights. In other words, if we want to make sound investment decisions, we need to start with high-quality data.
Big Data Buyer’s Guide: Introduction
Big data is not just about having more data or being able to analyze it faster; it’s about looking beyond numbers and patterns in order to make sense of complex information and identify valuable insights that can be used by companies. This requires a new way of thinking about big data—one that goes beyond simply gathering information from disparate sources into one unified platform where all relevant information streams can be easily accessed by everyone in your organization without requiring them to have technical skills or expertise in multiple databases or toolsets.
If you’re like most CIOs today — you’re likely getting up to speed on big data technology and trying to understand the opportunity this technology presents to your business.
Being an IT decision-maker in a big data world means choosing the right technologies and implementing them in a way that meets your business objectives. And that’s no easy task, considering the variety of solutions available and the wide range of vendors in the field.
To help organizations sort through all that Big Data has to offer, we have identified and evaluated leading and emerging big data software and services vendors so that tech buyers can assess the capabilities of various solutions and make the right choice for their respective organizations.
The value of a big data solution may be less in the technology itself and more in how the vendor enables an organization to achieve their business goals.
Big data has become a strategic necessity for most organizations — especially for those who want to stay competitive. For companies of all sizes, big data is transforming the way business is done. It’s not only changing the way they collect, store, and analyze data, but also how they operate and interact with their customers. This data gives them unparalleled insights into their markets, customers, operations and other areas of strategic decision-making — all of which can help them make better business decisions and fuel stronger revenue growth.
As data grows exponentially every day, it is evident that we need a strategy to securely and efficiently store this data. Many organizations lack the expertise, resources, capabilities or strategies to analyze their growing volumes of information, let alone extract value from it. They are struggling to capitalize on the vast potential of big data or failing to realize the expected returns from these investments.
But why so? It is because, without the right technology partner and solution, big data deployment and execution can become a daunting task. But with so many companies jockeying to be the leader in this emerging space, there is confusion around which solution is truly the best fit for a particular business. To help you find the right vendors this report should be helpful.
The 2022 Big Data buyers guide sheds light on the rising new generation of companies with class-leading big data solutions that are poised for major global growth. Some of them even emerged from stealth mode and are already setting the tone for a new generation of big data solutions.
We identify these companies as “Best-in-class Vendors” because they exhibit the best growth prospects, deliver significant value, are widely adopted, and show the strongest potential to scale. We believe each of these vendors will play a significant role in creating a competitive advantage for companies using big data throughout the coming years.
These companies are revolutionizing the way companies leverage data, enabling them to gain a competitive advantage and have the right combination of technology innovation, customer traction and overall market presence that we feel makes them a strong candidate to be included in the list. We are confident these companies will continue to gain momentum in the coming years and will be indispensable to enterprise organizations reaching their big data goals.
For the purposes of this guide, we focused on measuring how quickly the vendor gained traction and recognition in the marketplace as evidenced by press mentions, analyst coverage, customer adoption and partner activity.
The guide is written from the perspective of a buyer looking for solutions. It gives readers an in-depth look into leading and emerging big data vendors with information about their solution overviews, industry credentials, key differentiators, product capabilities, market position and strategic focus. The report also considers how these companies are positioning themselves to achieve competitive advantage by defining their niche, setting goals, and differentiating themselves in their respective markets.
The guide also covers key questions and factors that business leaders need to consider when evaluating big data solution providers along with the results of our survey assessing the opportunities and challenges facing organizations as they implement data-driven solutions.
We hope this guide comes in handy for anyone and everyone looking to make sense of the growing big data sector or to pick the right companies for existing projects.
Big Data Market Overview
Big data was once a passing fad that has now become a permanent fixture in the technology world, as companies that want to improve performance and deliver on customer expectations realize that data and analytics play an important role. In fact, Harvard Business Review declared big data as one of the five must-haves today for business leaders looking to stay competitive in a hyper-competitive market.
The advent of Big Data has created countless opportunities for companies to increase revenue, improve efficiency and gain new insights. In the last half-decade, the amount of data being generated has exploded, and with it, the opportunity to leverage data and analytics for competitive advantage.
The convergence of big data, enterprise data and cloud computing is changing how businesses interact with its customers. Newcomers to the data management market are bringing both technology and expertise to the table, and while big data still has its challenges, its strong foothold in data management solutions is allowing for a broad range of use cases and an expanding market.
Over the past several years, the big data landscape has developed into a mature and complex set of technologies, standards, and services. As a result, big data vendors are now expected to demonstrate more than mere compliance with various requirements; they are expected to have mastered sophisticated functions and manage complex solutions.
Big Data in numbers and forecasts
According to forecasts, by the end of this year, the number of implemented solutions based on Big Data in financial institutions alone will increase by 700%, since their use allows you to optimize costs. Therefore, the most relevant trend now is the transition from conventional information storage platforms to cloud ones. This will increase the number of Data-projects, which are increasingly based on Open Source.
The big data analytics market is projected to grow from $163.83 billion in 2022 to $273.53 billion by 2026, at a CAGR of 11.2% in the forecast period. The growth in the big data market has been phenomenal, with revenue projected to reach $180 billion globally by 2023. This is no mere pie-in-the-sky figure, but rather a result of several trends. To start with, the big data market is already rife with numerous high-profile companies that are already making heavy use of big data.
For big companies, analytics is in their DNA. Facebook, Google, and Amazon have access to hundreds of data points, which they analyze and use to target ads, personalize services, and predict customer buying habits. This means the big data market is already here, and it’s something every organization must now deal with.
But for many organizations understanding the business value of big data is a two-edged sword. On one hand, big data offers unique and powerful capabilities that solve many pressing business problems, on the other hand, big data has gotten so big that it’s challenging for business professionals to make a clear connection between their use of big data today and the “perceived” business value they are achieving with big data.
However, despite this enormous market, companies are struggling to derive enterprise value from big data. The reasons can be attributed to the fact that the industry still lacks standardization, has not achieved maturity and the available data is not of high quality. Enterprises struggle to integrate disparate data sets, find data talent, manage governance issues, and decide between traditional linear analytics approaches and new non-linear or “machine learning” techniques.
To meet these demands, the guide recommends solutions that enable companies to collect and coalesce a range of data sources for analysis and allow business users to explore their data using intuitive tools to enable rapid insights rather than requiring them to learn complex programming methods.
Big Data Market Trends
Market analysts say that in the financial services industry alone, the volume of new data will grow by more than 1200% during 2023. By processing data and extracting knowledge from it, companies can significantly increase their competitive advantage and reduce costs. For example, thanks to working with Big Data, Netflix annually saves about one billion dollars in customer retention.
Becoming an increasingly popular direction, Big Data is overgrown with numerous innovations: some of them take root, while others remain at the level of local experiments. Here are seven trends in Big Data that successful companies are guided by:
Cloud First
Analysts at Gartner predicted that cloud services will become essential for 90% of new data products and services in 2022. The milestone of the Cloud First strategy has come: while maintaining their own IT systems, companies use the power of cloud technologies, moving to the cloud as a data storage platform. This enables them to launch new projects as quickly as possible, test strategies faster, and bring new products to market.
However, the business could not immediately “taste” the benefits of clouds. Economic aspects were paramount for the companies: TCO on-premises and cloud infrastructure were compared. Then a fast go-to-market strategy became the main one, allowing you to quickly scale in the clouds or transfer a workload that is not business-critical, such as development, testing, and training environments for ML models.
And, finally, companies began to transfer data platforms to the cloud because they do not want to keep their IT infrastructure complex. The number of data projects began to grow, for which Open Source is increasingly used tools proven in world practice.
Cloud First enables a company to outsource some of its competencies. In world practice, most companies prefer to work at the application layer, which shows exactly how a company can benefit from data in the context of its business.
Companies keep only the core competencies in the field of business processes, and everything related to system software, data platforms and infrastructure is left to professionals. Even if the customer is not ready to switch to a public cloud provider, he wants to build a similar system within his company and find an external contractor who will completely cover issues with a scalable infrastructure and data platform.
The transition to cloud infrastructure will be more active as data security grows. This will also lead to an increasingly dynamic functional development of cloud providers, which over time will come as close as possible in terms of capabilities to Western competitors.
Data Minimalism
In the past, companies would collect large amounts of data with little regard for its meaning or relevance. This process has changed in recent years, and companies are now more focused on collecting data that is useful and compliant with legal regulations. This shift has resulted in better decision-making and more efficient data use.
According to the IDC forecast, by 2025 the total amount of data generated worldwide will more than quadruple to 175 zettabytes (for comparison: in 2019 this figure reached only 40 zettabytes).
It is assumed that almost 30% of all generated data will be analyzed in real-time against 15% in 2017. At the same time, more than 90% of unstructured data remains unprocessed. This leads to huge losses.
It’s no surprise that companies are rethinking how they work with data and redefining their processing needs. They try to use the minimum amount of data to solve a specific problem. In addition, there are more and more devices that can collect and store data on their own, without loading the centralized storage. For example, mobile applications of banks that perform a large number of tasks remotely without contacting the central bank data processing systems every second.
Digital twin
A few years ago, data analysis was mainly used for retrospective analytics. By analyzing the number of units sold in a given year and linking this data to a forecast, a salesperson could understand why sales had fallen. Over time, analytics made it possible not only to look into the past but also to predict what results in this or that decision will lead to.
Today, big data makes it possible to build recommendations that affect the quality of business processes in time. An illustrative example is the digital twins of various equipment, which are widely used in manufacturing enterprises. It is a virtual copy of a real object, which in real-time behaves just like it. The use of a digital twin allows you to conduct any experiment and analyze how a physical object would behave and choose the most optimal scenario for the development of events. This is especially true when predicting equipment failures.
Data Fabric
Data Fabrics continue to evolve as well. This architecture provides the ability to access data stored on various platforms and cloud services from a single point. It helps to reduce the cost of administration of data storage and management processes, but at the same time, its use leads to an increase in the cost of integration capabilities between different types of DBMS. A single storage platform comes to the rescue, which eliminates the redundancy of Data Fabric storage.
Artificial Intelligence
Companies today are able to process petabytes of information with high performance. To do this, they use distributed data processing technologies, as well as open-source tools. Machine learning and artificial intelligence (AI) systems help businesses identify patterns, anomalies, and make predictions.
AI is being used by organizations of all sizes to optimize and improve their business processes. It helps use big data to provide deeper customer support, such as through intelligent chatbots and more personalized interactions. At the same time, there is no need to significantly increase the staff of the customer support service.
AI-enabled systems are capable of capturing and analyzing vast amounts of information about customers and users, especially when combined with a data lake strategy that can aggregate a wide range of information from many sources.
Differential Privacy
Companies will have to start treating the customer as a subject and prepare to be held accountable for how they use their data. Data must be collected in a way that does not violate privacy. For example, anyone can ask a giant like Google for information about what data the company has about him. The client becomes a full-fledged participant in the process of working with Big Data: he wants to understand how the information collected about him is relevant, will be able to make adjustments to it and even monetize his data.
This trend is called Differential Privacy. It leads to the fact that companies begin to need not personal information about specific customers, but data on customer segments or clusters with similar characteristics.
IoT and Big Data
The trend of using Big Data in the Internet of things (IoT) has been gaining momentum for several years. According to Gartner, there were 21 billion connected devices in 2020.
By 2025 one of the growing markets for connecting devices will be the segments of monitoring vehicles and road infrastructure, housing, and communal services with connected resource consumption meters and video surveillance. Integrating the IoT with machine learning and data analytics increases the flexibility and accuracy of machine learning responses. Large companies are already using IoT devices to improve the efficiency of data analysis.
Data as a Service (DaaS)
According to Mordor Intelligence forecasts, the data as a service (DaaS) market will grow by an average of 10% and will reach $46.5 billion by 2025 in the US alone. Using DaaS allows you to improve the customer experience, increase revenue, and develop better products and services. 65% of respondents in an Adobe research survey believe that using data as a service has helped them improve their data analytics capabilities to better understand customer experience requirements. According to the forecasts, DaaS market revenue will grow to $10.7 billion by 2023.
Working with Big Data requires businesses to rethink many established processes. And these issues need to be addressed now, since each trend is multifaceted and constantly evolving, acquiring new knowledge, solutions and tools is a must. Some of them should be looked at gradually introducing them into the business, while others should be responded to promptly.
Market Dynamics: What do buyers want from Big Data providers?
The market for Big Data providers is rapidly growing, as more companies are realizing the value of this resource. However, like any new technology, it can be difficult for companies to find a match between what they need and what the current market has to offer. For Big Data providers, the key is to know how to use the technology in a way that provides value for their customers, and that means giving them exactly what they want. Here are a few aspects buyers look for when selecting big data solutions:
Innovative partnership
The next-generation big data buyers expect vendors to provide innovative solutions, bring fresh ideas, new capabilities and new ways of approaching their business problems to the table to automate processes and make better use of data to create business value. They want to work with vendors who have the potential to disrupt their industry and therefore, will be a trusted partner in the future.
The most important capabilities they look at are the ability to quickly and easily integrate data from multiple sources, automated data processing and management, ability to create custom reports and dashboards. Considering innovations from various big data application vendors, research in artificial intelligence and machine learning is advancing at breakneck speed and becoming a mainstream part of big data analytics. Choosing a vendor that is not at the forefront of this research could mean finding yourself falling behind in the competition.
Ease of use and Customer Support
Big data software buyers put an increasing emphasis on ease of use and functionality that can be understood by everyone regardless of their technical or analytic skills. Buyers want to be able to use their data quickly and easily without having to spend much time learning how things work or getting stuck trying to figure out what’s going on behind the scenes when they’re not sure what they should be looking at or how they should interpret it. Big data analytics solutions are long-term investments. CIOs need to ensure that they have adequate support, training, and advice in case they run into any issues with their big data analytics solution.
Flexibility, transparency and scalability
Big data solutions that offer flexibility, transparency, and scalability are in demand among buyers. Flexible solutions offer prioritized features, which give buyers the ability to customize their solutions. On the other hand, companies want to know whether they can scale up or down their big data solution, allowing companies to scale up their businesses and introduce large data sets. Most buyers are more interested in seeing transparent information on how a solution provider plans to address their business needs than looking at specific product capabilities. While many vendors claim their solutions are secure, few actually demonstrate end-to-end security against attacks or vulnerabilities in demos.
Help with architecture
Organizations are increasingly reliant on technology to drive their business. However, as the number of systems and applications grows, so does the challenge of keeping them all connected and running smoothly. This is especially true for organizations with a mix of on-premise and cloud-based systems. Often custom code has been developed by people no longer in the organization and business continuity for these systems is a challenge. Technology buyers want partners who can help assess their IT needs and implement changes without risking their business. They want a partner who can help them define what architecture they need, then use their experience and expertise in building systems to manage such complex requirements to make sure they work well together.
Cost-effectiveness
As organizations strive to do more with less, IT budgeting has become a complex balancing act. On one hand, there is a growing demand for quality analytical systems. On the other hand, organizations are under pressure to reduce costs. This has led to a more complex purchasing model, with organizations having to consider a variety of factors when making purchasing decisions. In response, leading providers in the Big Data space are increasingly offering data-driven pricing and cost-tracking solutions to help customers better manage information usage, costs and maximize the value of their data assets.
What to look for in big data solution providers?
All big data solutions are not created equal. A variety of factors impact the success of a big data strategy, making it essential to compare the offerings of various solution providers. For example, the ability to scale, deployment flexibility, and what services are included with the platform are all critical considerations for companies looking to adopt a Big Data platform.
Here are some critical factors that buyers need to consider in order to implement and leverage Big Data solutions:
Security of data
Big data solutions should ensure secure data transfer and storage. This means looking for providers who can encrypt and protect data using military-grade cryptography, as well as provide methods for time-stamping and source-proofing web sessions and mobile apps to prevent any tampering or hacking of sensitive data. Providers should also guarantee regulatory compliance through industry-recognized standards such as ISO 27001 and SOC 2 audits to ensure data security at every turn.
Scalability
Your big data solution provider must ensure that you can scale your storage and processing capabilities as needed, without having to worry about server maintenance or hardware upgrades. This allows you to focus on your core business goals and objectives while knowing that your data is in safe hands.
Data compression
Data compression is an important factor to consider when working with big data. By compressing data, you can reduce the amount of storage space required, which can save you money on storage costs. Look for a provider that offers data compression technology to help shrink down your digital footprint.
Fast to set up and easy to maintain
While there are many Big Data solutions out there, some of them are extremely difficult to implement and maintain. Proactive organizations and enterprise architects know that Big Data initiatives require speed and agility to be successful. They put together their teams and look for solutions that will help them implement quickly and be able to adjust to changes in their environment. Consider how quickly a business can get a particular solution up and running. Most companies expect to benefit from their big data projects in days or weeks, not months or years.
Integration with Existing Architecture
The integration challenges can stem from both technical and business challenges. Most organizations already have existing investments in data management and analytics technologies. Replacing the technology entirely can be costly and disruptive, so organizations often choose to find solutions that work with existing tools, or can augment existing software.
Accessibility for everyone
Big data solutions may be too complex and convoluted for an average user. The solution should be easy to use and understand for all users, regardless of their technical background or expertise. You need a tool that is easy to use, offers flexible permissions, and is accessible across multiple devices.
Batch vs streaming data
The earliest big data solutions, such as Hadoop, only dealt with batches of data, but businesses now prefer to analyze data in real time. This sparked more interest in streaming solutions like Spark, Storm, Samza, etc. Even if organizations don’t think they need to deal with streaming data today, streaming capabilities could become standard operating procedures in the near future. For this reason, organizations should consider data processing such as Lambda architecture that can handle both real-time and batch data.
Flexibility
The big data that a company requires now may be considerably different from what it required a year or two ago. This is why many firms choose to look for technologies that may help them achieve several goals rather than just one.
Total Cost of Ownership
The upfront cost of big data applications is only a fraction of that. Organizations need to ensure they take into account the associated hardware costs, licensing or subscription fees being adopted, staff time, support costs, and any costs associated with the physical space to deploy applications on-premises. Don’t forget to take into account the fact that cloud computing costs generally decline over time.
Private vs open source big data applications
Some of the most popular big data tools, including the Hadoop ecosystem, are available under open-source licenses. One of the biggest draws of Hadoop and other open-source software is the lower total cost of ownership. While proprietary solutions require high licensing fees and may require expensive dedicated hardware, Hadoop has no licensing fees and can run on standard hardware. However, businesses sometimes find it difficult to obtain open-source solutions to meet their needs. They may need to purchase support or consulting services, which organizations need to consider when calculating their total cost of ownership.
On-premise or cloud-based
Another big decision a business needs to make is whether to host big data software in its own data center or whether it wants to adopt a cloud-based solution. Cloud-based big data applications are popular for a variety of reasons, including scalability and ease of management. Major cloud computing vendors are also leading the way in artificial intelligence and machine learning research, which allows them to add advanced capabilities to their solutions.
However, cloud computing is not always the best option for organizations. Organizations with high compliance or security requirements sometimes find that they need to keep sensitive data in on-premises data centers. Additionally, some organizations are already investing in existing on-premises data solutions, and they find it more cost-effective to continue running big data applications in on-premises data centers or use a hybrid approach.
Customer Support
Even experienced IT professionals sometimes find it difficult to deploy, maintain and use complex big data applications. Don’t forget to consider the quality and cost of support provided by each vendor.
Tips for choosing the right big data solution
Clearly, choosing the right big data application is a complex process involving many factors. Experts and organizations that have successfully deployed big data software offer the following advice:
Understand your goals
When selecting a Big Data solution, it’s important to first define what problem you need to solve. The solution must fit well with the business’s overall objectives and data strategy. If you’re not sure why you’re investing in technology, your project is unlikely to be successful.
Find the right balance
Many companies try to quickly solve their need with a solution that meets some of the technical capabilities but does not address all the business needs. It is critical to choose the right solution that provides the right balance of technical capability and business value to help organizations achieve a specific goal within a reasonable budget.
Start small
If a business can succeed with a small-scale big data project, there will be more interest in using the tool. While small-scale projects can help businesses gain experience and expertise in technology, it’s important to choose applications that can ultimately be used across the business.
Ask the right questions!
Oftentimes, the devil is in the details so it is important you ask the right questions in order to make the right choice when it comes to spending your organization’s precious budget on technology that will help move your business forward.
Here are a few questions just to get started with when choosing a big data solution:
- Can you get access to all the data you need?
Most big data solutions provide access to large amounts of historical and real-time data, but some providers offer more flexibility in terms of the type or format of data they will provide. For example, you may want to know your company’s sales by day, week, month, quarter or year; some providers may offer that information as well as a breakdown by product category and customer type.
- What kinds of analyses will be available?
While many Big Data providers offer a wide array of analytics tools, not all tools will be equally useful for your business needs. For example, if your goal is to measure customer satisfaction levels by region (or even within individual countries), then an analytics tool that offers only basic visualizations might not be enough for your needs; you’ll also want something that allows you to drill down deeper into specific metrics like customer satisfaction level by product category or customer satisfaction level by country.
- What other clients have you worked with?
This question is especially important because working with different clients will require the provider to brush up on a variety of skills, which may not be reflected in previous work. If service providers have multiple clients with vastly different needs, they likely won’t understand the intricacies of your business and may not be able to meet your specific needs.
Top Big Data Companies/Solution Providers
Alteryx
Alteryx is a self-service data analytics software company that specializes in data preparation and data blending. Alteryx allows users to organize, clean, and analyze data in a repeatable workflow. Business analysts find this tool particularly useful for connecting to and cleansing data from data warehouses, cloud applications, spreadsheets and other sources. The platform features tools to run a variety of analytic jobs (predictive, statistical and spatial) inside a single interface. Alteryx went public in 2017.
Company Address: 3345 Michelson Dr Irvine, CA United States, Phone: +1 (888) 836 4274 Website: www.alteryx.com
Key Clients of Alteryx
Red Hat Inc, Blackfriars Group, Arrow Electronics, Thomson Reuters, Adidas, Abbott, Amazon, ABM Investama, Ask.com, Audi, CBRE, Century Link
Key Partnerships
Alteryx has over 300 technology and channel partners, key partner names include; Accenture, Cloudera, PwC, Snowflake, Marketo, oracle, UI-Path, Adobe, Databricks, 7-Eleven, 4-Lab Technologies.
Vendor Factsheet
- A 22-year-old public company with over 2000+ employees
- Expertise in customer analytics, marketing science, and operations and risk
- Over 400 data scientists and big data engineers
- Customer count: 6,955 in more than 90 countries, including 38 percent of the Global 2000
- Headquartered in Irvine, Calif and has 18 additional global offices
- Specialized in Finance, manufacturing, legal and healthcare industry
Key Differentiator
Alteryx’s robust feature set and ease of use make it an excellent choice for ETL. With a set of already built-in predictive models designed for market analysis, it can be extremely useful for direct mail marketers wishing to gain clear insight into their audience. For those new to Alteryx, the platform is easy to get started with and many features work right out of the box. Alteryx also enables users to create modular workflows to help with data extraction, transformation, and loading. Additionally, the platform provides the tools needed to build interactive dashboards. Although it does require some understanding of data modeling and analysis to truly make use of its functionality, Alteryx provides an extremely rich set of documentation that grows as new users contribute templates. As a result, Alteryx is highly recommended for direct marketers.
The company’s extensive experience in advanced analytics and growing machine learning capabilities are key strengths that allow them to solve a wide range of business problems. Alteryx’s key differentiator is the ability to handle any size data set, no matter how big or small. The platform is also designed for ease of use, so that anybody can solve data and analytics problems, regardless of their skill level. Alteryx’s extensive experience has allowed the company to gain blue-chip clients and establish a strong foothold in the market. Alteryx’s solutions are best suited for organizations where analytics is a strategic and mature function that is present and valued across all operations. Alteryx’s emerging differentiation seems to be its ability to provide users with an end-to-end analytics experience and its talent development and culture.
Big Data Solution Portfolio
Services:
– Advanced analytics including ML algorithms to derive value from complex and big data
– Business Insights
– Data Engineering
Key tools
Alteryx Designer:
Key features of Alteryx Designer include a drag-and-drop interface for data blending and a wide range of advanced analytics tools that make it easy to uncover insights in data. Plus, it comes with all the features you need to get the most out of your data, including support for big data sources, predictive analytics, and more.
Alteryx Machine Learning Platform:
Alteryx’s machine learning and predictive analytics visualizations provide an accessible way to gain insights from data without requiring code. The Knowledge Studio includes pre-built data preparation and data science functions that make it easy to get started and integrate with common programming languages for more advanced users.
Alteryx Server-FIPS:
Alteryx Server-FIPS is a powerful tool that helps organizations govern workflow processes, schedule workflows, and scale analytics. It provides centrally managed security and sharing, making it easy for organizations to protect data and ensure compliance with regulations.
Strengths
Superb User Experience And An Outstanding Community:
A very natural learning experience with quick adoption (short initial learning curves) for most users due to excellent visual design and logical grouping of tools on the canvas. Clear, simple, and concise tool configurations that are quick and easy to set. The five-year-old Alteryx community website offers features such as badges, tracking of activities, and other customizations that allowed it to recently win a very prestigious award: Alteryx Community Wins CMX 2021 Community of the Year. There is an extensive gallery of user-developed analytic applications that reduce development time.
Excellent Computational Speed:
Alteryx has tools such as the Calgary engine that make it very effective when working with big data sets. Data loading and retrieval are both speedy. Alteryx minimizes the movement of data, which produces high-speed computational performance.
Programming and Extensibility:
Ability to write comprehensive custom programs that have excellent performance and long-term stability. Ability to write standard, batch, and iterative macros for incremental processing of data.
Comprehensive Geospatial Analysis:
Alteryx was created with a geospatial processing focus, which allows it to operate similarly to a geographical information system (GIS) without the added expense of a GIS. The accuracy of the geospatial computations has been documented to be of the highest caliber when tested by third-party vendors.
Original article link: https://alltechmagazine.com/big-data/
