What better way to describe the state of big data analytics in 2020 than with numbers? The website Kommando Tech provides various statistics that demonstrate the tremendous influence big data analytics has on the way business does business.
- According to IBM, 2.5 quintillion bytes of data are generated by people worldwide each day.
- Forbes states that 65% of global enterprises will increase their spending on big data analytics in 2020.
- Global revenue from big data and business analytics increased from $122 billion in 2015 to $189 billion in 2019. It is projected to grow to $274 billion by 2022 as reported by PCMag.com.
- A Forbes survey of executives found that adopting big data and artificial intelligence remains a “major challenge” for 77.1% of businesses.
Few technologies are as daunting to businesses, or as necessary, as big data analytics. Fortunately, a new generation of big data analytics tools makes embracing the latest advances more straightforward. The goal of modern business analytics products is to deliver the benefits of big data, machine learning, and other advanced techniques directly to business decision-makers—no data scientist required.
Because the market for big data analytics tools is relatively new, many companies have no clear idea of which tools are the best fit for their specific needs. This buyer’s guide explains the services provided by the top big data analytics tools and assesses their benefits. It highlights the techniques the products use to help businesses spot emerging trends, as well as the types of businesses and organizations each product is geared toward.
Tableau: Bringing Big Data Analytics to End Users
Tableau offers a range of big data analytics tools for individual analysts, for teams and organizations, and for companies that want to embed Tableau’s analytics tools into their own products. The company states that its products for groups are intended to “turn people’s collective intelligence into a competitive advantage.”
How it works
Tableau’s three primary products are Tableau Desktop, Tableau Server, and Tableau Online. The Desktop version enables users to create interactive dashboards to help them quickly find and organize data, as Technology Advice explains. Data visualizations are created automatically by dragging and dropping elements. Tableau Server is a browser-based version of Desktop, and Tableau Online is hosted on the customer’s own server to enhance security.
Unique benefits and advantages
Tableau is able to connect to hundreds of data sources on the customer’s premises and in the cloud, which G2 states makes starting the analytics process easy. The product also supports natural language queries, which allows people without analytics experience to quickly gain insight into business problems. Tableau can be used to create powerful calculations, view trend lines, and generate statistical summaries.
Examples of how it may be used
Guru99 describes Tableau’s three best features as data blending, which merges data from multiple sources into a single data set regardless of file formats and other variables; real-time analysis, which allows data to be included in analyses as soon as it is available; and data collaboration, which Crossbeam describes as the use of data to enhance partnerships, alliances, joint marketing efforts, and strategic initiatives.
Xplenty: A Tool for Establishing Data Pipelines
Xplenty describes itself as “a complete toolkit for building data pipelines.” The product supports connections to more than 100 types of data stores and software-as-a-service (SaaS) applications, as SourceForge explains. It does so via Extract, Transform, Load (ETL); or Extract, Load, Transform (ELT), as well as through a replication. All three processes are accomplished using an intuitive graphical interface.
How it works
Data analytics requires the ability to tap huge data stores to find small nuggets of information that will help employees make critical business decisions. Xplenty is used to establish connections to these data stores by extracting the raw data, transforming it to a format the an analytics engine supports, and loading it onto the user’s system (ETL). In some cases, the transformation is performed after the data has been loaded (ELT), and in other cases there is no need to transform the data (replication).
Unique benefits and advantages
The data a company collects and stores comes in many different formats and forms. Some of these forms are structured, such as SQL databases, while others are unstructured, including email, text documents, videos, and photographs. Xplenty makes it possible for employees to connect their business analytics tools to these large and diverse data stores via a graphical user interface (GUI). It also integrates well with the data tools that are used by developers.
Examples of how it may be used
Companies frequently collect data from Facebook and other social media as a key part of their marketing efforts. However, it is often difficult to rely on social media application programming interfaces (APIs) to extract this data and integrate it with data from other sources. Xplenty simplifies the process by automating the extraction and transformation of the social media data before it is loaded into an analytics engine.
Microsoft Azure HDInsight: Cloud-based Analytics
Microsoft’s Azure HDInsight is a cloud-based analytics engine for enterprises. The open source product is intended to make vital enterprise data available to as many people in an organization as possible, as Predictive Analytics Today explains. It benefits from close integration with Microsoft’s Power BI for creating interactive data visualizations.
How it works
Big data solutions that are designed for enterprises can be deployed on premises or in the cloud. Azure HDInsight applies an analytics engine to various cloud platforms and tools. It works with the leading open source cloud frameworks, including Hadoop, Apache Spark, Apache Hive, LLAP (Live Long and Process), Apache Kafka, Apache Storm, and R.
Unique benefits and advantages
Azure HDInsight is noted for its ability to cost-effectively accommodate massive amounts of data. ClearPeaks points out that HDInsight’s native file system uses either Apache Data Lake Store or Apache Blob Store, but data can be imported from and exported to a variety of sources. Noteworthy features include the ability to reduce costs by scaling workloads up and down and creating clusters on demand. The product integrates with Azure Monitor logs to manage clusters via a single interface.
Examples of how it may be used
Big data analytics relies on both historical data that is kept in storage and real-time data that streams into the system as it is collected. Azure HDInsight works with both types of data sources. It applies structure to unstructured data and makes it available for data science and data warehousing applications. Data warehouses can be queried interactively, and models can be created for connecting to business intelligence (BI) tools.
SAP HANA: Analytics to Support Business Decisions
SAP promotes its HANA big data analytics tool as a way to apply real-time data to inform a range of business decisions using a single data copy. The product saves companies money and streamlines their data operations by eliminating data redundancy and reducing their IT footprint and hardware expenses. As ZDNet explains, SAP is working to make HANA cloud-native to take advantage of the cloud’s elasticity and scalability.
How it works
SAP HANA brings the power of in-memory database services to big data analytics, which speeds up transactions. The product efficiently manages large database volumes by using multitenant database containers and dynamic tiering. The system’s in-memory analytics processing supports text, predictive, spatial, graph, streaming, and time-series analyses.
Unique benefits and advantages
HANA provides businesses with a comprehensive and accurate view of their data processes by combining data from internal and external sources. The data can be accessed where it is located, integrated with other sources, or replicated to ensure consistency. HANA also simplifies administration by automating process monitoring and ensuring continuous data availability.
Examples of how it may be used
Companies rely increasingly on real-time data and in-memory databases to guarantee that business decisions are based on the latest information. HANA’s processing speed and real-time data feeds support data-intensive analytics operations such as live spatial intelligence that gives business data “situational awareness.”
Cloudera: A Hadoop-based Data Platform for Enterprises
Cloudera refers to its Data Platform as “the first enterprise data cloud.” It supports “valuable and transformative business use cases,” including predictive maintenance, genomics research, and real-time compliance monitoring. The Hadoop-based Cloudera Data Platform offers self-service analytics capabilities that run on hybrid and multi-cloud environments.
How it works
The components of the Cloudera Data Platform include data warehouse and machine learning services, as well as the Data Hub service for creating custom business applications. The product’s interface lets users control infrastructure, data and analytics workloads in various cloud environments. Cloudera’s Shared Data Experience (SDX) component provides shared data services that support accurate self-service analytics at a low cost.
Unique benefits and advantages
Finances Online highlights one of Cloudera’s advantages: the company’s technology partners include Intel, SAP, Google Cloud Platform, Dell, and Cisco. This gives Cloudera access to the latest technologies that boost performance and analytics capabilities. While using Hadoop can be complicated, the Cloudera Manager feature simplifies administration of the environment by automating resource, service, and configuration management.
Examples of how it may be used
Enlyft reports that Cloudera is used primarily in the computer software and information technology sectors. ZDNet states that the company’s decision to make the Cloudera Data Platform cloud-native, combined with the SDX, makes the product a good choice for analytics applications that require data-specific governance, access control, and tracking and auditing functions.
Oracle Brings Self-Service Analytics to Its Big Data Management Platform
Oracle has been a leading database vendor since the earliest days of the industry. The Oracle Big Data Management Platform combines several products, including Big Data Analytics, which features Oracle Analytics, Oracle Data Science, Oracle Big Data Spatial and Graph, and Oracle Machine Learning.
How it works
Oracle Analytics provides enterprise employees with self-service analytics that automate data preparation, data visualization, enterprise reporting, and augmented analysis. It uses both machine learning and natural language processing to facilitate collaboration with stakeholders in other areas.
Unique benefits and advantages
Oracle Analytics enables an enterprise to deploy global data transformation policies, consistently monitor and respond to business metrics, and unify business and IT toward a common set of goals. Predictive Analytics Today explains that the product allows companies to take full advantage of cloud-based big data features: scalability, reliability, and resilience.
Examples of how it may be used
Enterprises use Oracle Analytics to visualize and forecast behavior inside and outside the organization in real time. This enables companies to respond faster to market and industry trends. In addition to more streamlined business processes, Oracle Analytics provides a unified, integrated analytics architecture that enables employees to apply geospatial insight to improve customer experiences.
SAS Viya: A Cloud-based System Management Suite
Big data analytics is a key component of SAS Institute’s Viya cloud-based unified system management environment. Among Viya’s many products are SAS Data Preparation, SAS Visual Analytics, SAS Visual Data Mining and Machine Learning, and SAS Visual Text Analytics.
How it works
SAS presents the Viya platform as an “analytic ecosystem” that offers enterprise users access to any type of data regardless of complexity, size, format, or source. The system automates data preparation, creates powerful and efficient data models, and supports a range of analytics techniques and programming languages.
Unique benefits and advantages
ZDNet notes that SAS has been a leader in business intelligence for almost three decades, and Viya keeps the company at the forefront of the market by smoothly integrating all the self-service analytics features that enterprise users require. The product is easy enough for business analysts to use, yet powerful enough to meet the needs of data scientists.
Examples of how it may be used
An example use for Viya represents the ability of SAS Event Stream Processing (ESP) to ingest and transform real-time cancer research data and integrate it with stored data. Analytics tools from SAS or other vendors can tap the resulting data pipeline. A typical business application includes use of SAS Intelligent Decisioning to model customer next-best-offer scenarios.
IBM Big Data Is Part Products, Part Partnerships
IBM takes a slightly different approach to meeting the big data analytics needs of its customers. It offers a version of Cloudera’s Hadoop analytics product that integrates with IBM’s existing data services for enterprises. IBM also sells its own big data analytics tools: IBM Db2 Big SQL, IBM Big Replicate for Hadoop, and IBM Analytics for Apache Spark.
How it works
Db2 Big SQL is a SQL-on-Hadoop engine that offers high performance via massively parallel processing (MPP) as well as advanced data querying functions. Big Replicate for Hadoop is a data-replication platform that ensures data consistency across distributed environments, including on-premises, hybrid cloud, and SQL and NoSQL databases. Analytics for Apache Spark is an in-memory computing engine that IBM claims processes workloads 100 times faster than Apache Hadoop.
Unique benefits and advantages
IBM big data analytics tools are noteworthy for their power and speed. SelectHub states that IBM’s big data offerings improve marketing by using predictive modeling to better target customers. They also optimize supply chains by applying real-time insights about suppliers and customers. Additionally, they enhance risk management by detecting and responding to fraud and other threats.
Examples of how it may be used
Enterprises use IBM’s suite of big data analytics products to create models of business processes. This enables firms to monetize more data, predict their customers’ behavior, inform strategic decisions, and optimize all aspects of their operations. For example, to reduce the cost of data querying, Db2 Big SQL includes a cost-based optimizer that automatically rewrites queries so the execution plan is optimized based on data location, table, and column statistics, as the IBM Big Data & Analytics Hub explains.
Deloitte AI and Analytics: Big Data Analytics from an Accounting Perspective
Deloitte, one of the Big Four accounting firms along with Ernst & Young (EY), PriceWaterhouseCoopers, and KPMG, offers its own AI and Analytics services as part of the company’s artificial intelligence initiatives. Emerj explains that rather than being marketed as a stand-alone big data analytics tool, Deloitte’s analytics approach focuses on applying AI to increase a company’s productivity by streamlining workflows and automating operations.
How it works
Deloitte’s analytics strategy emphasizes tailoring solutions to specific industries and sectors. It also strives to make data from diverse internal and external sources available to employees at every level of the organization. Through a partnership with IBM Watson, the company offers its customers analytics solutions that are enhanced with the latest cognitive technologies.
Unique benefits and advantages
Deloitte’s analytics services tap the expertise of the company’s in-house data scientists, data architects, and business and domain specialists. It maintains a ventures fund intended to drive advances in analytics technologies and keep the firm at the forefront of the industry. Deloitte has established a worldwide network of delivery centers to support clients at more than 80 locations.
Examples of how it may be used
Among the applications of Deloitte’s AI and Analytics is their ability to quickly identify consumer trends to gain a “first mover” advantage. This enables companies to predict interruptions to supply chains, for example, and anticipate fraud before it occurs to discourage potential perpetrators. Services are designed to forecast sales accurately enough to enable companies to adjust production on the fly, and speed business decisions by collecting and analyzing information about the company in real time.
What the Future Holds for Big Data Analytics
The pace of business shows no signs of slowing. The winners will be the companies that are able to harness the potential of big data analytics tools and other AI-based technologies to gain and maintain an edge over their competition. Workers will benefit from the ability to make business decisions based on complete, relevant, and up-to-date intelligence about customers, competitors, markets, and industries.
Additional Resources:
Built In, “The 13 Big Data Analytics Trends Industry Pros are Watching”