Data analytics tools are, to be sure, in great demand. A May 2017 story in The Economist declared that data is now more valuable than oil. While it can’t run your car, data nonetheless is a key commodity that many of the world’s biggest businesses run on and is the life blood of many corporations.
In treating data as an asset, that means the tools to perform data analytics are just as vital to the business, because without analytics you have no context, no knowledge. You just have data, which, like raw petroleum, is useless unless it is refined.
The big data and analytics market is expected to jump from $122 billion in 2015 to $187 billion in 2019, according to IDC. There are players big and small in this market that automatically collect, clean, and analyze data. Others deliver information and if they are really good, predictions. Predictive analytics are the riskiest of analytical arts but potentially the most rewarding. That’s why they are the hardest to do.
What Is the role of data analysis? While the term "data analysis" seems self-explanatory, it’s also fairly generic in that it could mean any one of a lot of things. It’s usually used in the context of business intelligence (BI).
In BI, data analytics tools are often the final step in the chain of gathering, structuring, and processing data. The process starts with unstructured data and ends with actionable intelligence. Data examination is a part of predictive modeling. You can’t make predictions without examining the past, even in situations where past performance is no indication of future activity. That’s where the top tools come in.
What follows is a list by no means complete, but a comprehensive list of the different data analytics tools available. Some are free, others with a fee. They are in no particular order.
Leading Data Analytics Tools
Probably not the first thing that comes to mind, but Excel is one of the most widely used analytics tools in the world given its massive installed base. You won’t use it for advanced analytics to be sure, but Excel is a great way to start learning the basics of analytics not to mention a useful tool for basic grunt work. It supports all the important features like summarizing data, visualizing data, and basic data manipulation. It has a huge user community with plenty of support, tutorials and free resources.
IBM’s Cognos Analytics is an upgrade to Cognos Business Intelligence (Cognos BI). Cognos Analytics has a Web-based interface and offers data visualization features not found in the BI product. It provides self-service analytics with enterprise security, data governance and management features. Data can be sourced from multiple sources to create visualizations and reports.
R has been around more than 20 years as a free and open source project, making it quite popular, and R was designed to do one thing: analytics. There are numerous add-on packages and Microsoft supports it as part of its Big Data efforts. Extra packages include Big Data support, connecting to external databases, visualizing data, mapping data geographically and performing advanced statistical functions. On the down side, R has been criticized for being single threaded in an era where parallel processing is imperative.
3) Sage Live
Sage Live is a cloud-based accounting platform for small and mid-sized businesses, with features like the ability to create and send invoices, accept payments, pay bills, record receipts and record sales, all from within a mobile-capable platform. It supports multiple companies, currencies and banks and integrates with Salesforce CRM for no additional charge.
Sisense’s self-titled product is a BI solution that provides advanced analytical tools for analysis, visualization and reporting. Sisense allows businesses to merge data from many sources and merge it into a single database where it does the analysis. It can be deployed on-premises or hosted in the cloud as a SaaS application.
Chart.io is a drag and drop chart creation tool that works on a tablet or laptop to build connections to databases, ranging from MySQL to Oracle, and then creates scripts for data analysis. Data can be blended from multiple sources with a single click before executing analysis. It makes a variety of charts, such as bar graphs, pie charts, scatter plots, and more.
SAP’s BusinessObjects provides a set of centralized tools to perform a wide variety of BI and analytics, from ETL to data cleansing to predictive dashboards and reports. It’s modular so customers can start small with just the functions they need and grow the app with their business. It supports everything from SMBs to large enterprises and can be configured for a number of vertical industries. It also supports Microsoft Office and Salesforce SaaS.
Netlink’s Business Analytics platform is a comprehensive on-demand solution, meaning no Capex investment. It can be accessed via a Web browser from any device and scale from a department to a full enterprise. Dashboards can be shared among teams via the collaboration features. The features are geared toward sales, with advanced analytic capabilities around sales & inventory forecasting, voice and text analytics, fraud detection, buying propensity, sentiment, and customer churn analysis.
Domo is another cloud-based business management suite is browser-accessible and scales from a small business to a giant enterprise. It provides analysis on all business-level activity, like top selling products, forecasting, marketing return on investment and cash balances. It offers interactive visualization tools and instant access to company-wide data via customized dashboards.
Style Intelligence is a business intelligence software platform that allows users to create dashboards, visual analyses and reports via a data engine that integrates data from multiple sources such as OLAP servers, ERP apps, relational databases and more. InetSoft’s proprietary Data Block technology enables the data mashups to take place in real time. Data and reports can be accessed via dashboards, enterprise reports, scorecards and exception alerts.
Dataiku develops Dataiku Data Science Studio (DSS), a data analysis and collaboration platform that helps data analysts work together with data scientists to build more meaningful data applications. It helps prototype and build data-driven models and extract data from a variety of sources, from databases to Big Data repositories.
Python is already a popular language because it’s powerful and easy to learn. Over the years, analytics features have been added, making it increasingly popular with developers looking to do analytics apps but wanting more power than the R language. R is built for one thing, statistical analysis, but Python can do analytics plus many other functions and types of apps, including machine learning and analytics.
12) Apache Spark
Spark is Big Data analytics designed to run in-memory. Early Big Data systems like Hadoop were batch processes that ran during low utilization (at night) and were disk-based. Spark is meant to run in real time and entirely in memory, thus allowing for much faster real-time analytics. Spark has easy integration with the Hadoop ecosystem and its own machine learning library. And it’s open source, which means it’s free.
13) SAS Institute
SAS is a long-time BI vendor, so its move into analytics was only natural. to be widely used in the industry. Two of its major apps are SAS Enterprise Miner and SAS Visual Analytics. Enterprise Miner is good for core statistical analysis, data analytics and machine learning. It’s mature and has been around a while, with a lot of macros and code for specific uses. Visual Analytics is newer and designed to run in distributed memory on top of Hadoop.
Tableau is a data visualization software package and one of the most popular on the market. It’s a fast visualization software which lets you explore data and make all kinds of analysis and observations by drag and drop interfaces. Its intelligent algorithms figure out the type of data and the best method available to process it. You can easily build dashboards with the GUI and connect to a host of analytical apps, including R.
Splunk Enterprise started out as a log-analysis tool, but has grown to become a broad based platform for searching, monitoring, and analyzing machine-generated Big Data. The software can import data from a variety of sources, from logs to data collected by Big Data applications such as Hadoop or sensors. It then generates reports a non-IT business person can easily read and understand.