Tools and Technologies for Data Analytics

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Tools and Technologies for Data Analytics

Author: Palak Kumar

Analytics has unleashed the power of data by uncovering hidden patterns to derive valuable insights and leverage the power of data to transform the business and gain a competitive advantage. For example, data analytics can help us understand customer behaviour, perform predictions, and improve advertising campaigns. In order to unlock the potential of the data we need certain tools and technologies to analyse the data and make meaningful conclusions. In this article, we will look at some of the tools and technologies that enable us to leverage the power of data analytics.

Technologies that makes data analytics powerful

When we talk about technologies required for data analytics, we primarily are looking at four technologies that are needed to make this happen. The first is data management to ensure that we are properly managing the large data sets that we may be required to analyse. The second is data mining to be able to look into this data to understand what the data is telling us. The third is techniques such as machine learning to be able to process to data to uncover hidden patterns. Finally, technologies such as predictive analytics to be able to take the data and models to be able to derive value from it.

Data management Before analysing data, we need to have certain procedures set up to manage the flow of data in and out of the systems and keeping our data organized. We also need to make sure that our data is of high quality. Establishing a data management program helps to ensure that the organization is on the same page regarding how to organize and handle data.
Data mining Data mining is the process of sorting out large amounts of data to identify patterns and discover relationships between data points. Data mining helps us to sift through large datasets and figure out what is relevant. We can then use that information to conduct analysis and make informed decisions. Data mining technologies help us to complete these tasks very quickly.
Machine learning Machine learning is a subset of Artificial Intelligence (AI) and describes those algorithms that can learn by themselves. Machine learning allows applications to learn from the data and predict outcomes without someone explicitly programming the system to reach that conclusion. We can train a machine learning algorithm on small sample data and then the system will continue to learn as it gathers more and more data and it will become more accurate as time goes on.
Predictive analytics These technologies help analyse historical data and predict future outcomes. Higher the accuracy of the predictions the better are the decisions made by organizations. Predictive analytics is one of the primary outputs of data analytics. It allows businesses to understand their customer’s concerns and needs and predict future trends to stay ahead of the competition.

Tools for data analytics

With increasing use cases of data analytics, many tools have emerged with various functionalities that ease things out. You will find both open sourced as well as commercial software available in the market. Each tool has its own set of strengths and weaknesses, so you need to do a careful analysis before you decide which tool or technology you are going to use for your projects. Here is a list of some top tools that are used for Data Analytics:

R programming R is the most popular tool in the analytics industry. It can run on various platforms such as UNIX, Windows, and Mac OS. R provides tools to automatically install basic packages and then grow the libraries required to perform various analysis as per the user requirement. Over the years, R has become robust and can handle large data sets. There are over 8000 packages in R, adding a lot to its capabilities. R can also be integrated with many big data platforms. This makes it versatile and highly useful. On the flip side, R requires users to understand how to write commands using the R script programming language. The second limitation is that since R is an open-sourced application developed by 100’s of free developers around the world, sometimes the software may not work well together as new versions are released.
Python Python is an open-source object-oriented programming language. Python is easy to read write and maintain. It has been a favourite language among programmers because it is easy to learn. Now it has developed into a very powerful Analytics tool with many analytical and statistical libraries such as Scikit-learn, TensorFlow, Matplotlib, Pandas, Keras etc. It offers comprehensive coverage of statistical and mathematical functions. Similar to R software, users are required to develop the programs in the Python language. On the flip side, Python is slow and not a good choice for memory intensive tasks. It has limitations with database access and is not good for multi-processor work.
Apache Spark Spark is also an open-source processing engine that is built for Analytics especially for unstructured data and huge volumes of data. Spark has become Highly popular in the last few years due to many reasons, one of them being easy integration with the Hadoop system. This tool executes its applications in Hadoop clusters about 100 times faster in memory and 10 times faster on disk. Spark has its own machine learning library that makes it ideal for Analytics. On the flip side, it has no automatic optimization process and has fewer algorithms compared to the competition.
Pig and Hive Pig and Hive are integral tools in the Hadoop ecosystem that reduce the complexity of writing MapReduce queries. Pig and Hive, both languages are like SQL. Most of the companies that work with big data and use the Hadoop platform use Pig and Hive. Hive is a data warehouse system which is used for analysing large datasets. Hive uses query language called HiveQL. Pig is also used for large datasets and uses Pig Latin language. It is usually recommended to use Hive for large projects which can implement SQL like data access and Pig is a good language for immediate tasks or small projects.
SAS SAS is the most widely commercial software in the analytics industry. It is versatile, robust, and easy to learn the tool. SAS is a programming language and environment for data manipulation and analytics, this tool is easily accessible and can analyse data from different sources. It has added many new modules. Some of the specialized modules that have been added recently are-SAS Anti-money Laundering, SAS analytics for IoT, and SAS Analytics Pro for Midsize Business. The flip side of this software is that it is expensive and not open source. It has limited graphical representation. SAS is a procedural language and, in some cases, require more lines of coding than R.
Tableau Tableau is easy to learn and effective tool that does the job of slicing and dicing the data and creating excellent visualizations and dashboards. It can create much better visualizations as compared to excel and can handle large amounts of data. It connects to any data source like Excel and corporate data warehouse. It can also give real-time updates on the web. It is mobile-friendly and easy to use and upgrade and provides high performance. On the flip side, it is expensive, requires IT assistance for proper use. It does not have automatic refreshing of reports and has poor version control. It is not a comprehensive solution for all your analytics needs.
Qlikview QlikView offers in-memory data processing and delivers the results to the end end-users quickly. This tool also offers data visualization and data association with data being compressed to almost 10% of its size. QlikView is slightly faster than tableau and gives users more flexibility. Different teams can collaborate using this tool and can enable a self-service BI tool. The limitations are that it can be inefficient at times and end-user application development requires technical expertise. It is more affordable compared to few other BI tools but requires lot of extra purchases for additional functionality.
Splunk It started as a ‘Google for log files’ that means its primary use was to process machine log files data. However, now it has become much more than that. It can be used to analyse log files, generate reports, and develop forecasts and has a great dashboard/visualization. The limitations are the complexity of install and maintenance of the infrastructure and stability of some components. It could have a high learning curve.
Microsoft Excel Excel is one of the most widely used tools in Data Analytics and is usually used for the client’s internal data. This tool analyses the tasks that summarise the data with the preview of pivot tables. However, Excel is limited with respect to the amount of data one can analyse with this software and currently available use cases for performing data analytics is rather limited.
Sigma Magic Sigma magic is comprehensive and easy to use analysis software to improve business performance. Sigma magic software works on top of the Microsoft Excel platform and leverages the familiarity and ease of use of Microsoft Excel. Most users are already familiar with Excel. For the analytics functionality, Sigma Magic leverages the R software at the back end to perform advanced computations. Hence, the users can benefit from using a platform they already know and also leverage the R software at the back end to perform advanced analytics without having to learn the R script programming language. The limitation of this approach is that you can only handle a maximum of 1 million records due to the Excel software limitation.

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