Data is an asset in today’s business world. No wonder almost all companies are aggregating it from as many sources as possible. The importance of Data and Data Management Tools is tremendous for the business community, and they have acknowledged it. To measure data against the right metrics and expected outcomes, data analytical tools are used extensively. Studies suggest that the revenue from data analytical tools increased by 50%, to around $187 billion in 2019. It is essential to leverage big data management tools and techniques, as it is proving vital for customer acquisition and competitive growth. As big data management tools collect, sort, and analyze data and thereafter provide information and predictions, it can be effectively used to predict future trends.
Why Data Analysis
- By analyzing data, you can get a clear picture of consumer preferences, competitive landscape, growth potential, and more.
- Free and open-source analytical tools can be leveraged to enhance the company’s business at no extra cost.
Which Data Management Tools to use?
By using data analytics tools, you get to utilize a lot of built-in functions rather than having to design the same from scratch. It not only accelerates the value you derive from Big Data analytics, but you also get better quality insights in a quick time. Below are some of the widely used data analysis tools. We have tried to arrange these tools at each stage of the Big Data analytics process–this consists of ETL (data preparation), data analysis (actual number crunching), and data visualization (transforming numbers to actionable insights for business management). These tools can help your brand grow sharply at minimal or no expense:
Stage 1: ETL Stage
In Data Analytics, ETL is a process in which Data is collated from the source system and transferred to a Data warehouse. It is the primary step in the Data analytics chain. The various tools available for ETL are:
It is a Data integration platform that consists of a family of products related to Data analytics. It has massive parallel processing (MPP) capabilities which can deliver a hugely scalable and flexible platform to process multiple varieties of Data volumes. Not only it helps to collect the data but it also has the ability to check the quality of the data. The tool has been widely used by businesses from the time it was introduced. A recent survey conducted by Gartner gave it 4.2 stars out of 5.
SAS Data Management is a program that helps to cleanse, monitor, transform, and secure data and also helps to improve the quality and reliability of the data. The tool helps to deploy, extract, transform, and load architecture to leverage native source and target platform capabilities. The platform provides 300 pre-built SQL-based transformations that have the ability to insert, merge, and delete data from different sources and targets.
PowerCenter Informatica is an enterprise product from Informatica which provides a platform for Data integration. It is a fully scalable and high-performance product. Since it’s very accessible, it is quite popular among the Data Analytics community. Its ability to manage a broad range of data integration activities on one platform has made it vastly popular. Its ability to simplify the process of data marts and data warehouses is truly amazing. PowerCenter has also helped many companies to successfully collaborate the business aspect with the IT part. The main features of PowerCenter Informatica are:
- Data Masking
- Metadata management
- Seamless recovery
- Dynamic partitioning
Data Analytics Stage
The amazing ability of Big Data to dramatically enhance the customer experience and transform business models has made it one of the premier innovations of the current decade. For a company to use its data to the best advantage, it has to convert the data into knowledge. Data analytics tools come in to picture here. For example, manufacturing companies can monitor data coming from machine sensors and with the help of Data analytics, can modify the machine processes before a catastrophic event happens. After going through the importance of Big Data Analytics, here are some popular tools which you can deploy in your systems:
By providing business data analytics in a single seamless platform, Pentaho tries to solve the various challenges around Data Integration. Pentaho can be the future of Data Analytics, as its open-sourced software is easily available and simple to use. Its embeddable platform can handle all requirements, including diverse and Big Data projects. Pentaho constantly innovates and adds new features to its modern and integrated platform. Pentaho was the first major vendor to introduce a big data analytics tool. Since then, it has always taken a leadership position in big data analytics. By taking advantage of its early entry and its continuous technological updates, Pentaho has been able to garner a big chunk of data analytics customers. With its simple yet powerful product, companies can save a lot of time in designing and deploying big data analytics solutions. Services across the spectrum including financial, retail and healthcare use Pentaho’s product.
5) Skytree Server
Skytree Server is an AI-based machine learning program that is intended to be a replacement for SQL databases. By taking unstructured sets of data and crunching them faster than any other traditional method, it has become quite popular among businesses. Its learning algorithms are used in multiple industries for a wide range of applications including sales, fraud detection, astronomy, etc. The predictive behavior of the tool is quite effective in forecasting future trends. The application automatically adjusts the algorithms needed through its machine learning technique. There is also a free trial version of the software made available by the company.
Data Visualization Stage
By placing the crunched data in a visual context, it becomes much easier for people to understand. This is the USP of Data visualization. By placing the information in a graphical format, the information is communicated clearly and efficiently. It is the final step on the Data analytics ladder. Some of the popular and important tools used for Data visualization are:
Tableau is a data visualization tool that helps businesses to understand and comprehend data in a graphical format within a span of a few minutes. It’s simple to operate but has a quite robust architecture. Tableau helps businesses to create interactive and dynamic dashboards. By filtering out uninteresting data topics, a user can save plenty of time and resources. The two major products from Tableau company are Tableau Desktop and Tableau Server.
Highcharts is one of the most popular big data visualization products and is used by big brands like BBC, Twitter, IBM, Cisco, Facebook, etc and many more. Highcharts support various types of charts like spline, areaspline, bar, pie, column, and scatter charts. It works perfectly well with environments that do not support Flash (iOS tab devices) and has robust HTML5 compatibility. Its plot options is a great way to set the default ‘look and feel of a huge number of charts to be developed for different data. The best thing is that this default setting can be overridden with ease. Hence any customization to is easily achievable.
Microsoft’s Power BI can connect with organization-wide data sources and ensure simplified data preparation. It can carry out custom analytics and deliver rich, insightful, and stunning visual reports and dashboards. Features like Power Query (to connect diverse data sources), DAX and Merge transformation, and Power View (to create interactive charts, maps, and graphs) amplify the value you derive from Microsoft BI for data visualization. You can opt for the on-premise edition or the cloud version, depending on your specific business needs. 10) QlikView This offering from Qlik covers more than 40,000 customer accounts spanning more than 100 nations. Its neat and zero clutter interface provides rich capabilities not only in data visualization but also in BI and analytics. It also integrates well with its compatriot, Qliksense, for data discovery and data preparation. You can utilize its strong community support to improve the level and visual appeal of charting you can depict for your dataset.
Although Big Data may look quite a complex terminology from the outside, once you read a little about it you would know it’s not that hard and easily implementable. You don’t have to be a Data scientist to implement it in your businesses, just use the above data management tools can save the day for you. Are you utilizing any other Big Data Management tools that are offering substantial yields to your business? Do let us know through the comments section below.