In modern times, with digitalization across the globe data is the biggest asset for any company. Deep data means a chunk of high-quality data that is relevant and actionable. This ‘data’ answers and solves problems. Such huge data are always difficult and time-consuming to filter manually as per the need hence many tools help in analyzing deep data. Let us talk about the best 10 analysis tools for managing ‘deep data’.
1. Xplenty – It is a cloud-based ETL solution that analyses, manages, and transforms high-volume data without coding resources in few minutes. It is ideal for businesses with high volume data as this not only manages such nig data in minutes but also manages effectively without any error. This is a complete data analysis toolkit.
2. Zoho Analytics – Zoho Analytics operates on Windows, Mac, Linux, Android, iOS, and cloud. This is an Indian-based company, dedicated to offering solutions to data management for companies and business houses. There is a free plan; however, the basic plan starts at $22 per month.
3. Sprinkle – It is a cloud-based solution that helps with reports, segments, and dashboards with much ease. It is user-friendly as this does not call for high intelligence while operating it.
4. Hubspot – This software company helps in analyzing data, generating leads, marketing automation, and more. With the purpose to help the business houses grow, this company has a revolutionary inbound marketing technique. Its price starts from $40 per month.
5. Tableau Public – This is a free platform to share and visualize data online. With data visualizations, this platform makes data a part of everyday life. The features are good and with full functionality.
6. Rapid Miner – With an easy system and powerful GUI, this software is a data science platform that helps organization drives revenue, reduce costs and avoid risks. It concentrates on people, performance, and productivity.
7. KNIME – This software platform is free, open-source software for creating data science. It access, merge and transform data to derive solutions to business problems. It supports enterprise-wide data science practices and gives insights into the results derived.
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