Is Deep Data Analysis Changing the Meaning of Business Intelligence?

A Deep Data is referred to the high quality big data which are relevant as well as actionable. Therefore, A Deep Data Analysis has been considered as one of the applications of sophisticated data processing through which information from multiple data sources are yielded. There are four types of Deep Data Analysis which are: Predictive Data Analysis, Prescriptive Data Analysis along with the Diagnostic Data Analysis and Descriptive Data Analysis as well. These are not only structured data but also contain some of the semi structured data as well. A deep data analysis has an involvement of precise and complex queries. It has therefore a requirement of the real time responses. Deep Data Analysis has a deep association with cloud computing. Famous frameworks such as MapReduce have been managed through workload distribution. Columnar Databases along with the in-memory Analytics have been greatly used in order to speed up the processes of approach. Sectors which have greatly used this deep analytics are: the Financial Sector, the Scientific Community along with the Biomedical Industries. In some of the recent years, there has been a great usage of the practices within the enterprise.

Another name of this Deep Data Analysis is: Big Data Analytics. It has been known that eighty percent of the global data has contained different text, videos and images. Analysis of the data cannot take place without any typical traditional approaches. The founders of the Deep Data Analysis have been greatly influenced by the meaningful insights from the social media campaigns and therefore developed an all in one machine learning solution for the sake of rating the effectiveness of influencer campaigns which are therefore based upon the content and segment. Deep Data Analytics has the usage of Artificial Intelligence along with machine learning and natural language processing in order to obtain the insight issues from each unstructured data.

Deep Data Analysis therefore has an aim of leading the future towards the vertical integrated investment strategies. With the help of increasing the production ready neutral network, a Deep Data Analysis has successfully broadened all the accurate applications to different types of unstructured data challenges.

 

Best 7 Deep Data Analysis Tools

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.

Top 3 Deep Data Analysis Tools

If the person wants to improve their business then they need to understand the value of their product in terms of customers. The customers usually have the pattern of buying something that makes them more comfortable. However, there are huge number of people using the product and also, huge number of data related to it. So, the best way will be to use the deep data analysis tools to evaluate the actual data. The main role of deep data analysis tools will be to analyze the data that the business owner can draw some conclusion out of it for improvement.

Role of deep data analysis tools

The basic role of the deep data analysis tools is to collect the data and make them feasible for the analysis. Also, based on the analysis requirement, the tool needs to build the model and work on getting the desired conclusions. There are many deep data analysis tools available in the market. However, the top 3 deep data analysis tools are presented here.

1. Microsoft Business Intelligence

The Microsoft provides a full package of data analysis that will include the tools as well as some services. The major services will be related to data – ingestion, storage, integration, quality management and analysis. The services are fulfilled by the provider through various platforms. The data storage takes place at the cloud which is capable of vast range of data integration. It also has other features that can help in analyzing the data in bulk, batch or individually. Also, it helps in creating a report at the end that can help in drawing conclusions.

2. Tableau Public

The interpretation system of the software is more preferable because it provides charts, maps, and graphs. It can help in setting up a shared desktop along with server and reader. It can analyze the data from any cloud and also from any other software. However, it does not have its own data storage cloud. So, the owner will have to rely on other systems but then the software is free of cost for analyzing data.

3. KNIME

KNIME is free and open source deep dataanalysis tools. The software provides various features of adopting the data from any other software or language such a python and R. It can help in analyzing along with drawing out conclusions. Thus, the software is perfect for the corporations who are in their initial stage of business.