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How to Intelligently Apply Data Integration and Visual Analytics Tools

How to Intelligently Apply Data Integration and Visual Analytics Tools

Data integration requires merging date from different sources, stored using technologies. Companies build a “data warehouse where aggregated data can be stored and retrieved. This is particularly useful for researchers looking to big data to aid in their investigation and corporations usually during the merging with other companies.

According to ,Dataintegration,  integration of data can be performed using several organizational levels such as:

Manual Integration of data

Users can access all systems of different sources or interface of web pages but without viewing consolidated data.

Integration of Data Based On Different Applications

This organizational level requires particular applications to integrate data.

Middleware Data Integration

This data integration organizational level transfers the integration of data from particular applications to a new layer of middleware.

Virtual Integration of Data or Uniform Access

This provides a unified view of data that can be accessed across the whole enterprise. One of the advantages of uniform data access or virtual integration is the nearly zero latency of data updates from the source to a unified view. A user can access customer information transparently obtained from the system.

Physical Integration of Data or Common Storage

This means a new system is created to store and manage data from the source systems. The most common example is the data warehouse. However, physical integration of data requires a system of its own to handle huge amount of data.

Companies, marketers, data scientists and researchers can all benefit from this never-ending stream of information available to them by putting them into visualization tools to be able to study and analyze aggregated data.

Data Visualization is the presentation of data by using graphs and charts. It enables researchers to visually grasp complicated concepts or identifying new trends and patterns.


Here are some of data visualization tools for data analysis

1. MicroStrategy Analytics Desktop

MicroStrategy Analytics Desktop is user-friendly software for visual data analytics. This allows you to connect any database you need, import documents like excel and spreadsheets, files and other online sources. Through these you can gain insights from visualization of data.

2. Domo

Domo boosts an intelligent UI and is specifically designed to allow users to create dashboards, and particularly suited to users who are interested in visualizing in cloud-based apps. Domo can take input from databases, spreadsheets, and social media platforms viewable on handheld devices like mobile phone and tablets.

3. Tableau

Tableau offers a variety of tools that include an online and server versions. Tableau can connect to live-data for real time visualizations or warehoused data.

4. Qlikview

Qlikview has the ability to quickly visualize data with a custom-data visualization dashboard that allows real-time visualizations.

These are just some of the visual analytics tools available for researchers to be able to analyze and study data taken from different sources and data warehouses.

Insights that can be gained from integrated data and visualizations

1. Identify strengths, weaknesses, problems and opportunities

Companies can capitalize on information regarding the strengths, weaknesses, problems and opportunities of particular products and services so that they can improve their own products and services and compete in the competitive market.

2. Study customer behaviors and define factors that influence them

Learning about the factors that influence customer behaviors is vital in creating campaigns that can potentially attract customers. The more that you know how your customers behaves, the more that you will be making huge number of revenue for your business because you will have that knowledge on what the customers need or like on that exact moment. You as the owner providing that excellent service on the right moment will win the hearts of your customers even more,

3. Helps businesses understand purchasing behaviors and how to improve products and services

As mentioned, identifying which areas of the business is strong and which is weak can be used in developing products, improve operations and streamline services that will satisfy customers and drive sales. Having the proper knowledge about your own products further assist the business in making more revenue because you will know how your product impacts the market so as the customers that would be patronizing your products and services.

4. Discover emerging trends, predict sales and study patterns

An advantage can be given to businesses in this scene by making use of data visualization to uncover trends. It would also lessen the hassle of searching any problems that may lower down the quality of the products and immediately fix minimal problems before they get even bigger. Predicting the sales that you may be doing in the future is very essential in setting up the right expectations over the quality of the products or services that you will be providing to customers.

5. Grasp information quickly

Businesses can now see huge amounts of data just by having a representation of the information that they have. What’s good to this is that they can immediately conclude from all the information that they have. As a result, they can already address all of the problems that they would be encountering along the way even right before it emerges.

6. Share the information to others

Now that you already have all the relevant information that you need, you will now have to relay the message to everyone by making use of visual aids such as graphs or charts. It will be easier to have the information be spread among the people using this method. Sharing the information to everyone would be helpful in gaining more familiarity over the product or service, as individuals would have the knowledge on how everything works.

Data visualization promotes creative ideas of data exploration. It improves the way analysts and data scientists work with data, which may result in faster response to issues that need immediate and efficient solutions. 

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