Data visualization used to takes lots of time, labors, and costs. Back then, businesses relied on age-old BI tools to visualize data. The BI was so technically complex that, except IT people, no one could properly use it. So they had to hire more developers to run the tool.
All that for a single dashboard that often arrives too late for anyone to use.
This is why more businesses are turning to self-service data analytics tools; ones that not only can automate data visualization, but also offer robust analysis and data exploration.
The tools can work with diverse data sources, pull out only the most relevant datasets, and turn them into beautiful visuals. What's so great about self-service data tools is that they let non-IT users build and customize reporting dashboards on their own, often without any scripting skills or any helps from IT people.
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A note on Data Visualization
Data visualization means transforming data into visual elements, such as graphs and charts, to support the interpretation of that data.
This practice is nothing new. Businesses have been visualizing data for decades. A salesman would report his performance as dashboards of a handful of graphs; rarely as a spreadsheet that is hundreds of columns in length. So do the accountants, marketers, or supply chain planners.
It is because our brain processes visuals faster than texts and numbers. "A picture is worth a thousand words", goes the old saw. In other words, we understand data better when it takes the form of visual graphics.
What’s new is the way people approach data visualization.
The problems with traditional BI
It used to require two things to perform data visualization: first, a BI tool, which is typically on-premises and requires technical knowledge to use (which makes it ill-suited for business users); and second, a team of data professionals to operate the BI.
Visualizing data this way is more of a manual, multi-step, and time-consuming process. By the time data becomes graphs and charts, they may very well be obsolete — no longer useful to business users.
Self-service data analytics tools can remove those data guys who stand in-between and fast-track the data visualization process. Packed with self-service features, the tools are designed for business users — the top executives, marketers, accountants, or salespeople, who actually use data visualizations and benefit from data analysis but don’t have much time and technical knowledge.
Self-service data analytics tools can address the 3 challenges of Data Visualization
(1) The size of data, (2) the diversity of data types, and (2) the speed of data processing; these are the 3 challenges you might face when approaching not only data visualization, but also all the processes that precede: data collection, data exploration, and data analysis. Even experienced data professionals and their BI may fail to address these challenges adequately. But self-service data analytics tool can be helpful.
The size of data: Traditional BI tools may be screwed up when dealing with too large datasets; much less visualize them. On the other hand, most modern data analytics tools are designed with advanced analysis. This powerful data-crunching capability prepares the tools for any volume of data, say, thousands of tenants and TBs of data. The tool scales as your data scales. With the right tool, you can focus fewer efforts on collecting and analyzing data, and more on visualizing data to gain better insights.
The diversity of data types: In addition to structured data (data as columns and rows displayed in spreadsheets, and stored in on-premises data warehouse), there are values in unstructured data that you want to capture, analyze, and visualize. Unstructured data, for example, are emails, photos, and social media content such as videos and voices. Most self-service data analytics tools are able to connect to diverse data sources. The tools can mash up data from, for example, social media channels, and allow you to view and interact with them. When IBM surveyed more than 1,100 professionals, it found that fewer than 26% of them are able to analyze unstructured data.
Velocity of data processing: Insights from data are only valuable only if you can capture them in time, or perhaps more correctly, in real-time. It's therefore vital to shorten the latency between data collection and data visualization. Self-service data analytics can address this need for high processing velocity, thanks to the real-time analysis and real-time data visualization they offer. This way, you can ditch batch data processing that takes too much time. Instead, you benefit from real-time processing of data streams, which are also updated in real-time.
How data analytics tools visualize data
Data analytics tools are featured with data connectors. They can connect with most data sources, whether it’s your on-premises databases or specific applications such as ERPs, or even social media. By querying against those data sources, the tool gathers the data needed for visualization. And then it performs data modeling on that data. From data models it just creates, the tools build graphs, reports, and dashboards.
Basically, a data analytics tool would visualize data the way data professionals do. But it does it automatically; and the outputs — reports and dashboards — are produced instantly.
Benefits of Data Analytics tools in data visualization
Here are the reasons why you need a data analytics tool for your data visualization.1. Requiring no scripting skills, or only so much.
Modern data analytics tools are all about self-service. They are designed for non-IT business users. The tools allow you to analyze data and create dashboards by simple drag-and-drops. You can also mouseover on the graphs and charts for more detailed insights.
This eliminates the need for scripting or SQL skills, though some sophisticated tools may require some programming knowledge to perform advanced data analysis.
There are also tools, such as GoodData, that offer the best of both worlds: the user-friendliness that average users need, and the sophistication of advanced analytics that data professionals require.
2. Offering the frontend for data analysis and data visualization.
If Apache Hadoop and other Big Data framework focus on the backend (i.e., support data professionals store and process a vast number of datasets), self-service data analytics tools serve as the frontend. This also means they are to serve businesses, helping them navigate, manipulate, and transform data themselves.
3. Visualizing data from disparate sources
Data analytics tools are not just limited to visualizing structured data. With data analytics tools, business users can connect to unstructured data from diverse sources, and visualize it as stunning graphs and charts.
In data analytics tools, there are connectors. Through these connectors, data analytics tools can share dashboards with your applications, which allows users to integrate data from a wide range of sources— clickstreams, social media, log files, videos, and more.4. Building live data visualization and dashboards
Data analytics tools can build data visualizations in an instant, rather than involving other professionals such as programmers or IT staff. Users can export dashboards as flat graphic files, or as code snippets.
A code snippet is a reusable source code that can be copied and pasted into websites or applications.
Some modern tools also offer live connections, in which the dashboards will change in real-time as the data sources change.5. Offering Mobile-first experience
As everything goes mobile, so should your data analysis and data visualization. Certain data analytics tools support mobile devices, allowing users to experience the same stunning dashboards on tablets or smartphones as they do on desktops.
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