Gone are the days when analyzing and visualizing data to get information was a job that was limited to the IT and business intelligence (BI) divisions. Gone also are the days when the sole possession of knowledge, skills, and tools for data processing was in the hands of the “data guy.”
Data is becoming more and more abundant and essential for various business operations. This makes centralizing data processing on one or two divisions an inevitable bottleneck. On the other hand, analytics and visualization tools are becoming easier to use, with more intuitive user-friendly interfaces that require less and less technical expertise.
What SSBI Is About
Self-service business intelligence (SSBI), also called self-service data exploration, has become an important approach for data-driven insights in business. It means giving the ability to the wide range of employees who are not experienced with data to drive insights from relevant datasets and create exploratory visualizations to help them better understand the data and to use it in reports. It’s also a part of what is called data democratization if you’d like another fancy term on the plate.
It should be, however, distinguished from the second approach called dashboarding. While the latter should still be the responsibility of the experienced BI team, turning amounts of data to finely curated reports on the most important KPIs within a well-developed narrative can happen. The SSBI approach aims to:
Avoid time delays in data-driven decision making among the low and mid-level teams that may happen due to the centralization of analytics responsibilities.
Minimize intuition-based decisions that can be made by low and mid-level teams on a daily basis due to lack of analytical capabilities.
Enhance internal communication within the teams by making data-driven insights and visualizations easier to generate, and therefore more frequent integration of reports.
Enhance external communication of the organization as the insights and visualizations can also be easily used in developing publications, like blog posts for example.
Google Sheets and Datawrapper
There are tons of visualization tools out there that can enable you to create an SSBI system for your organization, some of which are technologically advanced, but each has its best uses and downfalls.
Just like Google Sheets and Datawrapper. The advantages of using these tools are the following:
– Businesses with no capabilities of experienced teams or infrastructure can implement the system.
– Anyone can use it as it requires little to no technical expertise.
– Visualizations can be easily duplicated and edited, suiting fast-based work routines.
– Visualizations can be easily well-formatted and laid out, leading to efficient reporting.
– Generate both interactive and static visualizations that are suitable for embedding in various forms of reports, from web-based all the way to paper-based.
Using a self-service BI solution can help streamline operations and support critical decisions. It also encourages collaboration, simplifies daily business needs, and increases one’s competitive advantage. With the efficiency brought by SSBI, businesses can focus on what matters most to them.
Want to understand how visual representations can support the decision making process and allow quick transmission of information? Sign up for The KPI Institute’s Data Visualization Certification course.
We deal with data every day, especially at work. It can fuel our decisions and change the way we work. At the same time, if we’re surrounded by a huge amount of data, we may not find it easy to arrive at an optimal decision. This is where data visualization comes in.
Data visualization refers to the graphical representation of the data. It makes large amounts of information easier to understand and helps identify patterns and trends. People can easily comprehend information and make conclusions through data visualization.
“Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space,” wrote American statistician Edward R. Tuffe, author of the book “The Visual Display of Quantitative Information.”
Understanding how to approach data visualization allows people to equip themselves with the right tools, approach, and strategies as they gather data and present them visually. This is important to businesses who want to understand consumer behavior patterns or governments seeking data-backed insights on a crisis.
Data visualization may be considered a science because it is a process and represents data methodically and accurately. Data visualization begins with volumes of information, undergoes an intensive cleaning, classification, statistical and mathematical modeling, analysis, and design process, and ends with a visualization.
On the other hand, many argue that data visualization is a language because it uses diagrams to convey meaning. Data is encoded into symbology and semiology. The syntax and conventions of these diagrams are not inherent and must be learned.
Data visualization helps to communicate analytics results in pictures. In simple words, data visualization is the language of images. That is on par with the language of words both written and spoken and with the language of numbers and statistics.
Merging science and language
Science and language do not have to invalidate each other. Their elements can go hand in hand in data visualization.
In data visualization, the challenge is how to make more people take interest in scientifically processed data. Presenting appropriate and relevant information in an engaging format through design is what makes data visualization successful. Science processes and provides information based on certain objectives while design is a form of communication shaped by visual elements.
Combined, scientific data and design can generate meaning out of raw data. The end result of data visualization is almost always a story. In storytelling, the plot (design) won’t be able to progress without the characters (scientific data) and vice versa.
Ensuring that graphs and charts present meaningful results is important now more than ever. In MicroStrategy’s “2018 Global State of Enterprise Analytics,” 63% of data-driven organizations said that implementing analytics initiatives led to high efficiency and productivity while 57% said they became more effective in decision making.
With this, the challenge for organizations is to know how to structure, format, and present their graphical data that will allow them to make faster business decisions. Sign up for The KPI Institute’s Certified Data Visualization Professional course to learn the fundamentals of creating visual representations, the most effective layouts, channel selection, and reporting best practices.
When it comes to our bodies, there is a rich and complex amount of data available for us today, which allows us to make better decisions about our health.
Given that we live in a time of data and information overload, we really need to have better mechanisms to make sense of it all. Data visualization supports the transfer of information to knowledge by illustrating hidden issues and opportunities in big data sets.
Big data is creating unrivaled opportunities for businesses. It aids them to achieve faster and deeper insights that can strengthen the decision making process and improve customer experience. It can also accelerate the speed of innovation and gain a competitive advantage.
A significant amount of the human brain is dedicated to visual processing. This results in our sight having a sharpness of perception far surpassing our other senses. Effective data visualization shifts the balance between perception and cognition. The visual cortex is used at a higher capacity and the viewer understands the presented information much quicker, better and is able to make a superior decision based on the findings.
Businesses are increasingly turning to data visualization to discover the overwhelming amount and variety of data cascading into their operations, and to eliminate the struggle of just storing the data and focus on how to analyze, interpret and present it in a meaningful way. The trend towards data visualization is worth delving into by any business seeking to derive more value from big data.
Tackling big data focuses usually involves the four V’s: volume, velocity, variety and veracity. However, it does not emphasize enough another “V” that requires attention, namely visualization. Even with the use of business intelligence tools and the incredible exponential increase in computer power, the need to consume information in a meaningful manner exceeds the ability to process it.
Visualization plays a key role starting from the raw input of the big data, where structures and underlying patterns that may be held within the data can be observed or formed, to the end result of a visual representation that presents valuable key insights in a fast, efficient and clever manner.
Crafting a visualization is more than simply translating a table of data in a visual display. Data visualization ought to communicate information in the most effective way, with the prime purpose of truly revealing data in a quick, accurate, powerful and long-lasting manner.
The main problem with big data involves complexity. Information and data is growing exponentially with time, as an increasing amount of data is made available on the internet. Furthermore, the number of insights, opportunities or hypotheses hidden in a dataset is exponential to the size of the datasets.
In achieving efficiency and ensuring the comprehensibility of visual representation resulted from big data, key performance indicators (KPIs) can be used, as to attain the goal of graphical excellence and to add value to the end result. Big data visualization requires skills that are not intuitive and the entire process relies on principles that must be learned. Each big data visualization created should follow a clear path to success, namely: attain, define, structure, extract, load, display, refine the data and interact with it.
KPIs add value to the entire process by ensuring clarity in developing the strategy of the project, focus on what matters and requires attention, as well as improvement by monitoring the progress towards the desired state.
When developing a project of big data visualization, the process should follow a cursive and pre-defined flow in accordance with the project needs and the requirements of the end-user. These recommended stages are:
1.Acquiring the data: this is usually how the process starts, unrelated with the platform which provides the data. In the process of big data collection, there is also the issue of data selection. Instead of “just throwing it all in”, one should focus on selecting high quality data, which is relevant to the project’s objective and does not add noise to the end result.
The noisier the data is, the more problematic it will be to see the important trends. It is suggested to have a clear strategy for the data sources required, as well as for the subsets of data relevant to the questions the project wants to answer to.
2.Structuring: The next phase in the process refers to structuring of the acquired data. This includes the process of organizing the data to align it to one standard. The data store might be comprised of a combination of structured, semi-structured and unstructured data.
At this stage, it is easier to identify the common aspects in each sets of data and to find relationships between the data at hand. This includes translating system specific data coding to meaningful and usable data (the platform where the data will be aggregated does not know that the set of data labeled “Customer No.” is the same as “# Customer” or “ID-Customer”).
3.Loading and visual mode selection: After cleaning the data, filtering through enormous amounts of data and replicating the application logic to make the data self-describing, the process continues with loading the data in the preferred platform and choosing the visual mode of representation.
In this stage, it can be noted if the background data is very noisy or high quality, as the emerging visual representation will be either hard to read or irrelevant to the strategic objective of the project, or clear and visually engaging.
By implementing KPIs along the project and linking them to the project objectives, the increased value will be added in the form of:
Better quality of the visual representations
Fewer project delays
Less rework along the way
Improved productivity
Greater contribution to the visuals’ value
Enhanced growth and innovation of the visual representation
