Overview
The options
and possibilities can seem to be overwhelming for those who are unversed in the
art of charting and visualizing data. Organizations with data visualization
solutions deployed to help derive value from Big Data find out that there are
readily available behind-the-scenes benefits in using these tools. Data visualization can be accessed at
multiple points and there are several very intuitive applications and tip
sheets that can make the execution of visualizations a lot easier especially
for starters.
Starting Data Visualization
Visualizing
data is crucial in making sense out of the huge amounts of it that can now be
tapped. In a recent midmarket (those with 2,000-5,000 employees) survey, 80% of
the respondents concurred that placing data to good use could tremendously help
improve product quality, discover new business opportunities and accelerate
decision making. Around 96 percent of those surveyed already have big data
projects which are either operational or just starting up. However, with reduced
budgets, constrained IT resources and above all, not well-trained data analysts
in their rank, many midsize organizations are not so sure where to start.
To begin, we
offer the following tips on how to get started and what should the organization
do to succeed. These very practical tips include many specific business
functions where visualization and analysis data can deliver the best results.
1. Business Case Building
Data
visualization is not a one-man show. The survey already cited and identified
much successful collaboration between business units and IT as one of the most
important success factors in data analytics projects and the
absence of this vital cooperation between the two as the most important cause
that led to failure.
Ambiguous
promises with the improvement of product quality or a change for better
customer service are not a sufficient measuring stick to justify an investment
in a data visualization solution project. Wanting of moving to data-driven
decision making, one should need to think about precisely what are the business
advantages of better data analysis would be and how much are the costs. This is
not as complicated as it sounds. For example, a data visualization is extremely
successful at growing the size of a shopping basket by analyzing former
customers’ behavior plus of course other factors and suggesting the
up-and-cross sell items that a particular customer is likely to choose. The
same kinds of questions could be presented for any facet of a business, namely;
engineering, finance, operations, human resources, and even IT. These what-if
scenarios are not problematic to compute and they place the need for a data
visualization solution on a rock-hard business footing.
2. Data Democratization
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| Fig. 2: An example of Data Democratization |
Data
visualization solutions are at first designed and developed as a business tool
exclusively for enterprise-scale organizations that can pay to employ
statisticians and other data scientists qualified of working with sophisticated
data analytics. These specialists often worked, and still, do, as in-house
consulting groups which make it too expensive, clumsy, and slow for midsize
organizations, and therefore must be shunned at all costs. Making serious about
data-driven decisions the rule and regulation in an organization, make the data
upon which decisions are based available without the interference of any
intermediaries, and in a functional configuration. This is an area wherein
having the right technology plays a huge role in the organization.
Combining
strong analytics and data visualization enable users to quickly and easily
explore data and also facilitates the convergence of different disciplines
within an enterprise to help in solving a business problem. This suggests that
employees don’t have to be knowledgeable in analytics to work with Big Data.
The power to make the right business decisions are an integral part of running
a business, and adding data visualization solutions to a strong, successful
core of analytics makes that easy and quick. Using visual analytics, users can
deeply penetrate data to verify a gut feeling, spot patterns, understand
trends, or figure out where in the process went wrong. Since these tools deliver
results visually, they are considerably simpler to work with and obtain value
from than the traditional analysis tools. Using all the data collected, data
visualization gives users new outlooks for data analysis, letting them explore
more options and makeup more accurate decisions.
3. Do Not Ever Disregard The Need For Speed
The speed at
which a data visualization solution performs is not something that only
concerns any IT department.
Do not ever let
a perceived lack of technical skills stop you. Having a well-defined business
purpose, consultants can be engaged on a limited basis just to obtain the
necessary technical skill needed in getting an IDG Research survey up and
running, in addition to a customized training regimen for the user base. This
is an economical and much more practical approach than seeking to employ the
talent needed, which at times might be hard to attract if the organization is
not a large one, much more when the system speed of a midsize organization is
wanting.
A system’s speed
has had two very realistic business after-effects.
First,
administrators who are attempting to figure out a problem need a system that
works in real-time and these administrators tend to be men and women of action.
Problem-solving in a business setting is an iterative process where each answer
leads to the next question, to the next answer and the next question, and so
on. If each answer necessitates an hour of calculation, it is then extremely
tough for users to maintain continuousness of thought. They are more likely to
abandon a system that requires hours or days of patiently waiting to deliver a
useful result.
Secondly, there
is another, technically more sound reason why system speed makes a difference.
Simply put, a slow system cannot digest the huge volumes of data currently
available to midsize organizations. To circumvent this problem is getting to
analyze samples rather than the whole data volumes, but unfortunately,
selecting samples to accurately represent a larger group of data requires an echelon
of expertise that midsize organizations do not always have. It should be always
remembered that the value of data visualization is proportional to the
number of employees in an organization who can directly work with data, without
help from experts. The bottom line here is that a fast system is needed since
the slow system will often make visual analytics impossible in midsize organizations.
4. Leveraging The Cloud and Looking Past Bedazzling Visuals
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Fig. 3: Leveraging Cloud for Disaster Recovery – Data Protection Group Discussion | Source: https://thinksis.com/ |
On the other
hand, good looks and dazzling visuals can only take so far. There are a variety
of report generators that are available that can build graphs, generate
impressive charts, and even exceptional dashboards. Though these products do a
good job of communicating well what already has been known more effectively,
they cannot simply tell what is not known unless these are backed by robust
analytical capabilities. At the very least, consider for any ability to drill
down into the data easily, to generate charts automatically, and to deliver
geo-mapping capabilities.
3 Steps to getting started with data visualization
1. Start with a purpose and end in mind
All data
visualization solutions suppose that you have a story to tell and the precise
numbers to back it up. Just like any strategic communication, data
visualization is a procedure that necessitates starting with a well-defined and
clear-cut idea of what is the goal to accomplish.
2. Selection of presentation vehicles
Once a story
and purpose have been decided, choose an appropriate tool to showcase the data.
There are several options to choose from - some are more complicated than
others. Depending upon a selection, any organization can probably produce it
in-house or hire an expert graphic or data designer for help. In all of these
cases, the bottom line is simplicity.
- Here are a
few options;
● Tabling
individual values
When the goal
is for people to look up individual values or when those values need to be
expressed exactly so, tables work the best.
● Bar Charts
Bar charting
aids in comparing two sets of data and are a favored, simple method of telling
a story. One of the best in this method is Cole Nussbaumer’s No More Excuses for Bad Simple Charts: A Template.
●
Charts
Fig. 4: Example of Delightful JavaScript Charts with FusionCharts
Chartings help a lot in processing huge amounts of data quickly. These methods take various forms and are all dependent on the objectives. More about charts types and their uses at SAP Design Guild
●
Graphs
Graphing works
best when the information is contained in the shape of the data, such as
patterns, trends, and any exceptions to the standard, or the entire sets of
values that must be compared. Data professionals have advised moving away from
pie charts as they complicate the mind when it is looking for relationships
between data points.
●
Sparklines
The sparklines are
a well-designed method to express a vast number of data points. These are small
graphics designed to provide a speedy depiction of numerical or statistical
data in a piece of text, taking the form of a graph minus the axis.
●
Infographics
Infographics are a system
gaining popularity as eye-catching and compelling presentations found online,
in annual reports, magazines, and marketing campaigns. These are often
developed with help of professional designers. Learn more about “Ten Fun Tools
to Easily Make Your Infographics”.
3. Telling
the story-
From the
examples above, it is noticeable that it doesn’t matter which type of
visualization is selected and that in effect, visualizations go along a pecking
order and format that help target an audience adhere to reasoning and
understanding of the purpose.
The presentation must have a;
1. Headline
– that explains
what the visualization is all about. A direct-to-the-point yet clear statement
that sets the stage and draws them in.
2.
Background – simply
set the context and give a short but concise clarification of why the data is
wealth worth looking at.
3. The chart
– once a format is
appropriately chosen, keep it as simple as it can be and must be clear of
“chart junk”. Keep the labels clear, spare data points, and draw the people’s
eyes to where they need to go. Learn the gestalt principles of visual perception.
4.
Conclusion – Tell
the viewer what is the point must be understood.
5. Attribution – Cite the source of the data. It is vital for people who want to know its origin, aptness, and when relevant, the scale.
Conclusion
Data
visualization is increasingly becoming an important element of analytics in the
age of big data. Organizations should take full advantage of visual analytics
to address several challenges related to visualization and big data.
Data
visualization helps enable an organization’s decision-makers to look at
analytics presented visually so that they can comprehend problematic concepts
or recognize and pinpoint new patterns. Take the concept of interactive
visualization a little further by using technology to drill down into charts
and graphs for more detail.
Data
visualization is a wise investment for the future of big data.
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About the Author-Writer
The Author is a
Non-voice BPO/DAS Tech Services project contractor and a Media Tech writer.
Studied engineering in the fields of Mechanical, Electrical and Electronics
& Communications Engineering with a Master’s in Business Administration.
Experiences encompass Travel, Industrial, and Telecommunications writing, media
publishing, and analytics. The Author has been in this field for over 15 years.