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
Most professionals interested in performance management must have heard by now about a new hip approach – Objectives and Key Results (OKRs). So, what is it all about? Why is everyone so mesmerized by this new system?
Some may argue that the OKR format became popular because companies with strong brands, such as Google or LinkedIn, credit their success to OKRs. Some might say that it is just another, more flexible, way of working with KPIs.
Others claim that OKRs are simply operational measures, while KPIs reflect the achievement of the strategy. However, supporters of the system state that OKRs represent a tool to create a link between the vision and the reality of an organization.
So, as we can see, there are many ways of interpreting them, but what is the truth behind OKRs and how did they become so popular? Do they really bring superior benefits to organizations compared to other performance management systems or are they used simply because KPIs are starting to be too “mainstream” and the field needed something new?
Timeline of OKR Popularity
Figure 1. OKR Timeline | Source: Author’s Compilation Based on Step by Step Guide to OKRs
OKR Components
Objectives and Key Results is a goal-setting methodology deriving from Management by Objectives, which tries to simplify the concept of performance management. The main goal of this approach is to be easy to use, flexible and answers 3 main questions:
Where do I want to go?
How will I know I’m getting there?
What will I do when I arrive?
Figure 2. OKR Questions | Source: Author’s Compilation Based on Step by Step Guide to OKRs
OKRs are there to better serve fast-changing, agile businesses and environments, given that this system requires regular updates and feedback, as well as employs a smaller time span for changing objectives or key results.
The main changes an OKR-focused system brings are the following:
The achievement of our actions or of what we want to do is supposed to be stretched (60-70% achievable) and set quarterly. In other words, the OKR methodology encourages employees and organizations to set inspirational, challenging, higher-risk objectives, not just operational ones.
The purpose is to strive to do more, which is why a lower achievement than 100% is considered good. The number of objectives is limited to a maximum 5 to ensure employees are focusing on the most important work for one quarter at a time.
Everyone should be involved in the OKR-setting process and employees should be responsible for creating their own OKRs. This automatically creates more empowerment and accountability for the value their job brings. The process of empowering employees to think outside the box, and allowing them to take risks, will result in higher employee engagement.
By not just focusing on day-to-day activities and taking part in a more creative process, your workforce will be able to generate increased levels of innovation as well.
The Value creation theory says Key Results should focus on the impact of activities, not measure the result of the tasks. Setting Key Results that trigger going the extra mile for each employee will create even more value for the entire organization, which will allow it to go even further than planned.
Objectives and Key Results should focus on alignment, not cascading. When setting their own OKRs, employees should take into consideration they own responsibilities, the strategic direction, the already-established OKRs or the management’s aspirations in the organizational context. It is recommended that an employee’s OKRs are actionable by that person, so it’s harder to assign OKRs or create a set of general OKRs for a position.
Given that OKRs are set quarterly and designed to stimulate constant communication, this tool offers more flexibility that the others. It allows fast changes through weekly or biweekly progress checks and makes sure that the focal point is reconsidered each quarter.
However, after all is said and done, we have to remember that the main change OKRs bring is cultural.
Instead of only giving employees objectives and KPIs, employees should understand the strategy, in order to be able to align their OKRs to the strategy or the management’s.
Instead of being given the measures of their performance, employees are involved in setting the focus of their quarterly work.
Instead of measuring the performance of the employees based only on what they have to do, employees are measured based on the value they bring and are offered the flexibility to work on innovative ideas, which might in return bring a lot of benefits to the organizations.
Instead of linking performance with rewards and making sure employees do what they need to do because of incentives, organizations try to engage employees, to make them part of the vision.
As we can see, when implementing Objectives and Key Results, the process feels a lot more back-and-forth than other management methods.
On the one hand, managers play a key role since they need to challenge their employees to consider the value they bring to the organization, as well as offer them support and stimulate regular communication on their OKRs’ status.
On the other, employees represent an equivalent key player, since they need to set their OKRs and be honest with themselves in the process, trying to set challenging OKRs and be willing to go the extra mile.
Visit our website to read more articles covering OKRs and other similar performance management concepts.
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Editor’s Note: This article is part of an ongoing series that will feature practical tips and tricks we’ve learned while implementing the OKR system within various organizations.This article has been updated as of September 17, 2024
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