With interactive visualization, business leaders and other key decision makers create “what if” views of data, explore data implications real time, and understand data to scientifically inform talent decisions to accelerate achievement of today’s and tomorrow’s business goals.
An organization’s people strategy is the single most critical plan for accelerating business performance.
Most Important Strategy to Accelerate Business Performance
Source: 2016 Brandon Hall Group Talent Management Study
Yet 60% of organizations responding to Brandon Hall Group’s 2016 research survey said their approach was not at all effective or largely ineffective. When asked why, senior leadership said because the vast majority of hiring, managing, developing, mobilizing, and rewarding decisions are made on mere intuition.
Just 16% of organization surveyed have matured their analytics strategy beyond gut feel and ad hoc reporting.
And, a mere 7% are top performing organizations (Level 4 organizations) using data visualization to predict and prescribe talent actions and decisions that make a business difference.
A 4-Level Model of Analytics Impact
Brandon Hall Group High Performance Analytics Impact Model
Source: Brandon Hall Group, 2016
Data visualization goes well beyond static reports or spreadsheets. It presents data in graphical or pictorial format readily exposing customer behavior patterns, top talent turnover trends, employee engagement risks, potential dips in productivity, and other critical business and talent information.
With interactive visualization, business leaders and other key decision makers create “what if” views of data, explore data implications real time, and understand data to scientifically inform talent decisions to accelerate achievement of today’s and tomorrow’s business goals.
Level 4 data visualization organizations yield, on average, 60% higher returns on their business metrics than lower performing peers.
‘Very Good’ or ‘Excellent’ Business Results: Level 4 vs. Level 1 Organizations
Source: 2016 Brandon Hall Group Talent Management Study
While the uptake of analytics usage is recently on the rise, analytics impact opportunity lies not in the availability or historical reporting of data but rather in its visibility and ability to interact real-time with it – data visualization that is.
Key characteristics of data visualization are:
- Ensuring the data is of high quality
- Telling the story behind the data
- Offering interactive self-service dashboards
Ensuring the Data Is of High Quality
Data visualization assumes data integrity. An effective, sustainable data quality plan includes at least the following three elements:
1. Data governance
Data pours into organizations in tremendous volumes. Mining billions and trillions of data elements can be overwhelming to an organization’s analysts, leaders, and employees and particularly when the integrity of the data is suspect. Despite the potential business value of data visualization, it is of zero value if the data is not of high quality. Level 4 analytics companies use a data governance process to raise awareness about why data quality is a business imperative, to document the quantifiable costs associated with the use of poor data, and to ensure clean, high-quality data avoiding subsequent issues tied to bad data.
2. Data outliers process
Graphical representations of data made possible by data visualization can highlight data outliers or errors more quickly than tables and spreadsheets filled with numbers and text. While removing outliers is essential before final analysis, analysis of the outliers themselves may reveal potentially valuable insights. Without data visualization, identifying outliers is extremely challenging.
3. Data accountability
Analyzing data, identifying data patterns, and creating insights is not a task for the inexperienced. Managing data requires expert resources and automation tools. Organizations that assign dedicated ownership of data and data visualization, even if only in a part time fashion, outperform those without dedicated accountability.
Poor data quality has a significant business cost – in time, effort and accuracy. On average, data analysts spend 20 to 60 percent of their time trying to understand, fix, or eliminate poor data. Organizations that use data governance, execute a process for spotting data outliers, and assign ownership of data to expert resources, completely eliminate – or at worst significantly reduce — the number of occasions they are plagued with bad data.
Telling the Story Behind the Data
Today, we have a visual trend on infographics. Infographics share information and are effective at showcasing a critical message. Take a look at the infographic below on turnover. It tells us that in large organizations, turnover costs are greatest in entry-level positions.
Infographic: Turnover
Source: www.globoforce.com
This infographic does not, however, offer critical turnover insights like: (1) Is the turnover coming from a certain geography? (2) How is turnover changing over time? (3) Is our turnover pattern different among the three employee levels (i.e. is one increasing at a higher rate than the other)?
This infographic and associated tabular reports and chart masks, even hides, the critical and actionable insights necessary to make intelligent business decisions.
Now, take a look at the following data visual on turnover.
Data Visualization: Turnover
Source: www.visier.com
The graphic above identifies specific employees who are at flight risk within the next 18 months, and offers attribute information on each employee. It offers imminent turnover information, suggests specific employees with whom targeted attention might be essential, and suggests locations and/or job roles that might be at risk and worthy of investigation.
This data visual allows leadership to proactively explore organizational risk areas and execute risk mitigation plans before business continuity is threatened, unveils insights that lead directly to actionable business decisions, and puts talent decision-making power in the hands of the managers.
Offering Interactive Self-Service Data Dashboards
Top-performing organizations are prioritizing analytics expertise in house, but not everyone has to be an analytics expert to use and have a need for data. Self-service data visualization dashboards allow all stakeholders to see and interact with data to identify sales patterns, predict talent turnover, or anticipate elevated service support times. In other words, self-service data dashboards maximize data insights, are fully interactive, and allow users (regardless of analytics expertise or lack thereof) to:
- Manipulate complex data sets and generate multi-tiered dashboards in seconds
- Drill down in to the data for querying, analysis, and reporting
- Set up alerts to trigger data update or change notifications
- Share dashboard data real-time with peers for team projects and simultaneous joint decision-making
- Track key performance indicators and conduct queries 24×7 from mobile devices
There are key business benefits to interactive data visualization dashboards:
- Improves efficiency by eliminating the need for multiple static reports
- Lowers operational costs by reducing the need for user training
- Improves decision making by empowering users with “what if” querying
- Facilitates the ability to easily identify negative trends
Despite the business value that data visualization tools and technology offer, nearly half of organizations do not yet have workforce analytics tools or technology. Fortunately, however, 86% of organizations acknowledge the business risk and have plans to replace existing technology or acquire technology that ensures their ability to have better workforce analytics and data visualization capability.
Data visualization is the key that turns “Big Data” into actionable data – data that provides the critical insights and understanding leaders need to make informed and intelligent business decisions.
Until next time…
Laci Loew, Vice President and Principal Analyst Talent Management
Brandon Hall Group
@LaciLoew
