For all of its advances, the IT sector’s first five decades could be characterized as the electronic storing of systems of record.
The move to the electronic era saw paper ledgers, tax returns, bank statements stored and archived safely and legally on machines instead of paper files.
Rows of data were worked on in spreadsheets and stored in SQL relational databases. Since then data has been everywhere. It has been in data warehouses, it has been in data lakes, up mountains where it has been mined and in pools.
It is now so voluminous that it can even be measured in something called a Brontobyte. (Though it is a generally accepted we are in the Zettabyte era.)
And more recently this digital era of data and big data saw enterprises embark on the quest to extract value from the information in order to make accurate forecasts.
The Holy Grail of data value began within a subset of mathematics with a discipline called probability and statistical analysis.
Specialists interrogated the data for patterns in order to conduct fraud detection, measure marketing campaign effectiveness or grade insurance claim assessments.
Probability and statistical analysis (a tough and not a very popular career choice) became Business Intelligence that in turn evolved into Data Science (highly sought after and well paid).
What a data scientist is and does has been described in many ways. It requires deep understanding of probability and statistics, a domain expertise such as finance or health and a high level of expertise in machine learning and the workings of big data frameworks such whether commercial such as SAP Hana or open source like Hadoop and their associated platforms, languages and methods.
Analytics now spans five categories: descriptive, diagnostic, predictive, prescriptive, and cognitive, with each building on the last.
The current effort of gaining value from business data is called advanced analytics.
Advanced analytics means using methods to pull meaning from data that will allow for accurate forecasts and predictions. It can also mean managing the added complexity of analysing combinations of structured and unstructured data and doing so in real time.
So, if a company is collecting a petabyte of data each day, say for example a telco or mobile network operator, then – it is argued – the use of advanced analytics will help that company better serve its customers by knowing that they will want or will do next.
This could help reduce churn and allow companies to up- and cross-sell features and services.
The big data storage challenges
The implications of advanced analytics for the IT professional with responsibility for the storage, security and accessibility of this vast data pool are huge. Simply managing the volumes of data pouring into the organization is proving to be a challenge.
For example, even powering and cooling enough HDD RAID arrays to store an Exabyte of raw data would break the budget of most companies.
Increasingly it will be software-defined storage and flash that will be deployed for big data as advanced analytics promises more insight for direct business benefit.
This will be thanks to these media’s improved speed, density, performance and reliability relative to disk and it could fundamentally change the storage infrastructure strategies of enterprises and organizations.
Click here to continue reading Ambrose McNevin’s article.
Marketing campaigns can be costly, time-consuming, and worst of all, ineffective. Too many companies waste valuable resources on customers and employees who probably won’t respond as expected, simply because the segmentation wasn’t done right.
RFM analysis, short for Recency, Frequency and Monetary value, is one of the customer segmentation methods that is easiest to deploy and, at the same time, returns the best results. We are showing how to apply it to the “internal customers,” a.k.a the employees of an organization.
2. Monty Python’s Life of Brian: “You Are All Different”
All this segmentation talk brings to mind a scene from Monty Python’s Life of Brian. The film tells the story of Brian Cohen, a young Jewish man who is born on the same day as Jesus Christ and is subsequently mistaken for the Messiah.
“You are all Different”. An illustration by Marisa Echarri
The film contains religious satire that was controversial when it was released in 1979. Some countries, including Ireland and Norway, banned its showing. Of course, censorship contributed to its spread.
The Swedish even marketed the film as: “So funny it was banned in Norway.”
During a moment of improvised preaching, Brian addresses the crowd, who believes he is the Messiah:
Brian: Please, please, please listen! I’ve got one or two things to say.
The Crowd: Tell us! Tell us both of them!
Brian: Look, you’ve got it all wrong! You don’t NEED to follow ME, You don’t NEED to follow ANYBODY! You’ve got to think for your selves! You’re ALL individuals!
The Crowd: Yes! We’re all individuals!
Brian: You’re all different!
The Crowd: Yes, we ARE all different!
Man in crowd: I’m not…
The Crowd: Shh!
Monty Python - Life of Brian - Your're all different - YouTube
3. Recency, Frequency, and Monetary value
RFM analysis is one of the customer segmentation methods that is easiest to deploy and, at the same time, returns the best results.
We can determine who our best clients are by using three metrics: recency (R), frequency (F), and monetary value (M).
These three metrics are the most important factors in client segmentation:
Recency (R): days elapsed since the last purchase. Customers who have recently made purchases are more likely to buy new products.
Frequency (F): the number of purchases over a period. Customers who have purchased more products are more likely to buy new ones.
Monetary value (M): that is, the total amount of money they’ve spent over a period.
The RFM technique is widely used in marketing, but traditionally it hasn’t been applied in any other field. We’ve transferred this technique to the field of people analytics, specifically to salespeople.
This way, you can classify employees in a specific organization based on the three basic parameters of the RFM analysis.
In this case, we’ve applied the analysis to real estate agents, in order to implement customized training and economic compensation plans according to each agent’s performance.
Note: If you don’t know R, don’t worry. I’ll talk you through each step so you have a conceptual understanding of what we’re doing. The end result and its implications, which I’ll describe later in this article, is very interesting and has tangible applications to how we manage people!
The script is very simple. It needs no extra packages.
Load the dataset (rfm-relastate.csv) in RStudio and apply:
$ R – Date of the last sale. We are dealing with this
$ F– Number of sales since
$ M – Total amount sold
This is how the original file looks like:
From now on, we develop a ranking based on the three RFM variables, assigning a value to each employee according to which group they are in. In this case, the values of each variable are divided into four groups of equal size.
We will need some data transformation.
To begin with, we are subtracting the last date of sale to the current date to obtain the days that have passed since the last sale
As a result, an employee who scores in the top 25% in terms of sale recency, the 25% of most frequent sales, and the 25% of total sales value, would belong to segment 444, the best group.
The highest values of each variable correspond to the most favorable segments, so the more recent the sale, the higher the number in the recency segment; the higher the sales frequency, the higher the number in the Frequency segment; and the higher the value of the agents’ sales, the higher the number in the Monetary segment.
Although we chose 4 levels (with quartiles), it is quite common to work with 5 levels (quintiles) and 10 levels (deciles).
We obtain 4 * 4 * 4 (64) groups according to this segmentation.
RFM analysis is an extremely useful tool, which quickly provides excellent results. In addition, it’s a very simple method to deploy.
5. What is in it for me?
Let’s see some examples, so that you can figure out how to apply it to your own organization.
5.1 Talent Management
RFM can contribute to answer some questions in the area of Talent Management. Such as:
Which employees should be promoted?
In order to get to the next level in their career, which actions should employees take?
RFM segmentation helps organizations make decisions in career path planning according to the evolution of employees in their RFM segmentation.
More often than not, there are meaningful patterns in the RFM segmentation when we analyze the progress in time of employees across RFM groups.
Evolution in time
We have to look at the evolution of people in their RFM-based grouping because an RFM model is a snapshot of employee (or customer) behavior from a current perspective. You’ll need to repeat the analysis in different phases and use the results to show how employees move between the different categories. This provides more depth than a single result.
The easiest way is to create a simple data file that saves the RFM scores for each customer by date. Using simple queries, you can extract the temporary RFM scores for individual clients or customer groups over time.
Training and Development
Developing a training program requires knowing what type of training is needed. RFM segmentation can turn into a suitable tool for matching different types and levels of training to the right profile of people.
5.2 RFM as an Objective Type of Performance Measurement
As a result, a number of algorithms could be run using RFM as a dependent, or outcome variable.
For example, you could be analyzing different forms of onboarding according to their effectiveness to raise people to the upper segments of RFM or calculating with a logistic regression if a point more of engagement brings about meaningful rises at RFM.
And, obviously, the other way round, RFM (and especially the evolution of RFM) can be a good predictor (independent variable) of turn-over or engagement.
The Winner Takes it All
RFM is just one seasoned type of segmentation. In our HR practice, RFM should be competing with other forms of socio-demographic segmentations (like age, sex, position, salary), or different forms of cluster analysis.
Whatever segmentation we carry out, at the end of the day, it will only be practical to the extent that it helps better explain relevant indicators that impact the bottom-line of the business.
A good deal of trial and error looms in the horizon. I promise to publish something about our own practice of the benchmarking of different segmentations in a new post, where I will be pitching the results of RFM against a variety of clustering-based segmentation, in order to obtain better models that explain performance, engagement or turnover.
But a large majority of senior executives don’t have a high level of trust in the way their organization uses data, analytics, or AI, according to a new report from tax and advisory firm KPMG International.
The firm surveyed 2,190 global senior executives, and found that just 35 percent say they have a high level of trust in the way their organization uses data and analytics.
Their concerns over the risks of data, analytics, and AI are high, with about two-thirds having some reservations or active mistrust in their data and analytics.
“We often see organizations run dual processes — one managed by humans and one managed by machines– to determine whether the machine-generated insights align to those delivered by their tried-and-true, human-generated processes,” said Brad Fisher, national leader D&A (data and analytics) at KPMG in the US. “That’s simply because many executives don’t have confidence that the insights are reliable and accurate.”
A huge majority (92 percent) are concerned about the negative impact of data and analytics on corporate reputation. Even so, many senior executives (62 percent) said technology functions, not the C-level and functional areas, bear responsibility when a machine or an algorithm goes wrong.
KPMG’s Guardians of Trust report suggests that the growing interrelationship between humans and machines calls for stronger accountability at the C-level rather than with the technology functions, and for proactive governance with strategic and operational controls that ensure and maintain trust.
As organizations make the shift to fully digital, analytically-driven enterprises, the study says, the management of machines is becoming as important as the management of people.
“Once analytics and AI become ubiquitous, it will be imperative and more difficult to manage trust,” said Thomas Erwin, global head of KPMG Lighthouse — Center of Excellence for D&A and Intelligent Automation.
“With the rapid take-up of predictive analytics, we should prepare now to bring appropriate governance to this wild west of algorithms,” Erwin said.
“The governance of machines must become a core part of the governance of the whole organization, with the goal being to match the power and risk of D&A with the wisdom to use it well.”
Click here to continue reading Bob Violino’s article
It’s no secret that organizations have been increasingly turning to advanced analytics and artificial intelligence (AI) to improve decision making across business processes—from research and design to supply chain and risk management.
Along the way, there’s been plenty of literature and executive hand-wringing over hiring and deploying ever-scarce data scientists to make this happen. Certainly, data scientists are required to build the analytics models—including machine learning and, increasingly, deep learning—capable of turning vast amounts of data into insights.
More recently, however, companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and—perhaps most important—translators.
Why are translators so important? They help ensure that organizations achieve real impact from their analytics initiatives (which has the added benefit of keeping data scientists fulfilled and more likely to stay on, easing executives’ stress over sourcing that talent).
What exactly is an analytics translator?
To understand more about what translators are, it’s important to first understand what they aren’t. Translators are neither data architects nor data engineers. They’re not even necessarily dedicated analytics professionals, and they don’t possess deep technical expertise in programming or modeling.
Instead, translators play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers. In their role, translators help ensure that the deep insights generated through sophisticated analytics translate into impact at scale in an organization. By 2026, the McKinsey Global Institute estimates that demand for translators in the United States alone may reach two to four million.
What does a translator do?
At the outset of an analytics initiative, translators draw on their domain knowledge to help business leaders identify and prioritize their business problems, based on which will create the highest value when solved. These may be opportunities within a single line of business (e.g., improving product quality in manufacturing) or cross-organizational initiatives (e.g., reducing product delivery time).
Translators then tap into their working knowledge of AI and analytics to convey these business goals to the data professionals who will create the models and solutions. Finally, translators ensure that the solution produces insights that the business can interpret and execute on, and, ultimately, communicates the benefits of these insights to business users to drive adoption.
Given the diversity of potential use cases, translators may be part of the corporate strategy team, a functional center of excellence, or even a business unit assigned to execute analytics use cases.
Welcome to the second edition of our ‘Most Trending Articles’ of 2018! Here at Analytics in HR, we hope you’ve had a great start of 2018 and are ready to continue on your HR Analytics journey. Here are the top 5 articles we enjoyed reading the most last month.
#5 The role of the People Analytics leader – Part 1: Building Capability.
Coming in at #5, we have an excellent article written by David Green. In his article he talks about the role of the People Analytics leader within his/her organization. He talks with Arun Chidambaram, recognized as one of the leading authorities in the field of People Analytics and well-known speaker.
In his article, David Green covers topics such as:
Skills needed in a People Analytics team
Traditional vs innovative projects
The evolvement of the structure of the team over time
Key milestones in the building of a firm foundation for people analytics
5-step research methodology
Challenges when building organizational capability
His article is filled to the brim with knowledge and first-hand experience about the role of a truly successful people analytics leader. This is only part 1 of this series, so stay tuned for part 2, which should be published in February!
#4: Introduction to Cultural Analytics, its implications and use cases
At our #4 spot, we have an article written by Soumyasanto Sen about Cultural Analytics. It was recently featured on our blog, but we loved it so much we wanted to highlight it again!
Culture is a big topic in the world of HR. Whether you’re a small firm growing rapidly, or a big corporation who just acquired another firm, culture (and the loss of it) is on everyone’s mind.
According to Deloitte, 86% of executives surveyed around the world rate culture as “very important” or “important. However, only 12% of executives believe they are driving the “right culture”. This showcases the mystery and difficulties HR professionals/managers have in grasping their company’s culture.
In order to keep a grasp on your company’s culture, Soumyasanto Sen talks about Cultural Analytics and how it can benefit your company.
If you’re at all interested in corporate culture, this is a great read! Read the article about cultural analytics.
#3 Practical Use of a Predictive Analytics approach to develop a model of employee engagement.
Predictive Analytics is a set of analyses that most HR professionals want to incorporate more of into their work. It is not so much about predicting the future, as it is about minimizing surprise.
It can be a valuable asset to any HR professional’s toolkit, but unfortunately, there’s hasn’t been a lot of case studies on this topic.
Jose Luis Chavez Vasquez, however, shares a simple, but practical example of the application of Predictive Analytics. In his case study, he analysed the engagement level of a team of engineers, who developed a unique skillset and could thus not be replaced in the market.
In order to use predictive analytics, he looked at several variables that could influence their engagement. This would, in turn, benefit retention levels of this team of engineers.
#2: 75+ Conferences on People Analytics & Data-Driven HR to attend in 2018
We’re featuring David Green not once, but twice this month! Two years ago, David Green made an article featuring all the people analytic conferences around the world that he could find. That year, he found 24 conferences.
Fast forward two years and the field of People Analytics has experienced a tremendous growth. In his latest list of people analytics conferences, he found a staggering 75 conferences around the world.
No matter where you are in the world, from Sydney to San Francisco and from New York to Hamburg, there’s bound to be a people analytics conference coming near you in 2018.
Looking for a conference to join this year? Take a look at David’s list here.
#1: Talent Management Special Report
For our #1 spot this month, we have great piece on talent management in this special report by Raconteur. This report is filled to the brim with knowledge and easy to grasp infographics on talent management and people analytics.
It’s well made and we highly recommend reading it, especially the articles on HR Analytics by Josh Bersin (p. 15) and Andre Robberts (p.8).
How will data capturing evolve in people analytics?
According to Jouko, data capturing at the beginning of the employee lifecycle is common and usually of decent quality. However, the further down the employee journey we go, the messier the data.
Laura notes that organizations are poor at capturing employee data – and I agree with her. By mapping the employee lifecycle and attaching different data capturing points along the way we will be very effective at capturing data.
According to Luk, this creates a lot more opportunities. In addition, he sees HR technology as a great way to support this data capture effort.
This is what they said when I asked them the question at UNLEASH.
How will Data Capturing Evolve in People Analytics? - YouTube
Companies have been trying to measure their people for many decades. Fredrick Taylor, an industrial engineer, started this trend in 1911 when he published his report Scientific Management, which studied the movement and behavior of factory workers in steel mills.
Since then companies have deployed thousands of engagement surveys, studied the characteristics of top leaders, done countless reviews of retention and turnover, and built massive human resources data warehouses. All in an effort to figure out “what can we do to get more out of our people?”
Well now this domain is called people analytics and it has become a fast-growing, core-business initiative. A study, entitled High-Impact People Analytics and completed last November by Bersin by Deloitte, found that 69 percent of large organizations have a people analytics team and are actively building an integrated store of people-related data.
Why the growth and why the business imperative? Several technical and business factors have collided to make this topic so important.
Firstly, organizations have more people-related data than ever before. Thanks to the proliferation of office productivity tools, employee badge readers, pulse surveys, integrated enterprise resource planning systems and monitoring devices at work, companies have vast amounts of detailed data about their people.
Companies now know who people are communicating with, their location and travel schedules, their salary, job history and training plans.
New tools for organizational network analysis, built into email platforms, can tell leaders who is communicating with whom, new tools for audio and facial recognition identify who is under stress, and video cameras and heat sensors can even identify how much time people spend at their desks.
It could be argued that much of this information is confidential and private, but most employees don’t mind organisations capturing this data, as long as they know it is being done to improve their work experience, as shown in 2015 Conference Board research, Big Data Doesn’t Mean Big Brother.
Secondly, as a result of having access to all this data, companies can now learn important and powerful things.
Not only are executives being forced to report on topics such as diversity, gender pay equity and turnover, but they can also now use people analytics to understand productivity, skills gaps and long-term trends that might threaten or create risk in their business.
One organization, for example, found incidents of fraud and theft were “contagious”, causing similar bad behavior among other employees on the same floor within a certain distance. Another is using sentiment analysis software to measure “mood” in the organization and can identify teams with high-risk projects just from the patterns of their communication.
Click here to continue reading Josh Bersin’s article.
There’s a belief, that I hear frequently in the HR analytics community, that HR Analytics means a move away from intuition. This isn’t true.
Analytics using your own data is just one tool needed to conduct empirical decision making. Doing analysis – however sophisticated – can only be part of what you need to make great decisions. ‘Numbers are just another voice at the table’ as the saying goes.
As Sam Hill mentioned in his post on this blog ‘People Analytics – It’s a mug’s game. Isn’t it?’:
‘The People Analyst will keep formal and informal channels of communication open with HR process owners, line managers, senior managers, HR Business Partners and potentially external stakeholders to measure the pulse of their organisation and to identify emerging workforce issues or opportunities.’
As HR professionals there is usually a good reason that we hold the beliefs that we do. Many of us have built up knowledge and experience over many years of seeing similar situations, reading case studies and books or speaking to peers.
Managers too have built valuable experience. Many tend to have a good knowledge about what is happening in their organisations. They will have seen similar situations or even studied organisation theory on a general business course.
Discounting this experience and knowledge would be like starting with your hands tied behind your back, but unfortunately it’s common with some HR analytics teams.
Let me illustrate this with an example.
Suppose we are asked by a friend whether a coin is fair (ie as likely to come up heads as tails). They then toss the coin 10 times and it comes up heads 7 times. The chance that this will happen is in the region of 12%. This would be unusual but certainly not impossible. Do you tell the friend it’s fair? I suspect you might.
Now let’s change the situation slightly. Let’s say your friend then tells you he was given the coin by a magician at his daughter’s birthday party. My guess is that this bit of information would make you think that the coin probably isn’t fair. Maybe you’d think you were lucky to see 3 tails.
In both instances you’re updating your view based on the information that you had before. In the first instance you start with an expectation that the coin is fair as most coins have an equal chance of coming up heads or tails. You probably have some doubt but not enough for you to switch your view built upon a lot of previous experience.
In the second instance the knowledge the coin came from a magician changes everything. Given the source you’re now comfortable declaring the coin isn’t fair. Background information makes a big difference!
Click here to continue reading Andrew Marritt’s article.
Today’s world is more interconnected than ever before, across continents, companies, institutions, generations and cultures. But how do we handle the increased speed and pressure that accompanies this interconnectedness?
In this article I will review the topic of culture and launch the concept of cultural analytics using multiple examples. When leveraged correctly, cultural analytics can create a competitive advantage for your business.
Definition of Culture
Culture is a common term often used to refer to symbolic markers used by ethnic groups to distinguish themselves from each other. But the definition of “Culture” is not simple to understand and indeed a difficult term to define.
In 1952, the American anthropologists, Kroeber and Kluckhohn, critically reviewed concepts and definitions of culture and compiled a list of 164 different definitions. There is a nice compilation of quotations to define “What is Culture” by University of Warwick, UK.
They defined culture as shared patterns of behaviors and interactions, cognitive constructs and understanding that are learned by socialization. Thus, it can be seen as the growth of a group identity fostered by social patterns unique to the group.
If we look into the scientific background of the culture:
Culture is the accumulation of values, underlying assumptions and orientations to life, beliefs, policies, procedures, and codes of conduct. These are shared by a group of people that affect the behavior of the members.
In every human realm, behind an action, knowledge, and thinking, there are components which are cultural values. The simplest and most important value is the assumption of survival and reproduction. The more complex mankind became, the more complex their cultural values grew.
Behind every bit of knowledge, there is a cultural assumption which is strengthened by knowledge, institutions, and authorities.
The smallest units of a culture that can be perceived are the Cultural Traits. Examples of these are values, motivations, meaning, preferences, and self-understanding. In order to measure these, we need to explore and define them.
Cultural traits are very important to understand an individual within or outside an organization.
We connect with ease across continents, across companies, across institutions, across electronic tools for work and life, across generations, and across cultures.
We repeatedly hear – and it’s true – that today’s economies face unprecedented challenges and new opportunities. VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) is impacting your business.
There are many ways to handle the increased speed and pressure in business. But there is no doubt that current and future business leaders at least need to develop a new range of strategic leadership capabilities to remain effective.
One of the most effective response options is to equip leaders with a working understanding of Cultural Intelligence.
This is the capability to adapt and to operate effectively when interacting with other cultures and its successful application. It’s, in fact, providing a huge competitive edge in any multicultural environment.
Corporate agility, dynamic adaptation, organizational learning, leadership competencies, management thought processes and, in fact, the organizational model, determine cultural patterns.
These patterns of shared basic assumptions are learned by a group as it solves its problems of external adaptation and internal integration. If these solutions are considered valid, they are to be taught to new members as the correct way to perceive, think, and feel in relation to those problems.
It defines the corporate culture as well, which is easier to measure. Just setting up values and driving diversity does not provide gain on your corporate culture in terms of company’s vision. Rather, we need to capture its growth and variance over time.
Culture is something which can be derived from the combination of the traits of the individuals from the organizations and corporate behaviors formed from its root and ongoing journey.
Corporate culture is no longer just a “nice to have”. According to Deloitte, 86% of executives surveyed around the world rate culture as “very important” or “important”. 82% of executives believe that culture is a potential competitive advantage. But only 12% of executives believe they are driving the “right culture”.
According to Multigence, a software company, an organization must focus on fitting individuals into the corporate culture. However, culture isn’t just for your current employees.
It starts the moment a candidate first comes across your brand. And this immediately activates the drivers for your organization’s growth and success, as outlined below.
Right hiring and promotion
Proper alignments of skills, including the soft skills
Taking the right talent decisions
Fitting to the corporate branding
The culture of the organization is shaped by every single individual. Successful talent decisions will be driven by cultural-fit.
According to Jim Bryson, CEO of 20|20, a qualitative research firm, “Leaders drive culture, culture drives people and the people drive business.”
As a business grows, leaders must be intentional about developing and maintaining a culture. And they are also responsible for changing the culture based on necessity.
Why we need Cultural Analytics
Management guru Peter Drucker once said, “Culture eats strategy for breakfast.” Culture is a key building block. Whether you consider talent acquisition, people engagement, business performance or any transformation, culture is the centerpiece of it all.
Using cultural data to identify the behavioral features of a corporate culture and identifying highly-aligned individuals is essential to the future success of any business.
This data also enables one to hire, develop, and promote using insight about an individual’s degree of alignment with the organization’s current or target culture.
In many cases, we need to re-visit our current culture and drive it in a completely new direction.
As organizations set to transform themselves into technology-enabled, data-driven organizations, they must also shift their culture to a more agile, uncomplicated, collaborative, and customer-focused one that builds on curiosity, open-mindedness, experimentation and learning as core properties of its future.
To achieve these results we need to understand, collect and prepare the different cultural data for further modeling and analysis.
What is Cultural Analytics?
Well, Cultural Analytics is not a new thing to explore. Cultural Analytics is the use of computational and visualization methods to derive and leverage insight about shared values and believes in organizations. The concept of cultural analytics was developed by Lev Manovich in 2005 and the term itself was introduced in 2007.
Cultural Analytics showcase an organization’s shared values and beliefs as reflected in procedures, decisions, competencies and individual behaviors. Using cultural data sets, cultural analytics can help organizations interpret and correlate cultural artifacts. These insights and observations help leaders understand how an organization behaves individually and collectively.
Interpreting the current corporate culture can help an organization with their future success. The existing culture drives the critical decisions and actions that eventually achieve or shape the future goals and objectives.
Therefore, it is not only important to identify the measurable organization’s culture, but to also assess the organization’s ability to achieve its mission and objectives within the chosen strategy. And whenever there are gaps, the culture and/or strategy must be changed in order to achieve success.
Moreover, leaders gain insight into the organization’s strengths and weaknesses. This enables an organization to improve the current performance and to implement change or transformation more smoothly.
We have to consider that there is no one right culture. Different cultures present a specific set of advantages that should be understood and incorporated into the organization’s strategy to ensure an organizational effectiveness.
These values also have a role in the organization’s goals, performance measures, standards and expectations, policies and procedures, rewards & benefits, decisions, organizational learning, and continuous improvement programs.
Today, there are many routes you can take with the data captured from peoples’ skills, their behaviors, beliefs, and other soft aspects. With the addition of statistical data analysis and data visualization techniques, these insights can provide great interpretation of humanities, explanation of anthropology, and correlation of social science.
Corporate leaders gained an easy understanding of their culture strengths and weaknesses by grade levels/positions and by functions/units/departments. Thus providing them the insights necessary to better align and optimize overall performance across the organization.
Cultural Analytics can help an organization to reduce costs in recruitment and in reducing turnover. Moreover, it can also help companies improve decision making on hiring and improving team performance.
(Courtesy: Multigence GmbH)
Additionally, it also has implications in:
Measuring culture progress – to see how the organization is evolving towards its future
Telling a storyline – the actions, behaviors, and tangibles that make up the employee’s experience in an organization.
Knowing subcultures and patterns – You can reveal patterns, contradictions, connections, and correlations one may have been missing within unique departments, levels, tenure, gender, race, and generations.
Identifying skill gaps – You can focus on what’s driving your organization’s success and what might be getting in the way of it. It gives attention to the skills that’s matter the most for your organization and to the things about those skills that actually matter.
Discover Role Models for Culture – Organizations are increasingly going to the Employee Net Promoter Score as a measure of sentiment, connection, and affiliation.
Case studies for cultural Analytics are in many areas, such as:
Merger and Acquisition
Organizational Culture Change
Project Team Creation & Collaboration
Change Management & Transformation
Below are two examples (more detailed analyses are possible, but not mentioned in this article). These case studies are taken from Multigence GmbH as references and use a wide variety of analysis to reach their conclusions.
A Swiss company is known as one of the leaders in their respective industry. Many newcomers and other organizations look to this company as their role model.
A large number of leaders in this Swiss company have been around for a long time. They are individual thought leaders in their respective areas, but most of them are not collaborative enough and lack engagement on the organization level.
The company needs to find the right team and key leaders at different levels of engagement for their future transformation and organization modeling projects. If they fail to do so, it can lead to low productivity & performance and high turnover rate.
By comparing the culture profiles between leaders at different levels of the company’s structure using cultural analytics, one can easily distinguish the leadership skills needed for the future transformation and modeling.
Companies can easily set a role model for each trait of culture or skills for future transformation and changes. This gives an insightful picture of the company’s current culture on leadership and details where it is lacking.
Insight 02 (Merger & Acquisition)
An old, traditional and successful Swiss company in the field of wood-making machines, which was very successful in the world market, decided to innovate by acquiring a young and dynamic Italian software development company from Italy.
The merger succeeded, but the transfer of knowledge was, for a long time, only minor. The analysis showed: The cultures did not fit together. There was a culture of “Success and Tradition” against a culture of “Knowledge and Cooperation“.
Reason: The different management ideals of both companies clashed
The solution was made by recruiting a new leading employee, who was able to bridge the two cultural differences with his/her personal leadership skills (because he/she would be able to communicate with the strongly divergent cultural aspects of both companies).
The recruiting was carried out with Cultural Analytics, in order to make the bridging function of the new employee visible.
Below you see three images that visualize the culture scores for the Swiss and the Italian company, and for the new candidate. As you can see, the candidate’s profile fits right in the middle of the cultures of both companies.
Cultural intelligence is a term used in business, education, government and academic research. Cultural intelligence can be understood as the capability to relate and work effectively across cultures.
In business, Cultural intelligence, also known within business as “cultural quotient” or “CQ”, is a theory within management and organizational psychology. It claims that understanding the impact of an individual’s cultural background on their behavior is essential for effective business. Measuring an individual’s ability to engage successfully in any environment or social setting is also essential for effective business.
Cultural awareness alone isn’t enough. Organizations need leaders, teams, and staff who can simultaneously advance the values and needs of an organization, while adapting to the cultures touched by the organization.
Today, Cultural Analytics can play a pivotal role for the organizations as mentioned in various areas and cases. If you are interested to discuss more, please feel free to contact me.
Can you run a complex algorithm for me that will save me time and make my company more money? Sure, Joe, I will get right on that. This might not be how we interact with artificial intelligence (AI) today, but it may not be far off. The future of work is a growing conversation with a 40% increase on social networks globally with Germans holding a slight edge at a 42% increase. Could the automation of more complex tasks be on the horizon?
A couple of months ago, Adobe unveiled new social listening insights on the future of work, which were discussed in Berlin on June 27 at Think Tank by Adobe: The Future of Work from Berlin. The insights are a follow-up to a social report we released in February focused on global English speaking mentions, this time with additional regional insights focused on Germany and the United Kingdom (U.K.). Our analyses looked at around four million English and German social mentions between January 2016 and May 2017 on the future of work and the technologies that will help define it. Social conversations are mostly focused around a few top topics for Germans and those in the U.K. including automation, people analytics (including working environments), and transportation.
The combination of robots and machine learning has energized those in the U.K and Germany. Nearly 50% of Germans were excited about the global digital transformation (27%) and the possibility of saving time with mundane tasks due to advancement in AI and automation (22%).
The U.K. is the most eager about the prospects of saving time (30%) and big data analysis (25%) improvements that automation can bring. They feel that automation will lead to jobs that are not yet thought of yet.
Along with automation, we examined people analytics—a company’s use of people-related data to improve all levels of its business including working environment, worker motivation, and stronger team collaboration.
71% of companies now consider people analytics a top priority per a recent Deloitte survey. It’s not going unnoticed. The daily conversations about people analytics have increased in the U.K., Germany, and globally over the last year (55%, 28%, and 20%, respectively). Positive net sentiment in Germany (0.56) and the U.K. (3.53) suggests workers are excited about the prospect of how companies use of data about its employees will improve working environments.
Conversations about future working environments have the highest sentiment in the U.K. (4.31 net sentiment) and the highest share of mentions in Germany, where it is known as “arbeitswelt” (26%). Workplaces focused on collaboration, improved management, and automation of tasks could be key to a smoother transition into the greater automation of complex tasks.
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