The process of shifting processes and tools from a traditional offline to a digital environment is known as digital transformation. Data-driven digital transformation improves the company, provides value through better knowledge, and aligns digital and offline data.
To compete in the new data economy, businesses in various industries have realized the need for digital transformation. Companies like Uber, Amazon, Airbnb, and Stripe have leveraged digital transformation technologies to disrupt the transportation, commerce, travel, and banking sectors in the last two decades. The business world has gotten extremely competitive in recent years. However, it is reasonable that businesses that use the old way as a business strategy may find themselves on the outs in the not-too-distant future. As a result, adopting AI with data at its core is critical for the improvement and long-term viability of the working system.
When information becomes a valuable asset, data becomes a strategic priority. Data is becoming a competitive differentiator for top organizations as they focus on digital transformation while still in the early stages of adoption. Data serves as a significant catalyst for a company's digitalization and transformation activities.
The Covid-19 pandemic has accelerated the acceptance of digital transformation as corporations and organizations began to operate remotely from all corners of the globe; the necessity to accelerate a technological shift that is already underway is becoming apparent. Companies immediately adopted AI and its characteristics to maintain income throughout what turned out to be regular. According to Gartner, 90% of company plans will clearly list information as a significant enterprise asset and analytics key capability by 2022.
However, despite enormous digitalization, 70% of digital transformation programs fail to achieve their stated purpose, as predicted by McKinsey. The crash can be attributed to several factors. However, a major flaw is failing to recognize that data transformation is a necessary component of digital transformation.
The first step toward digitalization that an organization should take is to engage in broad data literacy skills. The next step is to invest in data infrastructure, data tools, better procedures, and more agile organizational structures. By skipping the initial phase and moving on to subsequent investments, the business routine will be disrupted.
Rather than pursuing data scientists and analytics teams, training the existing workforce in data fluency is preferable. Surprisingly, data fluency is an all-encompassing framework for addressing organizational challenges, with each solution backed up by data. In a word, adequate digital fluency is required to leverage success in digital transformation.
Data Literacy Adoption
Digital world leaders will emerge from the enterprises embracing and extending analytics throughout their organizations. But low data literacy is holding many teams back and stalling digital transformation initiatives. MIT defines data literacy as the ability to read, work with, analyze and argue with data. It’s a skill that empowers all levels of workers to ask the right questions of data and machines, build knowledge, make decisions, and communicate meaning to others. Yet new research shows business leaders are struggling to master data literacy themselves, leading to widespread deficiency in data confidence. Without data literacy, leaders can’t thrive in today’s analytics economy, nor can they drive any cultural change toward leading with data across their organizations.
In a recent Censuswide survey on behalf of Qlik, the research surveyed 7,377 business decision-makers (junior managers and above) which the respondents were from across Europe, Asia, and the US which was carried out between August 2017 – February 2018 showed that the vast majority -85%- of data-literate employees say they’re performing very well at work, compared with just 54% of the wider workforce. This represents a significant opportunity for businesses to transform existing cultures without much resistance from their employees. . Censuswide abides by and employs members of the Market Research Society based on the ESOMAR principles.
Organizations have been hearing about the need to use data all the time but might not necessarily know where to begin. Helping the executive and leadership teams understand the need for data literacy and how to create that culture helps the culture permeate throughout the organization and helps all employees understand that leadership will support their desires to become more and more data literate. This is the other executive and leadership level: they need to buy into the data revolution itself.
To be successful and compete in today’s world of data, an organization’s top levels cannot look past the need for data and its power. Doing so can hinder the entire culture that needs to exist. Regarding a data literacy culture and the characteristics we mentioned earlier, the executive and leadership world touches upon each one. The leadership in an organization helps drive how data is used and governed, how the learning is done, a mentoring program, and so many more characteristics and facets. The role and messaging from leadership cannot be overemphasized.
The second role we want to explore within an organization is that of people leaders. Again, people leaders will see each fall into one of the four data personalities but play a critical role in an organization. People leaders set the tone for their teams. If executives set the tone for the organization, the people leaders set the tone for their teams and helping them to understand how important data literacy is. People leaders can help their respective teams establish a regular learning cadence. They can help set the example of learning by taking training to work on their data literacy skills. Along with learning, people leaders can really start to use the vocabulary of data in everyday work conversations and push and share the desire to use data more and more into their worlds.
When the leadership of an organization lays out a vision, it is the teams and business units throughout an organization that is laying the groundwork for that vision. If people leaders push for more and more insights from data, it drives an overall data literacy culture. People leaders really help drive learning, analytical thinking, statistical and visualization use, and the list. People leaders play such a critical role in a data literacy culture. One key thing to realize is, the executive team and people leaders of an organization are not data personalities. Each executive or leader within an organization will fit into one of the 4 data personalities we are about to explore and will need to be approached with data accordingly.
Data scientists play a crucial role in organizations, especially within the world of data and an organization with data. Data scientists know statistical concepts, have analytical mindsets, know how to work with data, so what role do they play within a data literacy culture? Data scientists play a crucial role: mentoring. This might not be where you thought we would take this, but data scientists have solid knowledge in data literacy and need to learn the personal skills to mentor others, which might help uplift skill-sets.
This might not have been a part of their jobs historically, but data scientists need to start working to help others in their roles for the data literacy culture to exist. Along with mentoring, there is one key thing that data scientists should be doing to ensure they are properly a part of the data literacy culture: continuous learning and improvement. Data is constantly shifting, so a data scientist needs to ensure they do not get bogged down and stuck in habits, thinking they don’t need to learn anymore. Data scientists should constantly be learning and shifting, keeping up with the trends. From the characteristics, data scientists should encapsulate all the characteristics, minus, maybe traditionally, the mentoring aspect. Now you can see why it is important for data scientists to buy into the data literacy culture and look to mentor people; they possess more technical skills in the world of data than probably anyone else.
Moving throughout an organization, let’s discuss many employees: those who use data but may not have
a full data literacy skill-set but need to work towards being fully data literate. There isn’t necessarily a direct name for these employees to describe them as data champions. This group of employees uniquely drives a data literacy culture. They may be eager to learn more about analytical mindsets, statistics, visualizations, etc. The key for this group of employees is to continue to develop more in data literacy, to grow their skills to read data, work with it, analyze and argue with it.
As they do so, they grow skills to mentor, to be people leaders. This group of employees needs to grow visualizations, analytics, and statistics as much as they can. As the executive team lays out the vision and people leaders work towards it, this group of employees can work to really enable the vision through data, becoming data fluent, and communicating with data as much as possible. This increase in the use of data helps to permeate throughout the organization the new culture of data literacy. Along with the expanded use of data, this group must continue to increase in all aspects of data literacy and fluency through learning and practice. In so doing, this large group of employees plays a crucial part with all the characteristics and pieces of a data literacy culture.
The third group of employees that fit into a data personality, which may be the biggest group within an organization, is the data dreamers. This is a group of employees who desire to learn data, utilize it, and help drive an organization toward data-driven decisions; they may not know where to start or what to do. This group of employees embody the essence of data literacy culture: they are trying to continually learn, working towards strength in data fluency and building visualizations, and understand the importance of all things data. Through this work, they help push the executive leadership’s vision, help their individual teams work towards goals utilizing data, and are especially interested in being mentored by the data scientists. When data dreamers embrace their roles in a data literate culture, the culture can move forward and thrive.
The final group we want to talk about and discuss regarding the data literacy culture is those who may not feel the need to utilize data or those who downright don’t like data: the data doubter. This group places itself in an interesting and, what can be precarious position within a data literacy culture. There can be such a delicate balance to ensure all employees buy into the new program, and the key with this group is helping them see the “why” behind data and how it is a necessity in the data-driven world. We want to ensure we are not offending or causing a big disruption to these employees. It is also imperative to understand data doubters can be great employees, individuals, and leaders; they need a nudge in the right direction. We want to create an environment of learning so that these employees can progress and grow in the world of data too. Each of these groups of individuals and employees in an organization plays a critical role in the success of a data literacy culture.
Check out this video for the summary:
4 Steps Data Literacy Framework to be Data Fluent in your Organization
For a person to be deemed data literate, ‘hard' skills such as data analysis/extraction, programming language, parallel processing, SQL, and information normalization were formerly required. However, digital platforms have taken on the "heavy lifting" for businesses by incorporating these capabilities as add-ons to their solutions. Although data scientists and analysts will still need to learn hard skills, training for general stakeholders should focus on soft skills such as:
How can the authenticity of AI output be determined? (and not merely take it at face value)
How to get the most out of the platform solution
For cyber-security reasons, data security, management, and accountability are essential.
How to use critical thinking and balanced judgment to understand facts in a business situation accurately.
Let your stakeholders listen to your data literacy ‘vision.'
Data literacy, like digital transformation, will fail if stakeholders are unaware of its importance and the value it can offer to their work. The justification for the data literacy campaign and the impact worker fluency will have on corporate goals should be presented in unambiguous terms by management. Ideally, this presentation should be given by a "digital head," such as a chief data officer, whose credentials will persuade employees that the company is serious about data literacy.
The Chief Data Officer will take charge and galvanize the workforce. He/She/They will explain how data literacy will aid workers in making better decisions and increasing overall productivity.
Perform a data literacy assessment
There are five levels of data fluency, according to Gartner, worldwide research and advising business.
Literacy: The ability to speak, write, and interact with data to solve problems or make decisions.
Fluency: Data literate throughout the majority of a given industry's business domains.
Conversational: Has a basic understanding of data concepts and how to apply them.
Competency: Ability to conceptualize, develop, and apply data from concept to conclusion
Multilingual: Data literate in a variety of industries and business domains.
The following step is to assess workers' grasp of the data literacy training after stimulating their interest. A data literacy assessment test can help in this regard. The test findings will allow the company to personalize training to the specific needs of individual employees.
Provide training regularly
Data literacy can only be achieved through high-quality data training and education. Workers should be split into learning groups based on their competency test results to accommodate disparate data fluency and stakeholder requirements. Rather than forcing everyone into the same training kit, this approach ensures that workers, in addition to studying specific areas important to their jobs, are educated at their own pace.
Study groups should be manageable to allow for in-depth talks and for designated tutors to track individual progress. Also, data should be supplied and made easily available so that workers may put what they've learned into practice right away. This can be accomplished through data literacy programs (individual or group) or real-world implementations based on stakeholder roles. In addition, if workers score poorly on practice tests, follow-up training and specialized mentor groups can be allocated to them.
E-learning or self-paced learning are used by many businesses to implement their training programs. The problem with E-learning or self-paced learning is that, while they are not for specialized training, they may not meet your organization's unique demands or provide comprehensive solutions for your employees.
Setting up an in-house data literacy academy or partnering with an external academy is another option. The ultimate result will be an end-to-end strategy that maximizes impact through imaginative and long-term training.
Data literacy is a lifelong learning experience that never ends. The first task is to change stakeholders' minds about data and stimulate their interest in learning about it. Even if this change occurs, it will take time for everyone to become fluent in the data language. There may be internal and external problems that attempt to destroy the program, but don't be discouraged.
Finally, you'll be rewarded with an internal data excellence initiative that allows for more accurate decision-making, more productivity, and increased revenue.
We just had Building A Data Literate Culture In Your Organization on 21/7/2021, and it was a full-day training. If you wish to have this training as private in-house training for your organization, please contact us via firstname.lastname@example.org.