By Saurabh Gupta, Co-Founder at CODEX
Organizations are sitting on torrents of data; they just need right acumen to extract insights and be data-driven.
Most business leaders still rely on their experiences and gut feeling when making business decisions , a report by forester surveys shows that 48% of decisions today are completely based on quantitative information and analysis. A lack of data literacy skills obstructs the transformation into an insight driven organization.
As quoted by Jennifer Belissent — “The dangers of waiting too long are that someone else will do it first, and better” The organization who understands their customer well can serve them better and who understands the process can improve them. Data and analytics can help but only when well understood across the entire organization and used effectively.
What is data literacy?
Gartner defines data literacy as the ability to read, write and communicate with data which includes deep understanding of data sources, analytical methods and techniques applied to turn raw data into meaning insights which can help in validating the decision with data and hence add value to an organization’s existing capabilities.
All these thoughts boil down to a simple question, “Does your organization speak data?”
The ability of understanding and communicating with data language is a core skill required today. The art of successfully driving value from data and analytics is a core competency for making an organization stand out and leaving the competitors behind.
Data literacy is an underlying component of digital dexterity, which is defined as an employee’s ability to use existing technology to drive better business outcomes.
Why is data literacy important?
According to the Gartner Annual Chief Data Officer Survey , Poor data literacy is ranked as the second-biggest internal roadblock to the success of the office of the chief data officer. By 2020, 80% of organizations will commence development in the field of data literacy to overcome extreme deficiencies, 50% of organizations will lack sufficient AI, data science and data literacy skills to achieve business value.
As organizations become more data-driven , poor data literacy will become a deterrent to growth.
Data and analytics leaders are responsible for narrating the story for data literacy and highlighting the business value which can be gained.
Organizations can start assessing data literacy by asking few questions:
● How many people aligned with your business do you think can interpret straightforward statistical estimations such as correlations or averages?
● How many managers can construct an optimal business case based on available data and relevant numbers?
● How many managers can access the output of their systems or processes?
● How many data scientists can explain the output of their machine learning algorithms?
Strategizing data literacy
The data literacy programs within organizations must start with general awareness and knowledge of what is data today, how it can be used for generating value, how can we collect and organize data, how can we protect data. Building a data literacy program will improve everyone’s ability to read, write, argue, analyses and create value with data.
Start by identifying the data speakers within the organization’s i.e., Business analyst, data scientist or architecture who can speak with data as they serve as a good mediator in incorporating data driven culture within the organization. Organizations can also identify the gaps where data isn’t utilized to its full potential and can conduct data literacy workshops, training, assessments for smooth induction of data literacy.
The Chief data officer (CDO) is the most important person to lead and build data driven culture. CDO is an expert in working with data and can inspire organizations to embrace the data driven culture and if the data literacy program is embedded in the CDO’S mission the potential resistance for the program can be reduced.
Six steps needed for incorporating data literacy program
Regardless the size or focus of business, organizations can induce a data literacy program following the 6 steps mentioned above, the cycle is an iterative process for growing data culture.
Step 1: Planning and Vision
For most of the enterprises, the data literacy program starts with a discussion drive between the data officer, competency managers, and the people leaders. The discussion should cover three critical aspects i.e., the participants, duration & budget, and the time needed to complete the program.
Step 2: Communication
Data Literacy is a workplace transformation program, not a technical training. Therefore, keep a note to communicate and communicate more.
i. Communicate the program objectives
ii. Communicate the benefit on personal & professional growth and productivity
iii. Communicate the trends by drawing industry parallels, case studies, and testimonials
iv. Communicate the examples set by the leadership
v. Communicate and broadcast the internal stories
Step 3: Workforce Assessment
A simple assessment will reflect the level of understanding of the workforce. Based on the assessment results, the leaders can segment the workforce into various personas. Based on how everyone performs in the assessment, the organization can recommend and build learning paths.
Data Learner — He has heard about data and is willing to grow in the field, but lacks subject knowledge
Data Apprentice — Comes with a basic level of expertise in data handling and analysis. Willingness to grow in the field.
Data Journeyman — Works on routine data activities like data collection, processing, and basic analysis
Data Star — Expert in handling all stages of data lifecycle management — data collection, processing, and analysis.
Data Rockstar — Has the ability to build stories with data and communicate to business stakeholders.
CODEX Data Literacy Free Assessment — https://naviz.qualtrics.com/jfe/form/SV_8vlrNI8nDCuw1Qa
Step 4: Culture Discovery
Learning is a habit and habits build culture. Organizations who have successfully implemented data literacy programs have a culture of learning and growth. Data literacy is not meant for the workforce to perform better in their projects, but it is to build habit of critically analyze data to make better decisions. Now here comes the tough part — why should we learn? Resistance, push back, and interrogation eats a lot of time and patience.
The only way to deal with change is to ask right questions with the right intent. The more one gets involved, the clearer the picture (and hence the change) will be. Raw questioning and resistance defeats the objectives. So, for leaders, ask more How’s than Why’s, use “Shall we” in place of “You should”, and empower people to drive change.
Step 5: Prescriptive Learning
The prescriptive learning is designed based on the different levels of data literacy; this roadmap helps in ensuring that no learner feels lost trying to absorb the concept or is bored by wasting time. The success of the program largely depends on devoted learning time in daily schedules incorporated with consistent assessments and feedback for improvement.
Step 6: Impact measurement
The data literacy program should have a preset success criteria that can be visited in due course of time to get a glimpse of the program on the ground and if needed, calibrate the program strategy. The metrics can be sign up, active users, program completion, and very importantly rewards & recognition.
Challenges with Data literacy training programs
“Large enterprises with strong corporate data literacy have shown up to 5% higher enterprise value $320 — $534 million.”
The growth of organizations solely depends on how the leaders responsible for incorporating data literacy in organization think about the data literacy program, data literacy is not equivalent to data training and it’s all about how effectively they plan the programs within organizations.
1. Data literacy by Persona
The plan for making a common data literacy program for everyone regardless of their previous experience, knowledge, skill set or role in the organization. For example, a data leader might have a different learning needs, while business stake holder might have a different ask.
A persona based data literacy program takes into consideration all the factors which reflect the need of a persona (leader, business, technology).
2. Data literacy training is made up of MOOCs
MOOC (massive open online courses) platforms offer numerous knowledge tracks and there is nothing wrong in adopting them. However, data literacy is best learned when you have data, an exploration tool, and a simple problem statement to chase. Instead of just reading the scripts, you will see the problem through data lens.
Remember data literacy is about learning skills which will help you more than anyone else.
3. Training does not include use cases
Another common mistake which organizations make is to equate training with data literacy, it is not an easy skill to learn if it doesn’t include use cases and it is likely to be forgotten after some weekends. Mentoring employees while they apply their learning on use cases will help them build effective skills for the longer term, and for achieving the best data literacy outcome employees should immediately apply what they learn in their current workflow.
Conclusion
Data literacy is a requirement for making effective insight driven decisions, with short term investment in the program, data tools the organization can democratize data. Successful data literacy programs can play a significant role in creating long term business growth when coupled with right executive support, strategic goals, and established metrics.
