These days, the hiring manager or recruiter will most likely ask about your salary expectations in the first or early round of the interview process. If you aren’t ready for this conversation, it can make you look unprepared, diffident, or worse….costing you the entire job opportunity.
So, let's show you how to avoid that and talk about your desired salary with confidence!
In this training, you’ll learn how to:
- Figure out the correct sites to explore while doing salary research
- Identify the tools you need to figure out your market value
- Choose a salary range that you feel comfortable with
Join our CEO, J.T. O'Donnell, and Director of Training Development & Coaching, Christina Burgio, for this live event on Wednesday, September 28th at 12 pm ET.
CAN'T ATTEND LIVE? That's okay. You'll have access to the recording and the workbook after the session!
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Subject matter experts in IT companies had the same problem. They were the experts but had no idea how to teach.
Leaders train and develop their teams. The team delivers better results. Parents teach and bring up their children. Hopefully, they lead more fulfilling lives.
Teaching is a key leadership skill. It can be taught.
Teaching ranges from a five-minute session on how to do something to delivering a doctoral-level course.
The shortest lesson and the longest course have certain things in common.
Aims
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Any unit of instruction needs a clear and precise aim.
Aims are best defined using “can-do” statements. They say: “By the end of this lesson/course, a participant can...”
You will have to ask yourself “What does 'can do X' mean?”
Your aim may be more complex than you thought. Instead of one lesson, you may need a course with multiple lessons and multiple aims.
Assumed/Required Knowledge
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There’s nothing worse than teaching people what they know already. However, your training session will collapse if your trainees do not know the minimum required to understand your content.
Define what they need to know before they start. Ask yourself if your trainees have this knowledge.
Look at your aims and ask yourself what they need to know. If you are teaching someone to create and use formulae in spreadsheets, your trainees will need to know basic arithmetic.
If you are training people to play their part in a process, they will need to know something about the whole process. They will understand the importance of what they are doing and why they have to do it in a certain way. Without this, they have no reason to try and do it properly.
Structure
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A good training session needs “inputs” and “outputs.” A typical “death by PowerPoint” session is all inputs and no outputs. At most, trainees will remember five percent of it.
As a bare minimum, a training session should include the following:
- A "Lead In”: The simplest is to tell participants what the session is about. You can also ask them what they already know about the topic, and what they want to learn. This way, you find out their expectations.
- Input: An input session should be no longer than 20 minutes. That is the average human concentration span. For teenagers, even that can be a stretch. Active learning is better than passive learning. Consider using exercises where participants match rules to examples. When going through the answers, you explain the key concepts.
- Output: This is the part most “trainers” forget! “Output" is an exercise or a test to see how much trainees have understood. Output activities may involve simulation exercises, role plays, or practical exercises. Trainees get the chance to “play” with their newfound knowledge in a realistic scenario. “Playing” is often very important to help trainees understand how to use what they have learned.
Delivery/Interaction With Trainees
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Successful training is never one way. You adapt to the trainees. You need to watch how your trainees react to the content.
My philosophy is if my trainees don’t understand anything, it’s not their fault; it’s my fault. If they don’t understand, I haven’t done my job properly. This is an important mindset.
Frequent changes of activity are recommended to keep your trainees’ attention. Pair and group activities are also recommended. Trainees engage more actively with the content if they are working with another person than they do in a question-and-answer session with the trainer.
Trainees need frequent opportunities to ask questions. Trainees may not want to ask questions in front of the class, so you can stimulate questions by asking a few of your own. This is where concept-checking questions come in handy. They can often be “What happens if…?” or “Why do we …?" questions.
Evaluating Learning
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Without evaluation, we do not know how successful our training is.
Many training courses limit their evaluation to a feedback form where trainees express their satisfaction. That does not tell us how well they understand and can use their newfound knowledge.
Where a training session contains an output activity, the simplest form of assessment is to see how well they complete the activity.
Other evaluations can include tests and quizzes. These can be gamified to make them entertaining rather than intimidating.
Looking beyond the end of the course, you can also ask trainees’ managers how much trainees have improved their performance based on the training they have received.
Next Steps
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When you deliver your next training session or “knowledge transfer,” think about:
- What must your trainees be able to do?
- What do they need to know before they start? How do you know they have this knowledge?
- How are you going to deliver your content?
- How will you check your trainees’ understanding?
Once you’ve thought about these questions and delivered your training, get in touch with me and tell me how it went!
Further reading...
For more knowledge transfer techniques read:
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Some firms will launch a simple branding campaign that says, "What does the data tell us?" or "Listen to the data." While doing something is better than nothing, we would like to think rightfully or wrongly so that in 2022 most organizations are beyond this approach.
In this post, I posit that data literacy programs need a bit of a reset, given the current point of view on data literacy existed coming out of the age of business intelligence. In the age of AI, self-service analytics, and digital customer experience, what goes into a data literacy program needs to be reset, and the view of what users know about data and analytics needs to be reframed.
The Data Literacy Programs Of Yesteryear And Many Still Contain...
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A curriculum that starts with, well, data is the currency of the times. It is the oil or glue that holds an organization together. Therefore, it is a strategic asset we must ensure is of quality, well-curated, and created correctly. These programs will then show how data is made, hopefully with examples/use cases from their business.
For instance, they will give a flow diagram of a customer service representative onboarding a customer and creating the initial customer record to show how an account should be set up and what happens if incorrect information is entered, or if the information is not filled in and left incomplete what that does to the ability to market and communicate with the customer.
This reminds me of Saturday morning cartoons I watched as a kid, where we learned about how a bill becomes a law. "I am only a bill sitting on capitol hill." While this is a valid use case from talking with business stakeholders, it borders on insulting as most people in their consumer life are engaging with a virtual assistant such as Alexa, wearing a smartwatch or tracking device such as an Apple Watch or a Fitbit, and using apps or other tools in their daily lives. Algorithms are everywhere, and people know them, and businesspeople know them as well. They understand what data is and how it drives consumer interactions. Many business stakeholders use operational dashboards and other data visualizations to run their businesses or departments. So, if we are ever to get into the world of insights to action and adaptive intelligence that industry researchers discuss, we need to go much further than using account setup examples. New examples such as how data analytics powers the guidance and advice advisors in a firm give customers through insight. Or how recommendation engines power next best actions which are sent to digital applications in real time are more modern relevant data analytics examples.
So, the time has come to rebrand data literacy, data analytics literacy so we can get more signals and patterns from the data we are collecting and move into improved decision-making.
So, What Is The Data Analytics Literacy Curriculum Of The Future Then?
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I recommend that in the spirit of test and learn and lean start-up/design thinking; the firm develops a vehicle to assess what their audience knows: briefly, whether that is a survey and focus group or playing a fun game (like jeopardy or other skill-based assessment) to assess the knowledge and maturity level that can help you develop a new curriculum.
Using the outcome of the assessment above combined with the five critical focus areas I am proposing below can help set the stage for a world-class modern data analytics literacy program. The program could include:
1. Data Fundamentals: What are the components of master data management? Quality, governed, gold standard data. This includes a description of the business benefits of governing data and clearly explains the importance of domains such as lineage, business definitions, and the data catalog.
2. How Data Turns into Information and Knowledge. What platforms are required, and why do business users need to understand these fit-for-purpose platforms?
a. What are the benefits of cloud-based platforms, data lakes, or lake houses, and what data science workbenches do, as well as self-service analytics, and why are these tools important? Why should the firm invest in these?
b. Business users should appreciate data Infrastructure-as-a-Service and managed cloud data warehouse such as Snowflake and what they can do to improve end-user access and protect privacy. What is the difference between structured and unstructured data and how data models and platforms may be different depending on the data structure in question?
c. Digital, Marketing Automation, and CRM Fundamentals and Platforms. An appreciation of real-time decisioning and omnichannel customer engagement including how data analytics informs customer conversations, lead targeting and qualification, and overall knowing the customer.
3. Lean Start-Up and Design Thinking. Fundamentals of design thinking, test and learn, control groups, and how to measure the success of tests—quantitative and quantitative research. Why is it essential to create a baseline, and how do you set up good KPIs to improve performance?
4. Analytics Fundamentals. Starting with the importance of defining what business problem you are trying to solve and then moving to select the proper analytics methodologies. Go to market analytics, segmentation, and a basic overview of the types of analytics, from descriptive to predictive. Program-related analytics and measurement, customer lifetime value, and engagement. As a consumer of analytics, the businessperson should have a basic understanding of how specific analytics solve business problems. Depending on the firm and industry the types of potential analytics should be listed and explained in the context of potential application to help solve business problems. For example, if you are in a digital business, digital analytics and digital signals to sales and how you measure SEM/SEO should be discussed. If you are in a retail business, site placement and drive time analytics should be reviewed. If you are managing risk, transaction scoring and analysis using deep learning should be reviewed. If you are in a large consumer business with lots of cross-selling, various marketing analytics should be reviewed. Consider establishing a baseline through certifications such as INFORMS CAP.
5. AI and Data Science Fundamentals. Explain the difference between how statisticians think versus data scientists. How does machine learning enable AI applications? The types of AI and how to work with data scientists. What are good questions for data scientists when building statistically oriented products and services for you? How do you know the model or tool is good and working correctly? What are the various types of models that can be built and what do they solve?
Given we are now in 2022, it is time to upskill and move to data analytics literacy. I recommend that some type of handbook be developed for the above and the course also be available through video training and updated at least annually. Also, consider a modular approach to the curriculum design where you define the categories and then can keep adding topics and use cases. This program is a dynamic living breathing program and not a point-in-time project.
In summary, these five areas will help business users of data analytics drive more impact with insights through understanding what is possible with the applications and use cases of data analytics. These five areas are not the only ones, and I would like to hear from the readers of this newsletter about what domains or subjects could be added to this curriculum.
I look forward to hearing your thoughts on whether the time for this reset has come in the way data literacy programs are approached. Also please share any success stories or changes you are seeing in the marketplace. What has your experience been with these programs? Are you using any third parties or consultants to set these up? What role do HR and internal education teams play in designing and enabling these curricula?
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