Many have tried explaining data literacy concepts and fostering a data culture in organizations to improve business decision-making. Data-driven decision-making comes by dedicating completely to data and setting aside gut feelings about business growth.
From Gramener, data science whiteboards in an initiative to teach data literacy and data maturity to enterprise decision makers through simple 5-min videos. In this article, we’re compiling 3 seasons and 20 data science whiteboard videos from our Chief Decision Scientist & Co-founder, Ganes Kesari.
Table of Contents
Data literacy refers to business users’ ability to understand the available data, its uses, and its limitations. Data literates can also combine data from different sources. Moreover, they can also improve data quality using trusted information from third-party sources.
In short, data literacy enables business leaders to translate raw data into real business value.
According to Accenture, only 21 percent of workers have faith in their data literacy skills. So only one in four workers feel that they effectively leverage data at their jobs.
According to the Data Literacy Project, organizations that score high on corporate data literacy enjoy higher enterprise value by more than $500 Mn.
Data literacy is a must to drive decision-making across modern businesses. In order to help enterprise executives understand the importance of data, we kickstarted the whiteboard video series.
These videos aim to help entrepreneurs and business leaders understand where they stand in their data journey and how to improve their data maturity and build strong data science capabilities.
Ganes Kesari has been a prolific public speaker since 2012. He has conducted seminars in prestigious academic institutions such as ISB, Hyderabad, etc. Since 2018, he has been speaking more regularly at US-based events, such as the ones organized by Microsoft.
In 2019, Ganes spoke at a series of notable events, including TEDx, Strata Conference, etc. In 2020, the Covid-19 pandemic struck, disrupting event calendars worldwide. Towards the end of March, the organizers of South by Southwest (SXSW), one of the flagship events on the US calendar, informed Ganes via email that the said event may have to be postponed or held virtually.
A week later, SXSW laid off 80% of its workforce and canceled the event. In the subsequent weeks, confusion and disruption engulfed the events industry, causing most global organizers and managers to throw their calendars out the window and their hands high up in the air.
Sensing an opportunity in this chaos, Ganes embarked on an experiment during the lockdown. Moreover, he wanted to find out if it was possible to pick a topic in data science and explain it to the target demographic of CXOs in an interactive manner in 5 minutes or less.
Working on his kid’s whiteboard, Ganes shot his first video and uploaded it on LinkedIn, garnering rave responses. Two more videos quickly followed, and the three videos received more than 3,100 views individually.
The marketing team at Gramener jumped on the bandwagon, helping Ganes polish his videos with better lighting, video and audio editing, etc.
Three years and 20 episodes later, Ganes’s whiteboard videos are still going strong. They have attracted more than 70,000 views on LinkedIn alone. So on average, each video has been seen by 3,000+ unique users.
The icing on the cake? The latest episode, the 20th in the series, was featured on LinkedIn News.
Ganes’s whiteboard series has attracted the attention of industry experts and leaders alike. This includes professors from such coveted institutions as The Wharton School. Often, existent and potential clients reveal themselves as avid followers of the videos with enthusiasm during meetings.
Almost all the whiteboard videos follow an identical format. In each video, Ganes addresses one question and tries to answer it within five minutes using the following flow –
These videos map to the five stages of the RADAR framework:
The questions that the clients repeatedly ask are handpicked by Ganes to be covered in these videos.
This video is about building data roadmaps. Some organizations, unfortunately, face stagnation after undertaking a series of data analytics projects. So after completing a handful of BI and analytics projects, adoption suddenly comes to a standstill.
Leaders can avoid a stalemate through the following five ways – syncing the data science strategy with the overall business strategy, onboarding data leadership, demonstrating RoI and securing funding, collaborating better by revisiting the organizational structure, and upskilling talent.
Leaders should ask themselves the following three questions when prioritizing their data science projects – How soon do I need this solution? Is this project feasible, and will it have a financial or qualitative impact on my business? Do I possess the requisite budget, resources, and data to undertake this project?
Urgent, feasible, and high-impact projects that deliver quick wins should always be the top priority.
This is a pet peeve and a common question for many project teams. Sometimes, there is an over-focus on the accuracy of machine learning algorithms. Therefore, customers get obsessed and ask for 100% accuracy.
This video explores the following four questions that can help leaders contextualize the accuracy of their ML algorithm in the best possible way – What is the human accuracy rate? By how much can the algorithm improve? Can the outcomes be bettered with a human-in-the-loop approach? How will this impact the business RoI?
This video explores how to balance algorithmic complexity and explainability – black box vs. white box algorithms. Where do you draw the line? How should you pick the right algorithm? What is the correct approach?
The following four steps can help ascertain how simple or complex the ML solution should be – assessing the margin of error, gauging the users’ data literacy level, leveraging accuracy to build an algorithm leader board and using a 2X2 matrix to evaluate these data points.
This video deals with the concept of augmented intelligence. Do humans have a place in AI? If yes, where? How do you incorporate the human element into a machine-learning algorithmic solution?
A human-in-the-loop approach has five benefits – increased business value and RoI, more stability, better accountability, correct machine bias, and fostering trust, understanding, and empathy.
At Gramener, we use a mix of data-driven and business-driven approaches. This also includes identifying the users, understanding what they want, knowing which initiatives will help accomplish the same, identifying the questions that need to be answered, and the data required to answer said questions.
Sometimes, clients building their data science team from scratch enquire about the roles they should be hiring for. They also want to know about the mix of skills for each position.
This video lists the five roles vital to developing any data science solution and adding business value.
This is a critical question that clients are prone to asking repeatedly. However, quantifying notional outcomes and attributing the results are the two major hurdles to calculating the RoI of data science projects.
Our video covers the following four-step approach to measuring RoI – set short and long-term business outcome expectations, use existing and new data to measure metrics, assess your project impact and calculate the total costs and gains.
Of course, data analytics alone will not lead to better business decisions. According to Gartner, decision intelligence will be one of the most critical buzzwords of this decade, with more organizations paying close attention to it.
Moreover, decision intelligence comprises of 3 major disciplines: understanding user behavior or social sciences, data science that consists of actionable insights and consumable recommendations, and managerial science or change management.
A successful data science strategy must align with the business vision of an organization. This involves identifying the following key elements – vision, governance plan, goals, top challenges, target stakeholders, top enablers, strategic initiatives, sources of funding, measures of success, and capabilities (people, process, and technology).
A Gartner survey revealed the following 5 top challenges for a chief data officer (CDO): absence of a data-driven culture, shortage of capital, inadequate data literacy, lack of proper skills, and absence of proper focus or vision.
A CDO can overcome these barriers by ensuring that D&A initiatives reflect the overall organizational strategy, align with business priorities, have an established implementation process, respond to business outcomes, and are adopted across the organization.
This video takes us through the five ways a data-driven culture can be created within an organization – initiate a top-down approach, starting with the leadership; spread the message across levels using change agents; eliminate fear and ignorance by improving data literacy; allow ready and easy access to insights and information and get employees habituated with data and data-driven processes.
At Gramener, we run data maturity assessments for clients. Sometimes, clients who have already been assessing their data maturity want to extract more value from the process and build a roadmap. This video shows how we help our clients assess their current state of data maturity so that it can deliver more value addition to their business.
This video also explores why data maturity is vital to benchmark and deliver business value.
For any organization, building and charting out a data roadmap of projects and capabilities begins with a candid assessment of their current state of data maturity, where they aspire to be, and evaluation of gaps and strengths.
A good data roadmap monitors progress across the following five dimensions at each stage of the journey – Vision, Planning, Execution, Value Realization, and Data Culture.
What value does our maturity assessment methodology bring to your business? This video explores if data maturity scores can help chart a client’s path with data.
Viewed separately, maturity assessment scores may be misleading. However, when combined with in-depth reviews of organizational practices and interviews, it can help uncover gaps and plan targeted actions.
The questions a client asks potentially reflect the state they are currently in, in their data journey. Organizations trying to figure out which business problems to solve are at the early stages of data maturity.
Conversely, organizations at an advanced stage of data maturity strive to inculcate data-driven decision-making into their culture.
Employees with a sound grasp of data and domain are data champions. They are usually early adopters of new tech solutions and help foster a culture of data-driven decision-making within the organization.
Companies that seek to improve the adoption of D&A solutions embrace data champions by nurturing, empowering, and rewarding them.
Within an organization, silos are often a common challenge to data adoption. People fail to orchestrate across business and technology teams.
Data and Analysis (D&A) leadership can eliminate silos using the following steps to improve the adoption of data science solutions – aligning D&A strategy with execution, implementing D&A projects, and monitoring their progress and results.
It is possible to make data-driven decisions a habit within a firm in the following ways – identify the right cue and create a habit loop, establish a routine and reward the use of data. This improves the adoption of D&A solutions and powers business growth.
Sometimes, customers rush to advanced analytics without building solid data foundations, resulting in failures. Therefore, if we take care of our data quality, it helps our downstream adoption.
Once the data is in place, organizations can identify small but impactful problems, assess data feasibility, pick quick wins, choose simple analytics, facilitate user adoption, establish trust, and plan for the future.
So, what is the most challenging aspect of making a whiteboard video? Is it explaining complex and sophisticated data science concepts in a simple and lucid manner? Or is it setting up the whole apparatus – the whiteboard, focusing the camera, and getting the lighting just right?
Or is it what comes afterward – the audio-visual editing resulting in the final version and its marketing and promotion through various channels to ensure that it reaches those who will benefit the most?
None of the above, according to Ganes! Chuckles, the creator of this hit series, “starting out, the most difficult part of making these videos was creating the thumbnails. Fortunately, I was rescued by my kids who helped me pose for the thumbnails, even posing with me in some cases and helping me with options to choose from.”
You can view the complete video series on the Gramener website or watch the whole series on YouTube.
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