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Accelerating Data Transformation

The past year has dawned an era of great acceleration. The covid-19 pandemic forced organizations to think, act, and work digital. As the lessons learned from this acceleration are still bearing witness, one thing remains clear: digital transformation is unsuccessful without data transformation. 

In this webinar, Adel Nehme, Data Science Evangelist at DataCamp, will discuss the state of digital transformation in 2021, and what are some lessons learned from the great acceleration. Moreover, he will discuss how people, not technology are the key driver to successful data transformation, and how data fluency, is the differentiator between organizations that succeed in the digital age, and those who fall behind. 

Key Takeaways:

  • Get an overview of the state of data-enabled digital transformation in 2021

  • Understand how people, not technology, is the key driver behind successful data transformation 

  • Learn how data fluency, is the currency of scalable data transformation


Webinar Transcripts

Agenda

Now let's start with today's agenda. What are we going to talk about today, how are you going to break down the agenda. So first of all, I want to talk about the two decades in disruption, and the state of digital transformation 2021, that is to say what has happened over the past two decades in digital transformation, what has happened specifically last year with COVID-19. 

Then I want to talk about how data transformation really underpins successful digital transformation. And I'm going to talk about why that is the case, and how their transformation is not just a technology investment but it's also people investment. And then we're going to discuss some practical key steps to scale the people in your organization for the data driven age. Finally, we're going to end with some closing notes and a Q&A. 

Two decades in disruption & the state of digital transformation in 2021

Now let's start off with the past two decades in disruption and how really the state of digital transformation is shaping up in 2021. I mean, we've all experienced this both as consumers, as people working within the field. The past few decades has been really animated by massive disruptive forces coming from the technology industry. And that has disrupted many industries as we know today, such as finance, travel, retail, transport. 

This is just cherry-picked, a few industries. But we can think about it across the board even in health care, even in FMGs, government. And really what we can say the what is common across these industries is that this disruption is coming from organizations and startups that are digital first digital natives that operate purely on a digital basis and that have these digital shops to be able to deliver on these digital experiences. Now I know I'm saying digital a lot. 

But essentially organizations such as Stripe, such as the Cash App. Such as the Airbnb, Amazon, Uber, and Lyft completely revolutionized how we experience as consumers interactions and services within their spaces. I no longer go to hotels because I have Airbnb. I no longer go buy my things because I can go on Amazon and book them immediately via e-commerce. I no longer take a taxi. I ride hail via Uber. And that is purely offered through their digital channels and because of their digital solutions. 

The digital transformation catch-up game

So naturally a lot of organizations in this space have been trying to catch up. This is what we call the digital transformation catch up game. So this has been happening for the past 10 years. I'm going to cherry pick a few statistics here from the past four years. 

For example, according to Deloitte, 87% of organizations in 2016 believe that digital will disrupt their industry. You can bet that it's even higher today. 23% of organizations in 2018 only 23% believe that they've completed their digital transformation journey. And in 2019, the average annual growth rate of digital transformation initiatives was 18%, which is quite sizable from a growth rate perspective according to IDC. 

In came along 2020

However, then came along 2020. And COVID-19 really proved to be a black swan event that really further accelerated the shift to digital by several years. Now I don't want to downplay the effects of the pandemic and purely talk about the digital initiatives and the digital acceleration that has happened. The pandemic has really created a lot of suffering for people from a psychological perspective, health perspective, all across the board. So I don't want to take that away. 

But really focusing in on the digital transformation perspective of it and how it has happened, COVID-19 really accelerated the digital transformation journeys for many organizations and forced them to think digital, even if they didn't have the capacities. So to give you a few examples as well, according to Gartner in 2020 in the wake of COVID-19 7 in 10 boards of directors have accelerated digital business initiatives despite massive budget cuts. And of those budget cuts, only 9% came at the expense of digital transformation initiatives. 

So many industries experienced massive budget cuts across the board because of the pandemic and only 9% of those were at the expense of digital transformation initiatives. It was the number one priority for CEOs in 2020 to lead digital business and transformation activities. These are all coming from Gartner. And 10% on digital transformation globally has increased in 2020 alone. So seeing the growth rate in 2019 being almost 18% you can take on top of that as well an acceleration and that growth rate moving forward. 

Covid-19 accelerated digital transformation

Looking as well as the amount of consumer digital adoption across a variety of industries according to McKinsey, we can see that the digital adoption of new customers, first time users using digital channels really jumped by a long shot across a variety of industries from banking, entertainment, grocery, apparel, utilities, telecom carriers, travel, and insurance. And I'll even turn this around to the audience here and ask them according to your experience how much do you think that you've been using digital channels because of COVID-19 pandemic? 

And think about it from an aggregate perspective, you can see that the acceleration these industries have had across regions is almost on average of three years to four years of digital acceleration across all industries around the globe. So we're talking about-- in the global economy, for example, acceleration of digital adoption across industries was almost around three years. But looking at Asia it's four years, and the remainder of Europe and North America it's three years as well. 

So this has massive impacts on how we experience product services and how organizations roll them out and make value of them. And we're going to discuss that shortly. Looking at as well digital processes, how we experience work as well, for those of us who were lucky enough to be able to stay at home during 2020, COVID-19 really ushered in an age of remote work. Looking, for example, at various industries like professional and business services, finance, manufacturing, education, information services, there has been a massive jump towards remote work thus enabling the creation of new remote processes, new remote learning opportunities, new remote onboarding capabilities, and the emergence of a lot of software that is designed to make working remotely easy, which requires as well a digital data native organization. And we'll discuss that how. 

Digital transformation initiatives often fail

But, however, before we discuss that what I want to highlight is that oftentimes digital transformation initiatives fail. And according to McKinsey, for example, 70% of digital transformation projects fail achieving their stated objectives. And my hunch-- I can be biased-- is that they are related to the two statistics that I'm about to discuss as well. 

So according to Forrester 60% to 73% of organization data is untouched for data analysis. And according to Gartner, 50% of organizations do not mention data analytics in their corporate strategies, as in an overarching corporate strategy. So even if there is a data strategy within the organization, it's not foundational to the strategy of the organization itself. 

Data transformation is the heart of digital transformation

So looking at it further from a resource from Gartner looking at Mike Rollings, a research VP at Gartner, he couldn't have said it better. "Leaders need to look at data first to succeed in their digital initiatives rather than treating them as an afterthought to help with ad-hoc projects." So let's discuss why. 

Because if you're a company, if you're an organization that is rolling out digital products, services, and processes, you have a lot of questions you need to answer and iterate upon that require you to scale to be able to be successful at digital transformation. 

These questions are like, what is driving my customer acquisition, what are the best marketing channels, what segments are we reserving best, how can we improve our customer experience, what features do our customers want the most, what features do they want the least, what should we prioritize in our roadmap, and how do we become operationally better. All of these questions are inherently data questions. They require people within your organizations to be able to answer them, and they require infrastructure within your organization to be able to have access this data at ease. 

So before we talk about data transformation and how we do that, I want to ask for the audience here, what is the biggest priority in your digital transformation strategy? Is it creating digital products? Is it creating digital channels, data analytics, or is it technology investments? Please let me know. 

I'm going to give it just one minute. I'll be super thrilled to just check out the different answers that you guys have and what's your experience in your organization. And if it's all of the above. Please let us know in the chat and what you think your biggest priority is. So I'm just going to give it an extra 30 seconds. I can see that 44% of you are voting. I'd love to get more votes. 

And please, if you don't find that any of the options are in the options-- that any of the initiatives that you're prioritizing are in the options above, let us know in your chat as well. I would love to learn from your experiences. So I'm going to give it just an extra 10 seconds. 

So looking at the different results we see that:

  • 8% digital products

  • 8% digital channels

  • an overwhelming majority on data analytics

  • And 13% on technology investments.

That's really awesome. And you're definitely in the right place and it's really exciting to see how you're prioritizing data analytics as part of your digital transformation journey. And please reach out for us in the chat as well or reach out to me on LinkedIn if there's any questions that you have after the session around your data analytics journey. 

Data Transformation rests on a spectrum

So now that we've discussed this, let's talk about how data transformation really underpins successful digital transformation. So first of all, what we need to discuss is that data transformation rests on a spectrum. There's often a lot of confusion or a lot of misconceptions around data transformation or even the digital transformation that it's like a one stop project and not a journey. It's absolutely a journey, and it's something that you need to keep sustaining over time. 

So the way we think about data transformation and data maturity overall is through the ways by which data is leveraged within your organization. So I highly recommend actually to read this white paper on the organization guide to data maturity and how it's important and how different maturity levels-- how do different maturity levels manifest within the organization.

  • So to start off with the first data maturity level, an organization can be data reactive so that no one accesses or uses data in their daily work. The company rarely reports on data.

  • The second data maturity level is data scaling. Very few people have the skills and access they need to analyze report and present data competently.

  • And third data maturity level is data progressive. Every team has at least one data fluent employee who can analyze report and present their data regardless of their role. 

  • And finally data fluent, where everyone knows how to access the data they need to do their job, and this definitely does not mean that everyone needs to code, far from it. And we're going to cover it at the end of the session as well.

But overall, I highly recommend to download this white paper to check out what the different steps that you need to make from one data maturity stage to another as well. 

So going back to the data transformation of organization and how a lot of successful digital transformation requires you to answer a lot of these data questions, ultimately the point that you need to reach and sustain is where you have a lot of people in your organization where your organization can answer these questions that we discussed here in a decentralized way where everyone has access to the same data, the same ground truth data, and they have the skills to analyze it, to understand it, to criticize it, and to argue with it and reason with it. And that is what happens when you have a data fluent or data literate organization. 

Data Transformation depends on many levers

So what is the framework by which you go through data transformation? What do you need to grow within your organization to become data transformed? Really the data transformation depends on various levers. The first two are infrastructure and people. These are the base levers of your data maturity and your data transformation. And these are the most important levers that you need to grow. 

Then you have supporting levers such as tools, organization, and processes.

  • So infrastructure is all about enabling data access that is clean, actionable, compliant, of high quality, and that is usable by the entirety of the organization.

  • People is all about transforming your talent for the data driven age so that they're able to make insights, derive insights from this data. 

  • Tools. It's about the data tools that you equip your organization and your people with, but it's also about the frameworks, the templates that you create that make working with data super easy.

  • Organization is about how you organize data teams. So whether you should have a centralized data team or you should have an embedded data team, it's all about that. 

  • And then processes is about what are the processes that you have within your data experts, your data scientists, your data engineers, your machine learning experts. But what are also the processes that you have between the business stakeholders and the data scientists within your organization so that the business stakeholders are strong customers of the data science organization. 

What that looks like in practice

So let's talk about what a data fluent organization looks like, and what a data transformed organization look like, and what does it mean in practice across all of these different levers. So the infrastructure side it means that data is trusted, usable, findable, and actionable. It means you have solid data discovery. You have tools that equip people to find the data that they need. You have scalable data governance, which means that you have high quality data governance measures set in place to ensure that the right data, the high quality data, that you have is sustained throughout the data analysis journey of your organization. 

And it means also high impact operationalisation. It means you have the infrastructure set in place to deploy machine learning models, to deploy dashboards at scale and not be stuck within the experimentation phase of gaining value from data science. On the people side, it's about hiring the right talent, so making sure that everyone has the skills and culture to scale data driven decision making. So you definitely need to hire, whether that's data scientists, data engineers, data experts. 

But it also means creating a culture of upskilling and continuous learning so that you can drive data driven decisions at scale, because ultimately, no matter how much you can hire data scientists or data experts, one, you'll never be able to endow them with a subject matter of expertise that your business experts have at scale. Secondly, you'll never be able to find that amount of data scientists, especially since regardless of the industry that you're in every single other industry, every single other players is competing for that same talent. 

Now on the tools side it's about ensuring that modern tooling is accessible with frameworks to lower barriers to entry. So modern tooling such as open source programming languages like Python and R, but also.

And then also frameworks to lower high barriers to entry. So these are templates like even spreadsheet templates that you can use to analyze data, Tableau templates that you can use to analyze data. 

So it's all about that combination of creating inclusive modern tooling for all the type of users within the organization, as well as creating templates that enable them to work with data at scale. Now, on the organization side, the data team means that set in place that you have data science experts within your organization and they're working on highly strategic projects. 

But it also means that data science is democratized for all by making sure that the data team is working on democratizing that data as well. Now on the processing side, it means that there's mature data process for all. So data purposes are inclusive for business stakeholders, data teams, and the data teams efficiency as well is highly scalable and is growing over time. 

So this is what a data transformed organization looks like. And a lot of the organization that we discussed here are the North Star that you want to be in as an organization that's going through this data maturity journey and that's going through this data transformation journey. So a lot of the organizations that we've mentioned here, like Amazon, Uber, Airbnb are really leaders when it comes to scaling all of these levers that they have reached a data transformed state or a data fluent state. 

Airbnb — a data science Northstar

My favorite example comes from Airbnb. They really have been at the forefront of a lot of highly innovative way of thinking and solutions when it comes to not only just scaling their infrastructure and technologies and tools but also thinking about how to scale their people. So for example, when it comes to sales democratization, I left here a couple of resources on how Airbnb democratizes data science with its own data university. So they have a learning and development team and a data science team that's almost dedicated towards creating learning resources, like the ones that you find on DataCamp around on data science and their training. 

They empower their own data science team with data engineering education so that they're able to scale their impact and create more scalable processes that doesn't rely on external resources as well. Now, when it comes to data quality and access Airbnb has an incredibly efficient process called-- a certification process for its data, and they discuss it here in these two particular blog posts here around data quality at the Airbnb. And they also really work hard creating metrics consistency, for example, so that they ensure that anyone who accesses a certain piece of data can derive the same insights regardless of where they are and what tools that they're using. 

And when it comes to tools and processes, for example, an example of a framework would be then creating art packages that allow any user with a few lines of code to create data analysis on specific Airbnb data or create visualizations with three, four lines of code that look exactly like this, that have the same aesthetic of Airbnb and that can be easily plugged into a report or a presentation. That's enabling data storytelling at scale, for example, at Airbnb. 

Transformation requires wide-scale adoption

Now with that in mind, I want to talk about how especially data transformation is people driven and it's not technology driven. So we saw in the previous section that indeed there are five levers towards scaling data transformation. Infrastructure, people are the base levers. They're the most important ones because ultimately, they enable data access and they enable the cultivation of insights from that data. And then tools, organization, and processes are your supporting levers that enable that maturity. 

My argument today is that transforming talent for the data driven age is absolutely the most important lever to scale. And the reason why is because transformation requires wide scale adoption. So regardless the size of the data science team that you have, their impact will be widely limited without wider scale adoption in the organization. 

This means that if your data team develops a dashboard, a machine learning model that needs to be embedded in a business process, all of these data products are being consumed by your business experts. These business experts need to be data literate, they need to have the data culture, and moreover, when thinking about the overall value that derive from data science, I would argue that machine learning and data science heavy type endeavors are not where the majority of the value comes from. The majority of the value comes from everyone in your organization making decentralized educated data informed decisions around business processes. And this requires high levels of data literacy across the board within your organization. 

Let's see this in action, for example. This is a survey that was done by NewVantage Partners that they've done every year on the state of data culture and data transformation. Their specialty is in data transformation. So according to the 2021 survey, 99% of organizations that they interviewed in the Fortune 1,000 are making data science initiatives and artificial intelligence initiatives. They're investing in data science. They're investing in AI tools and AI models within their business processes. 

Yet 29% have experienced any transformational business outcomes with data. 24% only have built a data culture. And most importantly, 92% of everyone interviewed believe that culture and skills are the biggest impediment towards building a data driven organization. 

The importance of building a data culture

And a friend of the show as well, Sudaman Thoppan Mohanchandralal, Regional Chief Data Officer at Allianz Benelux, talks about this and talks about this and his framework around building data culture. So he has also engaged in this activity and exercise of building data culture at Allianz To give you a brief summarization of how he discusses this in the webinar as well as the podcast here, Allianz Benelux started building a lot of data products, started building a lot of data science solutions, invested in data science teams more than four years ago. 

And then they realized a year to two years ago that in order to have adoption and to have the usage of their data science solutions to scale within their organization, what they need to do is to build a data culture because ultimately, as Sudaman says here, "Data culture is not just an option to succeed in data analytics initiatives, it is business-critical" because if your broker is not going to use data or the data solutions developed by data science team, which is here in the example of Allianz, then there is no use of that data solution that is being developed. The best way he describes it it's sort of like getting an Apple Watch that gives you all these health measurement tracking, but you don't do any type of behavior change after that that enables you to become a healthier person. 

Key steps to scale people for the data-driven age

So let's talk about-- I want to be practical here. And I want to talk about the key steps to scale people within your organization for the data-driven age. So I want to talk about five particular steps.

  • The first one is realigning your strategy and your objectives according to that data transformation strategy and that learning strategy that you have. 

  • Secondly, it's about identifying the learning personas within your organization, who do you want to upskill and why.

  • Thirdly, it's about personalizing learning facts, and creating a path-specific for these personas.

  • Four is about assessing skills regularly. And we'll see why.

  • And then five is about creating a learning culture that intersects with your data culture. 

1 — Realign your objectives and strategy

So let's discuss. First of all, it's about realigning your objectives and data strategy. First step within that it's really assess your data strategy as an organization, as a leader, and how you can be able to impact it. So how important within your data strategy is scaling skills and culture? When does culture transformation start to take place within your organization? 

For example, the example that we mentioned earlier at Allianz Benelux is that they started thinking about culture after they started developing solutions and data products for the team and they wanted to increase scalability and adoption of their solutions. So this is often-- there's no rule of thumb about when to start investing in culture and skills in organization wide skills. But ultimately your initiatives of building data science teams, building data infrastructure will be stalled without proper data skills across the board within your organization. 

Second thing that you need to think about here, setting upskilling objectives. So how are you aligning your data upskilling objectives with a business objective? For example, instead of saying, I want to upskill my people on data science technologies like Python and R how are you setting-- try to pivot it and try to think, I want to equip business analysts with the ability to automate their Excel workflows using SQL. And from then on out you can work backwards from that objective to build a solid learning plan for that particular persona, for example. 

Thirdly, it's all about measuring training success with business impact. So for example, the success of training and upskilling should be measured with business metrics like cost of dollars saved or value generated for the organization by seeing how much of an automation of a process happen. There are a variety of ways you can measure the business impact of learning. And it's all about creating a cross-functional team that incorporates learning and development, business leaders, and the teams that they're upskilling on to determine what is the barometer of success for the upskilling strategy. 

2 — Determine your learning personas

Secondly, it's all about determining the learning personas. So in any organization there's a variety of people.

  • We can start here with the data consumer who is a non-technical expert who is-- you can think about that person as a manager or as a leader. A particular role could be here like a chief marketing officer, for example. They just consume data. They don't go in the data. They don't do any hard core data analysis. They just consume data from their team. They're a leader type persona. So this type of person they require a different type of learning path than any other one. 

  • Another example here could be a data analyst. This is someone who really tries to analyze data through a variety of means and tools. 

  • And then you have a data scientist. This is someone who uses the entirety of the data analysis workflow and the machine learning workflow to provide value with data. 

  • And then you have a data engineer which is someone that tries to create the infrastructure to enable other folks within the organization to use data at scale. So for example, their skills here.

3 — Personalize learning paths

The skills of a data consumer could be data literacy, critical thinking, and the ability to ask the right questions in making data driven decisions. 

Now if I'm creating a learning path for that persona, it would be data literacy fundamental skill track on DataCamp. Another data analyst, they would need to data analysis, programming knowledge in SQL, R, or Python or business intelligence like Tableau in R or Power BI. They need to have subject matter expertise about the department that they're working in. They need to have solid data visualization skills, for example. 

On the data scientist side they need to have programming skills. They need to handle large diverse data sets. They need to know about machine learning and automation, exploratory data analysis, and cloud computing. Data engineers they need to also know about cloud computing, but they also need to know advanced programming skills and best practices. They need to be able to create extract, transform, and load workflows and pipelines in order to enable people to have access to clean data. 

Now, this is just a taste of different data personas. We can do an entire webinar on data personas only, and discuss what are the tools that they need to know, what are the skills that they need to know at an advanced level, at an intermediate level, and a beginner level and who within your organization can be a part of these different data personas. But what I want to do is provide the link to this white paper that you can access through the slides, The Data Leader's Guide to Upskilling. 

It really talks about eight different data personas, the tools and the skills that they need to know, whether that's data consumers and leaders, machine learning scientists, business analysts, statisticians, data analysts, programmers, data scientists and their engineers. So what's really important here is that you determine your learning personas then you craft personalized learning resources for them in order to one, take them to a stated objective of an upskilling objective that we discussed in section one. 

Now the second element here—now an example of a personalized learning path is also from Allianz. For example, at Allianz, they use DataCamp to create three different custom learning tracks throughout their people across more than 1,000 learners. That was this program that was launched in April. So it's part of their own data analytics academy. 

They have three different types of personas, a data analytics literacy type of persona, which is really aimed at data consumers, anyone to be able to speak the language of data, and you have a data analyst type track and then you have a data scientist type track. Now any type of learning resource that is important for you, you can integrate that you have already. You can integrate that within your own personalized learning paths in order to scale the people that you have in a way that is aligned with the business objectives, their career aspirations, but also where you want the business to be in a few years. 

4 — Assess skills regularly

Now, another aspect here is assessing skills regularly. So what you want to be able to do here is to demonstrate the return on investment of your skills over time, because what you want is to be able to gain executive support but also you need to be able to determine what are the skill gaps within your organization that are aligned with the business objectives that you stated. So this is why try as much as possible to create formalized assessments that track learner evolution over time so that you can see the effectiveness of your personalized learning programs so that you can see whether there are improvements that need to be made, whether there are skill gaps that need to be applied, whether there are additional learning resources that you need to recommend. 

And all of this is all about creating a data-driven way to be able to assess your skills and to be able to determine the success of your learning program. A lot of different organizations often fail at this because one, there is not a lot of good tooling set in place. But also because especially when it comes to data skills, it's a difficult skill to be able to quantitatively measure consistently over time. 

5 — Create a learning culture

Now, the fifth thing here is all about creating a learning culture. This is the most important-- arguably one of the most important tasks. And it all starts with a few steps. So the first one is collaborative learning. 

It's really important to make learning fun within your organization and to create and incentivize a culture where people are working together and feeling like learning is part of the job and they're able to learn from each other. So an example of this would be creating hackathons where your data experts endow their knowledge upon non-data technical experts, for example, or creating events with external speakers, external organizations, external training organizations to be able to go through a collaborative training session together around data skills, for example. 

The best example for me comes from hackathons as well as leaderboard type learning programs where you're able to one, see how others within your organizations are learning, but also trying to see how you can definitely learn together in order to be able to drive that value and discuss how you can apply what you're learning within your organization. Now, a second thing that you can think about is experimenting with learning modalities. For example, a lot of organizations-- we saw the Airbnb example-- they have a data skills academy. They have a data university where people access videos and are able to work with data science skills and learn about them and how they fit at Airbnb. 

Other organizations, for example, can opt for live trainings or even think about something that is more blended, or think about something that is self-led training for example using a course provider. There's a variety of ways to slice a cake, and what you need to experiment with here is what is the learning modality that fits your company culture and that fits the people within your organization and how they like to learn. Now, thirdly as we discussed, which is something that we mentioned in three, creating personalized learning paths is really important to sustain a learning culture, because ultimately if everyone is learning the same thing not everyone will have the same type of application area that you can apply within your organization to what you're learning. 

So this is why in order to create a learning culture, in order to feel like it's actually useful for you, personalization of learning is super, super important. Finally, and arguably one of the more important aspects of the learning culture component is psychological safety. So celebrating learning as a part of career growth as well as creating time and creating safety for people to be able to block off time on their calendar to learn. That is extremely important because ultimately what is very important to note here is that learning is the tool by which you can grow the business, by which you can grow your organization and galvanize a team that is high performing, especially when you're trying to create a data-driven culture and when you're trying to shift and create change management within your organization. 

Blended Learning at Bloomberg

So an example of blended learning that we discussed here comes from our friends at Bloomberg. So for example, in Bloomberg's learning and development team they host a quarterly data analysis with Python program. There's actually a webinar on it where Sheil Naik, the learning and development lead at Bloomberg who covers this program, talks about this in great detail. It starts off as a one-hour introduction deep learning program on data analysis with Python. Is a quarterly program that is really designed for non-technical users in order to enable people to use Python for their day-to-day work. 

So it starts off with a one hour introduction class explaining how Python is used at Bloomberg. Then it's 12 to 20 hours of learning using DataCamp recorder. Then it's three live in one and half hours classroom sessions led by technical experts with persistent chat. For example, you can see here Sheil Naik on the bottom covering specific data analysis projects using Bloomberg data. And then finally a final project using Bloomberg data. It's kind of like a capstone where people pass it, and it's actually a great way to incorporate both the self-led learning while also doing a more of an interactive collaborative experience as well. 

In summary

So with that in mind, now that we've covered these five steps let's summarize and take it home. So the first step that I want to cover here is that COVID-19 is indeed was the black swan event of 2020 that has accelerated digital transformation. So COVID-m 19 has really telescoped the future, and ushered in an age of digital work and digital first business models. 

So a take away stat here for me is that:

  • COVID-19 accelerated consumer digital adoption by three years according to McKinsey. Despite these gains, though, despite this acceleration, digital transformation is only underpinned by scalable data transformation. So despite a lot of these gains, many digital transformation projects are stalled.

  • Many data transformation projects are stalled simply because there are these five levers that you need to grow with first and foremost, people being at the forefront. So the base levers here are infrastructure and people, and the supporting levers are tools, organization processes. And people are definitely the most important one because without people your infrastructure is useless and tools are not used and your organized teams are not creating value and your processes are not being utilized. 

  • Thirdly, actionable steps to scale your people is realign your objectives and strategy determining the learning personas within your organization, create personalized learning tracks, making sure that you assess skills regularly, finally, focusing on creating a learning culture. With all of these five steps, they can create a foundation towards a scalable way to create a more data-driven workforce, and a baseline of how you can do change management within your organization. 

Adel Nehme Headshot
Adel Nehme

Data Science Evangelist at DataCamp

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