How do you assess and develop the data literacy and skills of your workforce and customers?

To assess and develop data literacy and skills, start by identifying the organization's data goals and specific needs. Evaluate the current data maturity, quality, and gaps among the workforce and customers. Develop a comprehensive roadmap that outlines the necessary skills and knowledge areas. Implement data literacy initiatives, including training programs, workshops, and hands-on projects. Incorporate cloud-native technologies and AI/ML services to maximize the value extracted from data. Continuously evaluate the effectiveness of these initiatives, using metrics and feedback. Leverage the latest tools and methodologies within the available budget. This approach helps both the workforce and customers to extract best value from data.

As part of your Data maturity Assessment, Every Organization falls under one among category based on the data culture adoption both technically and all aspects of data management. Usually, the Data Needs are well adopted when it is Top-down so that the Strrategy is well aligned with the business objectives. Success depends on Business users driving the data operating model and actively using the data catalogs where they feel empowered to derive their own data products, Self BI capability. Implementing data governance practices within the Org is vital. This includes defining data ownership, data stewardship, and access controls. It helps maintain data integrity and security.

You need to first understand who it is you are trying to improve literacy in, and what those needs are. They will be different for different roles so you need to take this into account when designing your literacy programme.

Developing an effective data literacy programme can be resource intensive – it’ll either eat up a chunk of your budget or a chunk of your team’s time, or both. Having a clear understanding of what you’re trying to achieve, why it’s important, and who’s involved in invaluable – not only will you create efficiencies in designing the programme, but communication with the rest of the workforce will be consistent and compelling. You don’t need to try and turn everyone into a world-leading Data Scientist, and you’ll probably scare them off if you try. Be pragmatic – what do you NEED to change in order to deliver your data strategy.

A alfabetiza??o de dados é um componente da estratégia de dados que deve estar conectada à estratégia geral da empresa para gerar mais valor e engajamento das lideran?as. Além de avaliar as capacidades técnicas que a empresa possui (ex: seus dados, sistemas e seu uso/qualidade), é essencial avaliar as habilidades de seus colaboradores e as diferen?as de nível entre os departamentos para direcionar seus esfor?os e foco. O MIT CISR possui uma planilha e um framework ótimos para mensurar de maneira geral a maturidade de dados da empresa e suas capacidades e existe também uma “Data Literacy Matrix” para avaliar mais especificamente esse tópico. Caso alguém deseje, posso encaminhar os links dessas duas ferramentas.

We have tried a heavy approach to this with an external partner and the results felt very much like it was a competition amongst different countries. Most recently we have opted for a lightweight self assessment on the proviso that it will shape some major parts of our global data strategy. So essentially it pays for each country to do a critical self assessment. So far the results are really insightful and we feel that the scores are an accurate reflection of reality. The execution of our data strategy requires global collaboration so it's a great start to what I hope is an exciting journey.

You can either have a go at assessing where you are yourself or use third parties. Be aware, third parties’ maturity assessments tend to carry a bit more weight with the wider organisation. Also think about it politically – do you want to be marching up to senior colleagues and telling them they’re not good enough? Regardless, once you know where you are and where you want to get to, you’re in a much better position to create the right pathways.

We have a whole process we use to assess data maturity for clients that is nuanced and detailed. Fundamentally data (analytic) maturity is a function of what tools are used, what data is available, and how much information is derived from it. In discussions with business users and IT/data teams we listen for their ability to communicate analytic concepts, and how they describe their interactions with tools and data. Gaps are found most commonly in understanding the information that could be derived from data and the techniques to pull it out. Second-most frequent gaps are in data access, and third-most are data quality gaps.

Similar to a blood test for assessing human health and underlying medical issues it is extremely important to assess data maturity across an organization. There are tools to use such as the Data Maturity Model and Data Culture Assessment which provide a framework for measuring. Knowing the level of awareness, adoption and integration in your processes, systems and culture will allow you to then pinpoint strengths and weaknesses that will impact data governance, literacy and innovation.

One very easy exercise is to ask people from different departments to show in real-time you how they interact with data on their daily-basis. You will see lots of processes and challenges you never imagined before. Once this is done, you will have a perspective on what's a general issue and what's a particular issue. All of these should be addressed, but prioritizing is always necessary.

Drawing from my experience, I've crafted a roadmap with a people-centric approach, providing a clear perspective on "where we are" and "where we aspire to be." The skill matrix for data literacy and role-specific skills is particularly beneficial. It is composed of three parts: 1. The essential data and metrics you need to be familiar with and proficient in using. 2. The necessary tools and their key features that are crucial for each individual. 3. Your contribution to the generation of high-quality data. Tailored to the unique role, this framework is grounded in the segment of the data chain each individual engages with, the business processes, and the daily decisions made by the individual.

First of all data literacy is not a one and done initiative. This would be a continuous improvement initiative aligned with the enterprise data strategy and data governance. Also, you need to measure the impact of data literacy on an ongoing basis and socialize the benefits as well. This is the key to the success of a successful roadmap which you have to refine regularly. I have done this successfully for enterprises. Patience is a virtue here. Hope this helps. Feedback appreciated.

Data fundamentals & domain knowledge are the quintessential for data literacy. I would emphasize on self-learning, in-person working workshops & trainings. You have enough online materials to augment your data journey learning. Visual analytics knowhow would be one of the best intuitive choices, other being understanding sourcing to serving - data attributes, movement & utilization. Skills need to be simple & easy for professionals irrespective of role & positions ...

Bring some empathy and relevance to the table when you’re designing a data literacy programme. Why should participants care? Are they able to put their new skills into practice during the working day? What are their preferred learning styles? The most effective data literacy-related programme I designed was during our rollout of a self-service analytics tool. Rather than point people to general technical documentation, I built a series of onboarding videos grounded in our own data. Developing materials in a relevant context means concepts are far easier to interpret. Don’t show off – this isn’t about defending your credibility as a skilled practitioner, it’s about enhancing your credibility as a skilled transformational leader.

Like most skills, data literacy is grown through guided experience. The fastest way to fail in a data literacy program is delivering it in a classroom. The roadmap consists of creating an environment of supportive group reviews of analytic work. It's largely setting up a cadence for formal support, and building the camaraderie within the analyst-corps. The ultimate goal of the literacy roadmap is to make analytics cool and fun so that they seek out peer assistance and do independent study and research on better ways to communicate information with data.

Helping team and clients become savvy with data is like embarking on a journey, think of it as sailing across a sea with tons of guides to navigate. Imagine this journey as a spectrum – it starts with a simple commitment. At this stage, we all agree that data is crucial, setting the tone for a shift in how we use it, thanks to visionary leaders leading the way. The next steps involve building trust in data tools and capabilities (like governance and visualization) and understanding the reasons behind data. Connecting the dots between different data bits comes next, unlocking its true potential. Then, there's the communication phase, where we not only understand but share these data insights.

Many previous data skills enhancement endeavors have faced challenges, primarily because the organization's employees either couldn't relate to them or were overwhelmed by the abundance of initiatives, leading to confusion and ultimately abandonment of the idea. The key to success lies in tailoring community initiatives to address specific aspects, given the vast and diverse data landscape. For instance, having distinct initiatives addressing 'Data Strategy and Governance,' 'Data Science,' and 'Data Privacy' ensures that employees comprehend the diversity of the data ecosystem. This approach allows individuals within the organization to choose their focus area and engage more effectively.

When delivering data literacy content, it's critical to understand your audience. Speaking in business terms, tying data concepts to tangible real-world examples, and avoiding information overload can all help data literacy content land effectively.

Comms, comms, comms – positive, consistent and business-oriented. You have to be very clear that this is happening for their benefit, not just for yours. Yes, it will help the D&A office (visibility, impact, etc.), but colleagues will be more motivated if they’re inspired and motivated by what the programme can do for them. Find your champions and leverage their progress, enthusiasm and connections across the organisation. Make it fun. Make it valuable. Make it visible.

We believe in a step-by-step approach: start with theory but jump into practice quite early on. Then theory again (go deeper), but test it out in a practical workshop. Always try to put local context to theory, because without it, the trainees won't be able to see how to apply it in their everyday work (e.g.: who should they contact if they don't have access to something?). After completing a training like this, a skill enhancement sprint (2 - 8 weeks) is recommended to strengthen the skills gained in the trainings.

To effectively evaluate and improve data literacy and skills outcomes, organizations must adopt a continuous feedback loop that integrates both qualitative and quantitative metrics. This approach not only identifies gaps but also fosters a culture of learning and adaptability, crucial in the rapidly evolving landscape of emerging technologies. Celebrating achievements and sharing best practices can enhance engagement and motivation, ultimately leading to a more data-savvy workforce capable of leveraging insights for strategic decision-making. In the media sector, where data-driven narratives are paramount, such initiatives can significantly enhance storytelling and audience engagement.

You should be ahead of the game by designing your evaluation methods alongside your preparation. Open up frictionless feedback loops to continuously improve content and delivery. Some ideas you might consider when evaluating success of your programme: How long does it take to refine a request for analysis (improved comms due to better common understanding)? Is colleagues’ engagement with data-driven products sustained? Is data quality measurably improving (colleagues can often be data generators)? Or more anecdotally, are people saying “hello” to you a bit more? Are they bringing data and analysis into more, and more important, conversations? And once you can demonstrate success, maintain that momentum through more comms.

One effective way to evaluate improvement of data literacy is to monitor the extent to which data is incorporated into the decision-making processes. Assess whether decisions are backed by data analysis and if employees feel confident in using data to inform their choices.

Before you can evaluate, you need a baseline to compare to. So at the outset of the program, you need to capture some of the analytic outputs so they can be compared later. Every quarter it's useful to either individually, or as a group, review the work from before compared to what is current. This will give everyone a sense of accomplishment and encourage more growth. Too often will feel like getting frequent quizzes in school, but too long between reviews doesn't keep the energy up. You have to respond to the needs of the team and hit the cadence they want to keep. It's their growth curve, so they need to own it. It always grows faster when it's organic and supported rather than mandated.

To effectively evaluate and improve data literacy and skills outcomes, organizations must adopt a continuous feedback loop that integrates both qualitative and quantitative metrics. This approach not only identifies gaps in knowledge but also aligns training initiatives with evolving business objectives, particularly in the fast-paced media and technology sectors. Celebrating achievements fosters a culture of learning and innovation, essential for navigating the complexities of emerging technologies like AI and blockchain. By sharing best practices, organizations can build a more informed workforce capable of leveraging data for strategic decision-making, ultimately enhancing their competitive edge in a rapidly changing landscape.

Growing Data Literacy is a people-centric effort. Everyone is motivated differently, so it may require a very deft touch with each individual getting a slightly different treatment to get the most out of it. No matter what the big plan is, you have to start small, and start immediately. Every day that you do nothing as a leader to support data literacy is another day without growth and this hurts the individuals as much as the organization, perhaps more.

Building data literacy starts with understanding the significance of critical data in the business context. It's crucial to educate employees on how data seamlessly moves from one system to another as an integral part of various business processes. This flow of data underscores the necessity of effectively governing this critical information at every stage where it is accessed, processed, or shared. Organizations should make data literacy a mandatory component of their annual training curriculum. Similar to other training initiatives like code of business conduct. By regularly reinforcing these principles, businesses empower their workforce to make more informed, data-driven decisions and contribute to the overall success of the company.

Getting clarity on the business reasons is essential, of course. But often, business goals are very abstract and difficult to connect to individuals' daily work. As a result, even the most comprehensive data literacy programs can fall short of expectations. Organizations can avoid this pitfall by establishing simple success metrics and pivoting business and individual performance conversations from reporting on activities to rewarding measurable results. With this pivot, we have seen a significant shift in how data literacy programs are perceived - flipping from academic and required to essential and desired.

Assessing and developing data literacy and skills within the workforce and customer base is crucial for effectively leveraging data. In my experience, strategies such as comprehensive assessments, tailored training programs, curated resources, empowered data champions, and continuous support are employed to accomplish this. These strategies help identify skill gaps, deliver targeted training, provide accessible resources, foster mentorship, and offer ongoing assistance. By implementing these actions, employees and customers can acquire the necessary data literacy skills to effectively leverage data for decision-making and problem-solving.

Teams are a mixture of experience, knowledge, needs, literacy, and so on. While data management is essential in every business, each team member might have different levels of data literacy and different needs. So a unique approach can be useful but only to an extent. For this reason, I recommend asking, “What do you need data to do for you?” instead of just telling everyone what the new tool or process will be.