Data Literacy and Corporate Training
Meet evolving business needs and give workers skills required to rise to new heights. 79% of executives report they are concerned about the lack of availability of resources with key skill sets.
A critical piece of the data transformation objective includes data literacy. Research underscores the need for improvement in this sector with 56% of HR professionals describing their organizations’ skills gap as moderate to severe. When success comes down to how people use information, it’s important to have an organization where employees understand how to embrace and use data to drive their decisions, processes, models, and workflows. Data is the “oil” in the big data economy. Yet, a lack of data literacy can lead to five days of lost productivity, per employee, per year – equating to billions of dollars in lost productivity. Not to mention, hiring and retaining an on-site data scientist can be cost-prohibitive. Our service offers data literacy training to business units and provides business insight communication training to technologists. We provide hands-on training by working side-by-side with your teams to upskill your employees with data-related competencies. As a result, you can transform your business and your culture while bringing data literacy to everyone.
What is Data Literacy?
A recent Deloitte survey states that a staggering 67% of executives are not comfortable accessing or using data. A treasure-trove of information and potential insights are waiting in your company’s cache of documents, social media reports, website analytics, financial records, and other repositories. Data literacy is learning how to work with and analyze data. This can be as simple as learning the right questions to ask, building knowledge, and communicating effectively with other team members.
Why Should We Invest in Data Literacy for Our Team?
Data literacy is no longer a luxury in a world of endless data – it is an absolute necessity. Current research shows that large enterprises with strong corporate data literacy have seen up to 5% higher enterprise value (a figure of $320 to $534 million). Transforming into a data-driven organization will create more data literate employees who are able to work more effectively to create a competitive edge.
Upskilling
Our skilled instructors will work with your team to learn how to read, analyze, and communicate with data. We also train groups in a variety of different technology solutions. Call or email today to receive a quote for a custom package. We offer remote training for groups of all sizes and on-site training for groups of 20 or more. Our hands-on courses vary from one-time workshops to a multi-course curriculum depending on your goals. We are also available to create training manuals upon request.
How We Work with You
- Customized workshops: Determine which training program makes sense for your team and organization. We’ll work with you to set goals to help them through data literacy.
- CIO and Line of Business Leader Advisory on Data Strategy, Data Governance, and Monetization Strategy: Our senior strategists work with leaders within the organization to help them think in a more data-driven way and leverage their data assets to drive value.
- Upskilling: Let us co-work on engagements with your resources as part of our team. Our team will help you work on engagement projects to a direct skills transfer. The goal is to empower your team and organization to learn how to do data analysis independently.
How We Help
Sales and Marketing
Optimize your sales by retraining employees on the latest customer management solutions.
Operations
Learn how to integrate your customer management systems, inventory records, financial records, and other data to perform at your peak.
Cost Transformation
Retrain your staff to be able to track the average amount of spend, and reduce indirect costs. A technologically savvy team can use data to form the right questions around where resources can be better spent. 1
Procurement
Gain the skills you need toe Engage in ongoing process improvements driven by data insights.
Customer Experience
Learn how to critically assess graphical representations of consumer preferences and purchasing habits to keep customers engaged and happy.
Strategy
Learn how to identify the right opportunities for making better decision and utilize data for improving strategic value.
1 Source: https://marketingplatform.google.com/about/resources/bcg-delivering-meaningful-moments/
Assessing Your Individual Level of Data Literacy
ASSESSMENT INSTRUCTIONS
Carefully read the 25 statements and answer and select the most appropriate response.
Each question has the following response options:
0 = Strongly disagree (don’t understand the question!), 1 = Disagree/does not apply, 2 = Neutral/applies somewhat, 3 = Agree/app lies mostly and 4 = Strongly agree/applies completely
Data Literacy Resources
Services, Software and Solution Providers:
Data Products – Data science consultancy providing data literacy services.
TecHive – Member organization teaching how to connect Data Literacy to personalized Data Narratives for leadership advancement.
SAS – Literacy Essentials
Data Yap – Data-centric ecosystem for innovative ideas, unwavering community spirit, great minds & partners, to network and elevate data in all dimensions into tangible business value.
Ambient Intelligence —talent identification machine learning algorithm.
Collibra University – training, learning paths and certification for data governance and intelligence.
Z. Gemignani and C. Gemignani, “Data Fluency: Empowering Your Organization With Effective Data Communication,” Juice Analytics
Gartner Consulting – advisory & solution services, design & delivery data literacy programmes
“Data Literacy for Everyone,” Qlik.
“Project-Based Learning, Data Literacy and Online Resources,” and “Why you should care about data literacy” SAS Institute
“Data Learning Solutions for Your Workforce,” Tuva
The Data Lodge – data literacy advisory services, bootcamps and community.
Data To the People – Databilities competency framework, assessments, benchmarks and capability development.
University and Academic Offerings, Case Studies (many early developments in data and information literacy are emerging from the education sector):
“Carnegie Math Pathways,” The Carnegie Foundation for the Advancement of Teaching.
“Strategies and Best Practices for Data Literacy Education,” Dalhousie University.
“Data Literacy and Data Visualization,” Ohio State University.
“Developing Data Literate Students” University of Georgia.
“Developing Data Literacy Programs: Working with Faculty, Graduate Students and Undergraduates,” Bulletin of the Association for Information Science and Technology
Data Business Diploma and MSc – University College Cork / Irish Management Institute
“Data Literacy: A User’s Guide” D. Herzog,
Additional Resources:
DataLiteracy.com – online resources, self-paced online training and onsite training
The Data Literacy Project – A global community dedicated to creating a data-literate world. Supported by organizations including Accenture, The Chartered Institute of Marketing, Cognizant, Data To the People, Experian, Qlik, Pluralsight
“The Ultimate Data Literacy Cheat Sheet,” ChartMogul.
DataKind — brings high-impact organizations together with data scientists to use data science in the service of humanity
Data-Pop Alliance — a global data coalition created by the Harvard Humanitarian Initiative, MIT Media Lab and Overseas Development Institute that brings together researchers, experts, practitioners and activists to promote a people-centered big data revolution
Data Value Map – a discursive template for building shared understanding around data initiatives.
Data Science Central — an open community of and for data scientists
KDnuggets — site and resources for AI, Analytics, Big Data, Data Mining, Data Science, and Machine Learning
Glossary
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Data discovery: Automatically finding, visualizing and narrating important findings within datasets (such as correlations, exceptions, clusters, links and predictions) that are relevant to users without requiring them to build models or write algorithms. |
| Data hub: Ingesting, integrating and provisioning data between and among a range of producing and consuming applications, data stores. A balance of collecting and connecting to data. |
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Data lake: Storing data in its native format for exploration, offering an unrefined view of data for highly skilled data scientists and analysts. |
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Data literacy is formally called out as a new core competency as part of a clear commitment to the organization and leadership valuing “information as a strategic asset.” Training programs (online and/or in-person; internal and/or external) are available and supported across all required levels of proficiency. |
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Data mart: A subset of a data warehouse oriented to a specific business function, group or purpose. |
| Data quality: A discipline ensuring that data is “fit for use” in business processes; includes cleansing, matching, profiling and enrichment. |
| Data storytelling: A combination of data visualization, narrative (the plotline) and context (the surrounding situation/scenario). |
| Data visualization: Use of dashboards (e.g., dials, gauges, charts and maps), infographics, flow charts, decision trees, slide show/series. |
| Machine learning: ML algorithms are composed of many technologies (such as deep learning, neural networks and NLP), used in unsupervised and supervised learning, that operate guided by lessons from existing information inputs. |
| Mean: The average (like the average grades of a set of students’ scores in a classroom). |
| Median: The middle number of a set of numbers arranged in order. If no middle, add the two central numbers and divide by two. |
| Mode: The number that occurs most often in a set of numbers. |
| Natural language processing: NLP is a way for computers to analyze, understand and derive meaning from human language in a smart and useful way. NLP is a subset of artificial intelligence (AI). |
| Predictive analytics: Addresses the question of “what is likely to happen?” Relying on techniques such as predictive modeling, regression analysis, forecasting, multivariate statistics and pattern matching. |
| Prescriptive analytics: Addresses the question of “what should be done?“ Relying on techniques such as graph analysis, simulation, complex-event processing, recommendation engines, heuristics, neural networks and machine learning. |
| Standard deviation: A measure of how spread out the numbers are from the center of a set of numbers. |