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Improving Data Literacy, the BaseCap Way


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A key barrier to digital transformation is a lack of digital skills, as 44% of business leaders surveyed by Accenture noted. Only 49% of the same respondents have a strategy in place for the management and development of their workforce in a digital world. Alarmingly, 93% of respondents felt their workforce lacks the data skills to achieve optimal productivity, as reported by CDOTrends from a survey of business leaders.  

To address this “skills gap”, let’s understand the foundational skill: data literacy. Poor data literacy causes ineffective communication and is a primary roadblock to progress with data and analytics, according to Gartner research

In this article we will answer the following questions about data literacy: 

A) What is data literacy – and what does proficiency look like? 

B) How do we empower our clients to succeed in data literacy? 

C) How do we develop and improve our staff’s data proficiency?  

 

A) What is data literacy – and what does proficiency look like? 

Buck Woody, Applied Data Scientist employed by Microsoft describes proficiency in data literacy as the ability to do the following: 

1. Find authoritative data  

a. Identify source 

i. Understand types of data (e.g. Quantitative vs. Qualitative data) 

ii. Research and document the different data sources 

iii. Recognize discrepancies between sources 

iv. Consult with appropriate experts in determining sound data sources 

b. Verify data 

i. Validate the data quality of sources 

ii. Identify data quality issues and conduct root cause analysis 

iii. Designing an effective pathway to remediate bad data is also an important step 
 

2. Work with data using various tools  

a. Have a working understanding of basic statistical concepts 

b. Have a working skill with tools to query, alter, and display the results of data 

i. Widely utilized programing languages include: SQL, R, Python 

c. Gain familiarity with newer reporting tools, now becoming the standard industry practice (e.g. Power BI, Tableau, Domo) 
 

3. Analyze data in context  

a. Perform steps to turn raw data into useful insights: 

i. Gather and verify data  

ii. Homogenize or standardize the data to enable effective comparison and groupings 

iii. Group into comparable data sets 

iv. Analyze the target data – by using tools discussed above 

v. Extract information from the analysis 

vi. Communicate results among key players for interpretation (To support the results, the methodology and tools involved should be documented to withstand scrutiny) 

b. Develop big picture correlations and trends from the insights 

c. Be critical about results of analysis 

i. Be aware of cognitive bias 

ii. Be aware of logical fallacies  
 

4. Use data for intelligent decisions 

a. Understand the problem – ask the right questions to grasp the underlying issues/challenges 

b. Apply the proper data and resulting analysis to the problem 

c. Consider alternatives, constraints, priorities 

d. Explain the solution to stakeholders and maintain documentation for clear communication 

e. Learn from mistakes – there will be mistakes with the approach or data. The key is to keep an open mind to revising, as necessary. 

Benefits: 

  • Empowerment to support Data Democratization – Data literacy empowers a larger portion of your labor force to be effective data owners, decentralizing some of the responsibilities traditionally assigned to the IT department. The effective democratization of data reduces the risk of a bottleneck caused by too many data requests going through a single owner.  

  • Agile Responsiveness – When domain experts are not only relying on IT for data requests, they have a more direct path to insights and therefore can respond and act quicker in this age of big data. 

  • Better Communication – Being able to “speak data” improves communication within teams as well as between IT and other teams. Without pervasive data literacy, teams will require lengthy and numerous conference calls to ensure technical requirements are aligned with business requirements. 

  • Data-Driven Culture – Data literacy is a foundation for other technical skills but is also a driving factor for a strong data-driven culture.  

 

B) How do we empower our clients to succeed in data literacy? 

BaseCap Analytics has worked with many organizations that have a hierarchical IT structure. Many are in the process of modernizing their data systems for both immediate needs such as regulatory requirements, and for their future success and scalability.  

While our primary focus is on delivering solutions that help clients resolve their data issues, we understand that a successful implementation is reliant on our client’s long-term level of data literacy, technical knowledge, and processes to resolve data issues internally. 

For example, a large national bank needed to update its data requirements in order to comply with new federal regulations. BaseCap helped this client defining and obtaining new data requirements. Additionally, our experts ensured that the key stakeholders were aligned and understood the data lineage and operating model to produce the required regulatory reports. The result was not only a technical solution, but also a common understanding of the data processes to continue collaborating on the bank’s ongoing regulatory reporting requirements.  

 

C) How do we develop and improve our staff’s data proficiency? 

Being a data analytics company, we push for high data literacy across all functions. Correspondingly, our business analysts are proficient in data, and our engineers are in tune with our clients’ practical business needs. With the necessary skills widespread throughout our organization, we developed a program that continues to improve data literacy as well as other proficiencies within our team. 

We hold Lunch & Learn monthly virtual sessions, where team members educate the rest of the company on a specific topic that they have expertise in. By bringing the members from different areas together on these sessions, team members learn to communicate and operate more effectively.  

One client commented, “BaseCap completed in 2 weeks work that our team would have taken 6 months to deliver.” At the end of the day, data-literacy is not just about having effective hard technical skills, it is also about effective communication and collaboration. In the age of big data and democratized data models, the common ability to “speak data” will be critical for effective data governance.  

 

About BaseCap 

BaseCap Analytics helps its clients ensure the quality of the data their businesses rely on. Our Data Quality Manager automates and streamlines data quality controls, providing a foundation for data initiatives.   

Contact us to see how we can help with your data issues and get a demo of the Data Quality Manager. 

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