{"id":4950,"date":"2023-06-21T00:00:00","date_gmt":"2023-06-20T23:00:00","guid":{"rendered":"https:\/\/members.dataiq.global\/article\/dataiq-members-briefing-what-does-great-data-literacy-look-like\/"},"modified":"2024-03-21T16:38:57","modified_gmt":"2024-03-21T16:38:57","slug":"dataiq-members-briefing-what-does-great-data-literacy-look-like","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/dataiq-members-briefing-what-does-great-data-literacy-look-like\/","title":{"rendered":"DataIQ Member\u2019s Briefing \u2013 What does great data literacy look like?"},"content":{"rendered":"

Identifying the journey\u00a0<\/strong><\/h4>\n

Each DataIQ member is at a different stage of its data maturity journey and within that journey data literacy capabilities can vary from department to department. The first step, as put forward by one member in the energy sector, is for data leaders to identify the current abilities of each department \u2013 perhaps even individuals \u2013 and whether they require foundational or technical levels of data literacy to achieve their goals.\u00a0 “It is really important to capture the foundational data literacy gaps that you might have in organisations,\u201d said the member. \u201cAs data leaders, we want, at a click of a button, to provide good quality data for all levels \u2013 even where team members may have not had that technical data science background.\u201d<\/p>\n

The member continued that it is important to understand that although an individual outside of the data office may not have the data knowledge required, they will likely have the business knowledge, skills and acumen to ask the questions that data is trying to solve. Education is key and requires time and investment, but the benefits vastly outweigh the initial costs.<\/p>\n

Elsewhere, a member that is involved in transport explained their description of data literacy for their organisation as \u201cdata literacy is a set of knowledge and skills that allows you to understand and to work with and to derive insights from the data is the core for everything that is going to be related to the data culture in an organisation.\u201d They continued that data leaders \u201ccannot talk about what data culture and data mindsets are if they have not had a conversation with the core people affected and those who make the decisions regarding data literacy.\u201d<\/p>\n

One participant described their previous experiences of data literacy at multiple organisations and stated that, \u201cto me, data literacy is a combination of not just the technical skill set, but more importantly, it includes the attitude and the culture.\u201d It has been mentioned at previous roundtables by DataIQ members that data literacy and data culture go hand in hand and the success of one cannot exist without the success of the other. The member continued, \u201cwhen there is a culture for agility or curiosity in the company, data literacy tends to flourish. But when you do not see that, it does not happen and might be very siloed, focusing on a specific technical skill set. This then means that when something is implemented, you will see that the different teams will notice a lot of gaps that are missing.\u201d Data literacy is not one thing to fix all issues and it is not easy to identify what constitutes strong data literacy skills without fully understanding the specific business in question. The data literacy skills required for a retailer vary drastically from a B2B organisation that sells data sets as its core product \u2013 but each operation has different levels of data literacy that are identifiable.<\/p>\n

It needs to be noted however, that \u201cthere is always going to be some discrepancies in terms of maturity\u201d when it comes to data literacy abilities. A one member explained, \u201csome individuals and teams are scientifically more prone to data\u201d and this needs to be accepted by data leaders. There will always be an imbalance with learning speed and enthusiasm for certain areas of data literacy, but they can be addressed with time, education and a strong culture.<\/p>\n

 <\/p>\n

Suitable tools\u00a0<\/strong><\/h4>\n

It was agreed that great data literacy involves, as described by one participant \u201cgiving individuals the tools for whatever level they are at and giving them an environment that says it is okay not to be perfect.\u201d Furthermore, CDOs and data leaders need to support this concept all the way through to giving an advanced toolkit for the experienced people to be able to do what they what they want to do with data to achieve their goals.<\/p>\n

A member involved in the hospitality sector explained that CDOs must remember that many staff without technical or data backgrounds have dozens of daily essential tasks, so the implementation of any tools must not interfere with their day-to-day operations. For a large organisation that has hundreds of brick-and-mortar establishments, each separate frontline team needs to have multiple skills including finance, HR, sales and data, which can be a massive strain on staff. The staff want to know how well their store is doing, and head office wants accurate data to be able to examine everything on a regional and national level \u2013 this requires data tools, but the tools need to cater for a wide variety of data literacy abilities.<\/p>\n

Leaders need to have regular feedback from users about the tools that have been provided, and this in itself can be a pain point. As one participant noted, only three out of eighteen users provided feedback. They noted that frequently only half of the people would attend monthly forums about the tools being implemented, even though when the tools are unveiled, and the plan is explained there is overwhelming enthusiasm and excitement for the prospects. Data leaders do not want to implement a surveillance state approach to monitoring data use and how tools are being used, but without regular feedback this is perhaps the best solution to ensure efficiencies and strong return on investment.<\/p>\n

One of the major tools that needs to be understood by all team members is a reporting dashboard. It was noted by multiple participants that dashboards are, in a sense, the heart of monitoring data literacy. Through dashboards, leadership teams can see where data is coming from, the quality of the data and how it is then being implemented. The goal is to have teams actively requesting data and pulling on the data team, not having data pushed onto them and this has been achieved using dashboards and data literacy training.<\/p>\n

Ultimately, the image of great data literacy varies between businesses, but the data leaders of those organisations can identify it when it appears. There are some benchmarks and steps that can be taken to improve data literacy \u2013 and data culture as a secondary impact \u2013 and these will speed up the journey to achieving great data literacy. There needs to be some groundwork put in place, such as assessing different departments and starting dialogues with all relevant parties, and this takes time, but the results are worth the effort as a team with strong data literacy has higher quality data, higher success rates and can evolve more effectively.<\/p>\n

Sign up for a future roundtable discussion here<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Data literacy definitions vary between organisations, but the results and goals are similar for everyone. DataIQ members debated what great data literacy should look like and why.<\/p>\n","protected":false},"author":19,"featured_media":4964,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,281],"tags":[169,234,228,232,237,119,238,236,235,233],"pillar":[195],"class_list":["post-4950","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-roundtables","tag-analytics-and-insight","tag-culture-and-skills","tag-data-culture","tag-data-for-good","tag-data-leaders","tag-data-literacy","tag-data-skills","tag-investment","tag-return-on-investment","tag-roundtable","pillar-literacy"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-21 04:37:30","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/4950","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/types\/article"}],"author":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/comments?post=4950"}],"version-history":[{"count":0,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/4950\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/4964"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=4950"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=4950"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=4950"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=4950"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}