{"id":23942,"date":"2024-10-08T11:12:45","date_gmt":"2024-10-08T10:12:45","guid":{"rendered":"https:\/\/www.dataiq.global\/?post_type=article&p=23942"},"modified":"2024-10-08T11:12:45","modified_gmt":"2024-10-08T10:12:45","slug":"3-data-culture-dos-and-donts","status":"publish","type":"article","link":"https:\/\/www.dataiq.global\/devstage\/articles\/3-data-culture-dos-and-donts\/","title":{"rendered":"3 data culture dos and don\u2019ts"},"content":{"rendered":"

Data-driven culture starts from the top<\/span><\/b>\u00a0<\/span><\/h4>\n

Strong data-driven culture comes from leaders and decision makers that are proactive in setting expectations that decisions need to be rooted in data, and then leading by example. Particularly if the data team is growing, the data leader must be seen to be emphasising data-led decisions and questioning anything that seems to fly in the face of that.\u00a0<\/span>\u00a0<\/span><\/p>\n

For example, data leaders that have gained a seat at the table should push to spend some time at regular meetings to examine summaries and proposals that demonstrate a data driven approach. This will then instil a culture that demands data evidence to support decisions and business direction.\u00a0<\/span>\u00a0<\/span><\/p>\n

Furthermore, these types of practices will propagate downwards. When team members across the organisation want to communicate with senior leaders and start developing a name for themselves, they will need to put forward their ideas in the language being used by decision makers. If that language is one of a data-centric and data-driven mindset, then junior members of the team and future leaders will adopt the same.\u00a0<\/span>\u00a0<\/span><\/p>\n

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Do not pigeonhole data scientists<\/span><\/b>\u00a0<\/span><\/h4>\n

Data scientists are often given their role and then left in a room, resulting in a lack of knowledge surrounding other departments. Very simply, how can the data team provide the best data-driven approach to different issues if they do not know the context of the issues being raised? Much like siloed data from different teams, a siloed data team will have diminishing returns if pigeonholed.\u00a0<\/span>\u00a0<\/span><\/p>\n

Data leaders in particular need to make a push for data members being positioned into other departments. One of the best ways this can happen is if there is a data-curious member of an existing team that wants to better connect with the data office. This then provides a near-seamless connection between the data capabilities and the department in question without the traditional poaching of talent from different teams.\u00a0<\/span>\u00a0<\/span><\/p>\n

Some DataIQ members have found great success by rotating data members into different line roles which not only improves communication with the data office, but also allows data professionals to better understand the day-to-day of diverse roles. It has also been found to improve performance as data professionals that are interested in different parts of the business get the opportunity to improve their skillsets and, perhaps, create a new permanent data function within that team.\u00a0<\/span>\u00a0<\/span><\/p>\n

Open routes of communication need to be advertised by the data team; whether this is through dedicated channels, internal newsletters, or regular reminders at company-wide meetings. Some DataIQ members have created dedicated portals for raising data concerns and creating queries about issues that data may be able to address.<\/span>\u00a0<\/span><\/p>\n

Finally, another way to improve communication with a data team is to explain why new hires into different teams should have a level of data literacy and understanding. By working with HR and different department heads, data leaders can demonstrate the countless benefits of having a data literate team and instigate a series of interview screening questions or tasks that can help identify data knowledge.<\/span>\u00a0<\/span><\/p>\n

Very simply, department leaders in data-driven organisations and those claiming to be data driven need to be involved in the process to improve data culture. There are numerous ways to achieve this, but each one involves collaboration and communication.<\/span>\u00a0<\/span><\/p>\n

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Be rapid to fix basic data-access issues<\/span><\/b>\u00a0<\/span><\/h4>\n

It is an unfortunate reality, but data-access issues will arise. No matter the company, the technology being used, and the level of maturity achieved, data leaders and their teams will always need to address access problems at one time or another.\u00a0<\/span>\u00a0<\/span><\/p>\n

There is a common complaint from DataIQ members that they can struggle to obtain data \u2013 even basic data sets \u2013 from different teams. This can arise for several reasons, but the simple fact is that data teams and leaders must be rapid in sorting issues.\u00a0<\/span>\u00a0<\/span><\/p>\n

It is true that there is a level of user error that comes with this territory and new technology, even when data teams have done their utmost to democratise data, but the problem must still be addressed. Questions around why data from different departments is not being stored correctly, or available easily need to be raised and fixed as soon as possible.\u00a0<\/span>\u00a0<\/span><\/p>\n

Data leaders should implement training for different departments so that they can understand how and why this data is needed, and therefore improve data culture. Once a strong data culture is in place, the errors and difficulties that do occur tend to be few and far between.\u00a0<\/span>\u00a0<\/span><\/p>\n

To avoid logjams, data leaders can provide gradual universal access piece by piece for specific key measures. The best place to start would be identifying what metrics are on the agenda for stakeholders and business decision makers and then tying in subsequent numbers to this data source which will improve the amount of use.\u00a0<\/span>\u00a0<\/span><\/p>\n

Data culture takes time to take root, and without roots it cannot flourish. But with some of these fundamental steps taken, data culture can become a central part of organisational identity in the coming years.\u00a0<\/span>\u00a0<\/span><\/p>\n

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DataIQ members can assess their <\/span><\/i>data culture maturity<\/span><\/i><\/a> and receive recommendations.\u00a0<\/span><\/i>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Developing data culture can take time and be confusing for non-data professionals, so here are some key things to keep in mind when promoting data culture.\u00a0<\/p>\n","protected":false},"author":19,"featured_media":23943,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_searchwp_excluded":"","footnotes":""},"categories":[129,398],"tags":[1038,467,1037,228,866,1034,1035,1036],"pillar":[],"class_list":["post-23942","article","type-article","status-publish","format-standard","has-post-thumbnail","hentry","category-editorial","category-public","tag-access-issues","tag-culture","tag-data-access","tag-data-culture","tag-data-driven","tag-do","tag-dont","tag-pigeonhole"],"acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-21 02:49:49","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\/23942","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=23942"}],"version-history":[{"count":1,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/23942\/revisions"}],"predecessor-version":[{"id":23944,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/article\/23942\/revisions\/23944"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media\/23943"}],"wp:attachment":[{"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/media?parent=23942"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/categories?post=23942"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/tags?post=23942"},{"taxonomy":"pillar","embeddable":true,"href":"https:\/\/www.dataiq.global\/devstage\/wp-json\/wp\/v2\/pillar?post=23942"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}