Are You Hip To Your Data Culture? Operations People Are More Into It Than Marketers

Email team members trapped in your silos should ask yourselves this: Are you truly part of your company’s data culture?  

The answer will determine how well you deploy data for campaigns, judging by The Alation State of Data Culture Report, a study from Alation, conducted by Wakefield Research. 

Assuming you are part of your firm’s marketing unit, you are hardly at the forefront. Only 36% of marketing staff are top proponents for data and analytic usage. 

That changes to 44% at top-tier data culture companies. 

But, overall, 49% of operations people drive data and analytics use, versus 44% of governance, risk and compliance experts. 

Only 12% of companies say all departments can conduct data search & discovery, and 15% claim data literacy across units. In addition, only 13% claim universal skills in data governance. 

Yet most mid-tier and low-tier firms think they are doing well at it. 



For example, 83% of mid-tier outfits have an inflated sense of their data culture, as do 91% of low-tier companies. Only the top tier firms justly claim a high grade in this area. 

What does having a data culture entail? It includes:

  • Managing data governance at the point of data use — 49%
  • Collaboration between business and data & analytics — 46%
  • Creating metrics and KPIs around data curation — 42%
  • Embedding data scientists in departments — 42%
  • Training/re-training on using data — 41% 
  • Raising awareness about the value of using data — 38%
  • Designating or assigning data stewards within business units — 38% 
  • Emphasis on data skills in new hires — 37%
  • Establishing an internal data community — 36%

But there are serious challenges, as one knows who looks over their cubicle wall:

  • Lack of analytical skills among employees — 45%
  • Data democratization: not everyone can access data on their own — 41%
  • Managing compliance — 37%
  • Lack of a data-driven culture — 36%
  • Data discovery: do not know what data exists or who has what data — 36%
  • Data quality: bad, inaccurate, or redundant data — 34%
  • Organizational silos: data is not shared among different groups — 34%
  • Lack of buy-in from company leadership — 29%

When it comes to AI, here are the issues that lead to failure:

  • Inconsistent standards across data collection — 50%
  • Compliance/privacy issues — 48% 
  • Lack of democratization of or access to data — 44%
  • Inadequate data infrastructure — 44%
  • Bad data quality — 39%
  • Lack of available data — 37%
  • Incomplete data — 27%

Aside from that, the biggest hurdles to using AI effectively are getting buy-in from executives who control funding for AI (55%) and having employees without skills to create AI models (45%). 

Sound familiar?

Meanwhile, there’s no question the top-tier are ahead of the game—38% were likely to exceed their revenue goals, compared with 28% overall. However, there’s a seeming when it comes to simply hitting the goals—62% of the top-tier companies have done so in the last 12 months, and 61% of all firms. 

Wakefield Research surveyed data and analytics leaders at firms with 2,500+ employees in the U.S., UK, Germany, Denmark, Sweden and Norway. 

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