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MORE THAN A FIFTH ( 23 %) SAID THEY FELT SO OVERWHELMED WHEN CONFRONTED WITH DATA THAT THEY AVOIDED DOING THE TASK ALTOGETHER .
workers ’ capabilities and actual employee experience . Indeed , 46 % of public sector respondents believe that all or most of their employees have the tools they need to work productively with data , compared with just half of middle managers and below .
To gain an accurate view of data skills across the enterprise , co-ordinate focus groups with different teams to understand how they are currently using data and what further access and tools they need .
An understanding of skill levels can be ascertained by using a data literacy assessment , which measures the ability to read , understand and work with data , rather than technical expertise . There are also free individual assessment tools available online , while bespoke , enterprise assessments can be provided by data literacy consulting services .
With this holistic view of employees ’ use of data , the data champion will identify and dictate the investments that will empower employees to deliver against the organization ’ s goals and for enterprise to action .
3 . Arm employees with the right tools to a HR manager ), but they must be easily integrated into existing working practices to encourage adoption .
Therefore , to support the uptake of dataorientated working practices , tools that are put forward for business users must meet the following criteria :
• Relevant : Does this tool provide appropriate and useful insights that can inform the decision-making process for that role ?
• Consumable : Is the information presented in a way that can be easily digested ?
• Embedded : Can this tool be embedded into existing software , such as HR or CRM , so that it ’ s easy to integrate data insights into the user ’ s working practices ?
4 . Educate to improve data literacy
The true benefits of data are realized when everyone – no matter their level or function – is comfortable and confident using data to inform the decisions they make every day . Yet , we are facing a significant data literacy skills deficit .
Overall , 35 % of public sector employees believe that data literacy training would help them be more productive . This can take many forms , with some integrating data literacy training into existing skills initiatives , while others provide standalone e-learning courses or specialized classroom training for staff .
However , it should always be a continuous learning program to ensure that skillsets are continually reinforced and developed .
The data champion must therefore work closely with HR teams to evolve the program every year in line with the organization ’ s changing use of data and employee needs .
5 . A constant state of evolution
To extract the greatest value from data , public sector organizations must constantly be exploring new opportunities to better inform processes and decision-making .
In collaboration with the data champion , leaders have two critical responsibilities here : firstly , they must ensure that this process becomes ingrained in the organization ’ s DNA so that data is always used to effectively improve its competitive advantage . Secondly , they must ensure that employee skill levels , access to data and tools are continuously reassessed to ensure that they can deliver on these new opportunities .
People at the heart of successful data strategy
We are living in a time when we all understand that having access to reliable , accurate and up-to-date data is critical to decision-making . Yet , too few leaders actually lead from the front and recognize that success is dependent on their ability to empower employees to comfortably and confidently use data . Following these five steps , public sector bodies can put people at the heart of their data strategy to unlock the potential for data-informed decisionmaking and deliver the services and experiences that their citizens demand , as well as drive more efficient services during these unprecedented times . •
It is essential to work with the CTO and CIO to ensure that the data tools serve the needs of each user . Not only will these tools look dramatically different across skill levels ( for example , a finance analyst compared
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