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DataIQ Leaders briefing – Best practice for developing soft skills in your data office

At the first DataIQ Leaders roundtable of 2022, attendees discussed the best methods for developing soft skills within the business. The conversation centred on the importance of getting to grips with a business’s unique needs, best methods for tracking skills development, and the use that can be drawn from competency frameworks.
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  1. Structure your approach with a skills framework

The stereotypical view of the data and analytics professional is changing. It is becoming increasingly apparent that the most efficient data teams are comprised of talent with diverse skills, perspectives and backgrounds. “In the past, the thinking was that the data office could get by purely on technical skills,” said one Leader from a large retailer. “But 11 strikers don’t make for a good football team – and similarly the data office needs a broad range of skills and expertise.”

As one Leader from a large building society explained, organisations are increasingly utilising competency frameworks to map the various skills needed in different areas of the modern data office. “Our old skills frameworks centred on macro-level risk mitigation – we’re now putting emphasis on rounded, soft skills, and putting training programmes in place to develop them.” Skills frameworks outline core skills that are recognised as essential to a given role, within which the importance of soft skills can be crystalised. These need to be kept up-to-date as the internal culture of an organisation matures.

Technical skills will inevitably underpin a significant amount of the work undertaken by a data office. Soft skills ensure that technical concepts are debated, shared and built upon. To facilitate this, one Leader has set up an informal community for their organisation’s analysts. “We wanted this to be an organic space in which analysts can get together and share common experiences,” they explained. “We set monthly analytical challenges and award prizes for winning teams, which has really helped us to ensure that soft skills are shared in an informal, unstructured setting.” DataIQ has often seen such informal beginnings mature into more structured communities of practice which can provide a sustainable place for learning, knowledge sharing and networking.

  1. Ensure that your business understands data, and that your data team understands the business

The quality of any insight is directly tied to its relevance. One Leader shared an anecdote to emphasise this point. In a previous role, businesses stakeholders had approached the data team with a problem: a new three-for-two promotion on nappies wasn’t performing as well as hoped. The answer to this problem wasn’t immediately apparent within the data, which prompted a group of analysts to visit a store. It quickly became apparent that customers simply could not fit three packs of nappies into their trollies, causing them to give up on the deal. “We quickly realised that once we had built-up our hard analytical foundations, it was really important for us to build up the bridges between analytics and the business,” said the Leader.  

One useful method for doing so is to place an emphasis on business partnering, wherein members of the data team establish a direct line of contact with specific personnel within various business functions. Doing so will reveal the different cultures present throughout the business and the range of data literacy within them. “Some areas of the business might be keen to engage with data directly, others might be more engaged by visualisations – business partnering helps us to understand what the best approach is for a particular business problem,” said one Leader.

This approach relies on the business itself having a developed understanding of organisation-specific data, the data team and data structures. To this end, one Leader has introduced a “Data-101” class for all new starters. The class outlines the data available within the organisation and how it can be accessed or requested for certain business problems. At the other end of the spectrum, running a C-level masterclass on the art of the possible with data also wins all-important executive support. Research by DataIQ found 68.8% of our community using this approach.

  1. Find a way to track the success of soft skills development initiatives

Data is an intangible asset, and the metrics used to value it are subjective. Some organisations have adopted a top-level approach, wherein data’s role is defined by a measurable set of criteria within the wider business strategy. But as the role, size and cost of the data office grows, so too does the pressure to demonstrate the value of investments into data – including skills development programmes – on a more granular level.

This area remains nascent. One Leader has identified two key metrics as an indicator of success: a reduction in one-off demand for data and an increase in self-service usage of data tools. “By monitoring our own workflow alongside the number of people accessing our dashboards and data sets, we can get a pretty good idea for how well we have developed the knowledge of data within the business,” they said. Research by DataIQ found 66.1% of our community tracking an increase in self-service and use of data tools to this end.

Elsewhere, organisations are measuring the success of their skills development initiatives through HR metrics. “By correlating the amount spent on training with our staff retention rates – particularly those that are expensive to replace, such as analysts – I’ve been able to demonstrate that investment in development can have a positive impact on my organisation’s bottom line,” said one Leader.

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