In this blog Paul Clarke, Director at FiguringOutData.Com, explains the importance of a Learning Needs Analysis for businesses looking to identify how learning can have the greatest impact on growth. He also describes an approach that has delivered significant benefit for the businesses within which it was applied.
Following the publication of the UK’s Data Strategy there is a huge push by the government for greater use of data across the economy. Businesses can now access heavily subsidised education and training resources plus a myriad of equally well funded start-up and growth support programmes.
But there is a big assumption being made – that better data skills and education about data will translate into business growth.
It will, but only if traditional methods of learning are turned on their head.
Learning provision has to start with a Learning Needs Analysis (LNA) that establishes the relationship between learning and business growth for each employer – which then translates into a learning agenda that works for the Employer.
This means time away from the workplace kept to a minimum, knowledge and skills that are immediately relevant, and a Learner able to relate what they have learned to the needs of the business.
This is a big ask for any Educator, who might understandably prefer to lay out their stall of existing programmes and courses, and ask businesses to simply take their pick.
At FiguringOutData.com we design and perform a Learning Needs Analysis for every organisation for whom we deliver any form of professional development, training, skills transfer, or wider education programme.
Within this article we outline the bones of our approach in case others may find it of use.
There are three parts to the analysis, each is completed by the Employer but facilitated by ourselves. Parts 1 & 2 are independent of each other but are brought together in order to complete Part 3.
Part 1 – learning needs for business growth
Using interviews and surveys we pose a series of open questions that funnel a broad set of requirements for growth into a set of specific learning requirements focused upon tasks and activities that will do most to drive growth. A typical structure is as follows:
- Within your business, what is the growth you want to achieve?
- What do you want to do better, do more often, do less of, that will help the business to grow?
- What difference would those changes make to the business?
- For the big changes, which roles and tasks would be involved?
- What’s stopping the business from making those changes?
- How would data help you to make the big changes?
- How would data then help your business to grow?
- Which people would be using the data?
- What tasks would they be performing?
- What do they need to be better at doing?
Part 2 – learning needs from best practice
We ask Managers in the business to complete a survey to identify how they are currently using data. Using their responses, we work with them to identify what practices they would like to see develop.
The survey is based upon the following themes:
- Leadership & management teams – how excited are they about the possible uses for data, and how willing are they to apply data to the building and execution of strategy?
- Boards of Directors or Trustees – how willing are they to support investment in and experimentation with technology and data?
- Strategic priorities – how well are they reflected in the questions that data will serve, and in the willingness to collect data in order to find the answers?
- Curiosity – how much interest is there across the business in the wider questions that data can answer, and in finding opportunities to experiment?
- Data collection – how much effort is applied to collecting additional data whenever a call for it takes place?
- Support and encouragement – how much encouragement is there for people to use data to communicate about issues and opportunities within the business?
- Recruitment and learning – how much support is there for on-going learning and professional development in relation to data, and the recruitment of people with data skills?
- Infrastructure, tools and applications – how easy is it to perform data manipulation and develop results?
- Data quality – how much effort goes into monitoring the quality of data and resolving issues as they occur?
- Data security and privacy – how comprehensive and relevant are data security and privacy policies, and how well are they policed?
This is to discover the extent to which the environment within the Employer will encourage the Learner to apply what they know, and how far the business is from the ‘gold standard’ for practice as expressed through the survey questions. Also, whether there are additional learning opportunities that will help to embed good practice and give the business a further boost.
Part 3 – the learning timeline
Part 3 involves knitting Parts 1 & 2 into a learning timeline for the Employer that will ensure the best possible return from their investment in learning.
For example, if a business development manager wants to use data to emphatically confirm the benefit of a product, before their team become involved it will be essential for the senior Leadership team to understand the language that will be involved. This might involve short ‘data literacy’ workshops.
The Learning and Development team, whose job it is to source the right education services, might then want an overview of the technology involved so that they know what to look for.
Technical teams, who will collect the relevant data, may need to understand how data quality will impact the exercise. They might be directed to short self-learning courses about the statistics that underpin data quality management.
The use of Employer driven Learning Needs Analyses is on the rise. At FiguringOutData.com we support SME Employers within four separate programmes. Within each, Employer led analysis, similar to that above, defines how that support will be delivered.
But education providers need to come forward with delivery models that can respond to the bespoke requirements that come from this sort of analysis. There are signs they are there, but they have yet to surface in any meaningful way.
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