In our ‘Guide to Culture & Skills‘, we outlined how a well-designed data and analytics training program is vital to improving data literacy in your organisation.
Many companies struggle when starting out on the transition to becoming data-driven, for a few potential reasons:
- Low data literacy and lack of related skills are among the top four barriers to data and analytics success.1
- Data and analytics training often focuses on how to use tools or applications, but leaves users unprepared for the accompanying business and process changes required.
- Organisations either lack formal data and analytics training programs or fulfil individual training requirements in silos that don’t support enterprise-wide initiative.
Modern training programs should include a mixed portfolio of training approaches and tap into adjacent competencies as a foundation to drive greater value. We’ve drawn up the following list of topics every organisation should consider in their data literacy curriculum:
1. Data literacy overview
Start with the basics. Introduce the concept of data literacy and explain it so that everyone – all levels, all functions – understands how and why it benefits decision-making. You can do this by highlighting the increasing body of evidence that demonstrates the link between data competency and business performance.
You can get your employees excited by showing them the benefits they can reap from becoming fluent in the language of data, from empowering their professional development and business acumen to making day-to-day tasks simpler.
2. Data and analytics leadership
Set the right example for data and analytics leadership by outlining the benefits of a having a clear data and analytics strategy. Put a training program together that demonstrates how leadership can influence the success of this strategy, and train leaders on how to make the business case for data and analytics.
You can also provide sessions on how to promote and encourage a data-driven culture. This will arm your data champions with the tools they need to take ownership and push adoption.
3. Stakeholder engagement
Good analytics cannot be done in isolation. In addition to their technical skill set, your data and analytics team need the consultancy skills to be able to engage with stakeholders from across the business, to understand their needs and appreciate the context.
This can involve training on facilitation skills, business analysis and requirements gathering, data storytelling, design thinking, decision management and the design of performance metrics.
4. Data and analytics delivery
Key to the delivery of individual analytics projects is the ability to successfully manage and deliver projects and programs, so make sure to provide training in these areas.
In addition, make sure to add in training that augments the ability to work with third parties. The ability to deliver projects via external service providers is a key skill in a field such as analytics, which has many specialist sub-domains, the competencies for which may not be available in-house.
5. Data and analytics organisation
A good strategy is one that is executed. One of the keys to any data and analytics strategy is the ability to bring together the skills needed to implement it. As such, make sure to include training on organisational structures, roles and responsibilities.
Encourage the proliferation of what Gartner calls ‘citizen analysts’ within your organisation by training on how those outside the ‘inner circle’ of analytics can nonetheless leverage its potential in their own roles.
6. Data governance and management
We’ve written elsewhere about the importance of data governance, best practices and standards. Make sure you include an element of training on data and analytics governance practices and the separate roles and responsibilities that play a part in broader data and analytics activities.
On a more technical level, it is important to provide individuals and teams with training on how to catalogue and curate information as an asset correctly, including Master Data Management (MDM), metadata management and taxonomies. Finally, data quality protocols are obviously of paramount importance, so include this on your curriculum too.
7. Information security, data protection & privacy
Obviously, there are a lot of rules and regulations around information security, data protection and privacy. Make sure your team has the knowledge and tools required to stay aligned with these as they change and evolve by including training on information security practices, data protection legislation and data privacy best practices.
8. Data management and architecture
The architecture that underpins your analytics activities, and how it is managed, needs to be trained. This will require programs on how to choose and monitor data management processes, the ins and outs of data modelling and design, data hubs & integration, managing external sources of data and open data stewardship.
9. Analytics methods and practices
Everyone at all levels should be versed on how analytics methods and practices can be used within their own functions. Training topics to cover this include the identification of business analytics opportunities, platforms and tools, research methods, as well data visualisation, preparation and how to develop analytical models.
10. Advanced analytics
As your organisation begins to grow in analytics maturity, or if you already have a base level of competency, you can look to train on advanced analytics methods, such as data science and machine learning processes, artificial intelligence, bias, IoT and streaming analytics.
To reiterate the opening message of this article, learning and development is a crucial element in ensuring investments in data and analytics deliver a return for your business. Not only does it ensure you have the skills available within your organisation to execute on your data and analytics strategy, but it empowers your employees and encourages adoption of data-driven decision making on a general level.
This article is part of our Data-Driven SMB series. For more information, advice and resources on how to accelerate your organisation’s data and analytics maturity, click here or contact us today.
Max has a background in Business Intelligence, Analytics and Data Warehousing with over 12 years in the field. He leads the delivery of enterprise level Analytics projects with a mix of technical and consulting elements at Truecue. He has been working across a multitude of industries and strives to get business value and insight out of each project, he enjoys the challenge of solving data problems with the intention of delighting our customers.