Are you preparing to transform how your organization approaches analytics?
If so, you’re not alone. Respondents to a McKinsey survey said that data analytics is the skills gap companies had the greatest need to address.
But transformations aren’t easy. Around 70 percent of projects fail, and the average transformation project stands a 45 percent chance of delivering less profit.
And analytics skills gaps are one of the main reasons transformations fail. If you’re thinking of transforming your but are facing internal skills gaps, here’s what you can do to ensure your mission ends with success.
Conduct a Skills Gap Analysis
Skills gaps are inevitable. 43 percent of companies are experiencing them now, and 87 percent expect to have skills gaps within the next five years.
The best first step is to run a skills gap analysis, which you can do by following this list:
1. Identify the skills you need to stay competitive within your industry both now and in the future
Consider the skills you’ll need now and 5-10 years down the road. Include analytics endeavors such as data science (DS), machine learning (ML), and artificial intelligence (AI)
2. Assess your current skills at both the individual and team level
Look beyond job titles and roles. Each person will bring varying experiences and knowledge to a team. Then, evaluate each team. Ask yourself what each team would need to meet the goals your company has for the next 5-10 years.
3. Assess Your Current Analytics Technology
Using your skills analysis, determine which technology you will need to reach your goals. Sometimes, your current tech stack can exacerbate — or close — gaps. Consider how your current analytics solutions negatively or positively impact skills gaps, talent retainment, and future hiring.
Align and Create a Plan to Address Gaps
Once you know the gaps you need to address, it’s vital you align your plan with your organization.
As we’ve mentioned before, transformations fail due to:
- Unspoken disagreement among top managers about goals
- A divide between the digital capabilities supporting the pilot and the capabilities available to support scaling it
To counter this, you can work with your organization to:
1. Clearly communicate your analytics transformation goals and outcomes
What outcomes do you hope to achieve and what’s the time frame for them? It’s best to start small with one project, but having a long-term plan is important for ensuring success. Goals can include having at least one member of each team experienced with machine learning and successfully implementing explainable AI best practices.
2. Align goals across departments and teams
If one of your goals is to have each team proficient in analyzing their data, then you need to provide each team with the training, resources, and technology access to achieve it. Knowing your expected outcomes will drive the expectations — and your planning — for teams and data workers.
3. Set expectations
You can increase your chances of success by setting clear expectations. Expectations can include things such as understanding basic machine learning concepts, participating in 30 minutes of training a week, developing a team-driven data strategy, and more.
The more people know the reasoning behind changes, the more you use their input to drive changes, and, crucially, the more likely the changes will help people achieve, the more likely your team is to get on board and adopt new changes.
Pick an Opportunity
An analytics transformation can fail for two reasons: (1) The goal isn’t clear enough and/or (2) the goal is too broad.
The best approach to enact company-wide changes is to start small and scale. Your first project can be as simple as automating a time-consuming process, such as data preparation or reporting.
[See How Automating One Step Led To 1,500+ Uses Cases]
The key is to focus on something that’s easy to achieve but also time-consuming. You can then use the time savings from your first opportunity to expand into other areas.
Additionally, starting small makes it easier to gain alignment and set clear expectations — especially when you have skills gaps.
Search for and Identify Solutions
Once you’ve identified skills gaps, aligned teams, and identified an opportunity, all that’s left to do is find the right analytics platform to help you.
Here are questions you need to answer about any potential new vendor or your current solutions:
Does it scale easily?
Consider how it scales for both expanding users and expanding talent. As you enact your analytics transformation and address skills gaps, can your analytics platform provide a range of services?
For example, can people use it to solve both repetitive data tasks and provide machine learning? Can data workers, leaders, and executives use it for their needs?
Does it prepare your company for success?
Addressing skills gaps includes gaps created by departing employees and new hires. Your platform needs to enable you to attract new talent, onboard them, and retain them as they grow.
Does it enable collaboration?
Collaborative environments lead to increased success, but collaboration has become more challenging with the shift to a more remote working environment.
The right platform will include tools that make it easy to share assets, explain data lineage and processes, work across different time zones and regions, and more.
Conclusion
Driving analytics transformation while facing internal skills gaps presents challenges to any organization.
Before you start investing in solutions to address them, take a step back, assess the situation, align your teams, and pick a time-consuming yet high-value use case. Then, empower your teams to do the rest.