Information as a Second Language, or ISL is a critical need. Research firm Gartner, Inc says that “Unless data and analytics leaders treat information as the new second language of business, government, and communities, they will not be able to deliver the competitive advantage and agility demanded by their enterprises.”
Organizations that lag behind suffer economically and will begin to lose market share to competition. Increasing data literacy is of vital importance.
The future is certain; ISL initiatives are not optional. A recent survey by Gartner, Inc found that poor data literacy is the second highest roadblock to progress. As organizations deploy more advanced analytics software, the need for “speaking data” is even more apparent.
And the need for data literacy isn’t just in maximizing investments in technology. It’s ensuring workers remain relevant and productive. As technology progressively replaces more and more of the “stare and compare” work, an information-language barrier will disrupt work as we know it – and not in a good way.
One of the key challenges in developing an ISL program is in developing an understanding that organizational change requires more than the traditional “people-process-technology” trinity. The modern, digital enterprise requires a new core element - "data." C-level executives and professionals alike must recognize the importance of data literacy and commit to improvement.
Why do we have mass data illiteracy?
One of the causes of data illiteracy is that data-driven decision making has traditionally been reserved for only a select number of individuals within an organization. Today, with the rise in the Internet of Things, and more embedded technology than ever, more decisions need to be data-driven by an increasing number of workers – not just higher management.
Another condition contributing to the problem is a lack of education. There are plenty of resources for learning about data, analytics, and statistics, but these are rarely (almost never) taught as part of any on-the-job training initiatives or continuing education. This is a hurdle that is only overcome with a solid instructional learning program.
Start With the Big Picture
How does the organization's culture relate to digital transformation?
If the efforts to go digital are flowing from the top of the organization down, then you’re in good shape. If the CEO or board of directors are not leading the charge for change, an uphill battle is in order. A successful ISL program is rooted in information-speak being a part of everyday language from the CEO on down.
What will happen to your organization if digital literacy does not increase?
Note industry trends and what the competition seems to be doing. If new technology is being developed and adopted, it’s most likely taking advantage of machine learning and new analytical capabilities. Successful adoption of this type of technology mandates a data literate workforce.
Has the organization experienced any big wins related to digital transformation?
If so, focus on these. Apply what was learned related to implementation and user adoption. There are probably some very good examples of learning new ways of working with data, new reports and dashboards, and new vocabulary. These are all elements of an ISL program.
How committed is leadership to digital transformation?
The key here is to align digital transformation goals with data literacy goals. If the commitment to digital transformation is low, that needs to be “fixed” first.
What are some measurable and specific outcomes of the intended ISL program?
The most important outcomes are those that relate to the organization’s core goals. A straightforward way to determine initial desired outcomes is to focus on one business process at a time. Determine where existing data that isn’t being used will add value to the outcome of the process. That’s a low-hanging fruit. Another suggestion is to add value to core processes by collaboratively aligning KPIs to new data metrics. Once trained, end-users and business leaders will provide extremely valuable insight and subsequently increase revenue.
How will you measure desired outcomes and prove the program a success?
This may be one of the most difficult steps of the journey if the organization is not already data-driven. If you don’t have a performance baseline for core processes, starting with a small-scale proof-of-concept ISL program is the best approach. If the organization is heavily data-driven, a not-so-obvious measurable outcome could be determining at a personnel level, how much more data is being used to make decisions. This could be determined by methods such as user access to data sets or by surveys.
Look for potential roadblocks.
Ensure funding for the program supports a minimum viable product. Achieving positive business outcome will likely mean sourcing additional training opportunities. Balance the costs of the program with this handy formula for calculating ROI: divide the net dollar value of each business outcome (or benefit) by the costs of the training. External factors, such as upcoming mergers and acquisitions must be considered. Internal factors such as company culture, processes, and management approach must also be carefully considered.
Is the program for everyone in the organization?
While the program may be made available to all workers at some point, it is best to start small. Starting with a proof-of-concept (POC) or only a subset of workers is a smart way to start out. The best way to determine who should participate in the POC is with a needs analysis. Resources for traditional training needs assessments are valuable. Look at the most and least data literate users within a department. Choose a subset of workers who are in the middle of that spectrum.
Needs Analysis – Questions to Ask
The following are some questions that should form a basic data literacy needs survey.
- How comfortable are you with visualizing data?
- How often do you look at charts or graphs containing data?
- How often does data drive your decision making?
- Could you make better decisions if you had more or different data?
- Do you track any metrics?
- Do you monitor any KPIs?
- Do you create or supply data to other departments?
- Do you depend on data from other departments?
- Do you ever feel frustrated trying to create a chart or graph?
- Do you wish you could understand charts or graphs better?
- Do you wish you had more training on data and analytics for better decision making?
- Do you dream of digital dashboards full of information?
- How do think data might affect the decisions you make on a daily basis?
- How comfortable are you with statistics?
- Do you know how predictive analytics works?
List Data-Driven Processes for Each Department
Because no department lives in a data silo, it’s important to form a formal understanding of the data requirements of all departments. If you don’t yet have a master data management solution in place, this is a good first step. Identify the processes and systems responsible for creating or managing the following:
- Transactions – like sales, purchases, deliveries, email, etc.
- Create Read Update Destroy (CRUD) – CRUD related to customers, products, assets, and employees
- Unstructured data – archived email / attachments, white papers, brochures, articles, intranet repositories, PDFs, etc.
- Hierarchical data – the relationships between other data
- Metadata – data about other data
List Vocabulary for Key Processes
Every organization is different. There isn’t an off-the-shelf product that provides the necessary vocabulary for ISL. Every organization has their own terminology and their own way of referring to common data elements and processes. Gain an understanding of how data, reports, metrics, and KPIs are referred to in each process. Pay close attention to what might be industry “dialects,” and what might just be organizational dialects.
Summary and Tips from the Trenches
Things to consider before you begin plugging things into your ISL program’s needs analysis:
- What is the purpose of the project – what am I trying to achieve?
- Who is my audience?
- What are the learning objectives/outcomes? Are they measurable?
- What does a learner need to walk away with knowing?
5 Tips to building outstanding content:
- You will always be interrupted with other work. Count on it!
- Set manageable milestones
- Plan extra time for writing and proofreading, and revisions
- Stay laser-focused on your objectives
- Explain everything – use graphs, data, and procedures to ensure there are no knowledge gaps