Creating an Analytics Literacy Program
To take full advantage of data and analytics, organizations must develop the knowledge and skills to use analytics to drive better business decisions.
- By Alex Campos
- October 16, 2020
Today’s organizations are investing in the technologies and capabilities to take advantage of data analytics in order to stay relevant and competitive. Every day we witness successful use cases, but we also hear from organizations that couldn’t reach their goals for data and analytics. The truth is that there is a giant gap between expectation and reality -- and a long way to go.
Data is both an intrinsic result of digital transformation and an asset that is expected to be monetized and profitable. Looking at companies that have embraced a data-driven culture and succeeded in using data at scale, a common factor is working to prepare people for coming changes, both technological and cultural.
The objective of democratizing information is not only establishing data lake-type platforms and processing capabilities but also educating the different levels of the organization to use data in daily activities.
Do You Speak Data?
In the same way that companies demand candidates who speak Spanish or Chinese, being able to communicate in analytics terms is already a requirement and priority. Data communication ranges from knowing programming languages (such as SQL or Python) to being able to express analytics results through presentations and reports -- or indicating the appropriate algorithm depending on the analysis you want to obtain.
The skills to speak and communicate with data must be spread and promoted throughout the organization, not just in technical areas that handle data directly. It doesn't matter how aligned the organization is with new digital trends and communicating data-driven values, the organization needs an analytics literacy program that allows people to feel comfortable with the daily use of analytics to execute their functions. Speaking "data" as a second language should be on the digital transformation agenda of any enterprise.
What Is Analytics Literacy?
I’d like to offer this definition of analytics literacy, which is based on Gartner’s definition of data literacy: Companies that develop the skills of analytics literacy are able to read, work, communicate, analyze, and sustain business decisions using data, regardless of the technological solution, technical level, or people’s roles.
From this definition, the following points are worth highlighting:
- "... able to read, work, communicate, analyze, and sustain business decisions using data ..." represents the organizational capacity to transform complex data into knowledge to drive better business decisions and actions.
- "...regardless of the technological solution, technical level, or people’s roles." represents the level of information democratization and the definition of a corporate strategy that considers data as an asset.
Once data and analytics become relevant for an enterprise’s operations and part of the digital strategy, employees must have the minimum skills to communicate and understand conversations related to analytics.
The first step in developing a literacy plan is to identify the learning needs of the people in the organization. As with any language, people start out with different knowledge and skills, sometimes related to their functions.
Fluent: People able to develop and explain different analytics processes, such as data ingestion, preparation, and correlations. They can perform descriptive and/or probabilistic/advanced analytics. Generally, this group is more technical.
Translators: Individuals who generally have high knowledge in specific business areas or functions and are capable of translating the needs of a specific business domain into analytics solutions.
Proficient: People from business areas who are capable of incorporating a certain degree of analysis in decision making. In general, the types of analyses they develop are specific because they usually access prepared and enriched data from certain business domains.
Beginners: People able to incorporate little analytics in their daily activities, mainly due to a lack of skills, basic technical knowledge, and/or access to data.
Within this hierarchy, the Translators support the Proficient and Beginners in identifying opportunities to implement analytics in business processes, acting as a bridge with the Fluent, who can provide a more technical evaluation and corporate vision.
Designing an Analytics Literacy Program
Once each individual's learning needs have been identified, it is time to define and develop your literacy program. Consider the following aspects:
Planning: Define program times, financing, communication strategy, participants, and stakeholders.
Results: Identify improvements that are expected after program implementation. It is important to define success criteria that will be accepted by stakeholders and at the same time are realistic and achievable.
Content: Generally divided into two main groups:
- Technical: Define technical content, such as programming languages, platform/systems workshops, integration, infrastructure, and information security. Some roles require specialized content such as data science, teaching algorithms for machine learning, Python, and advanced analytics frameworks.
- Methodological: More focused on the business and data initiative generation, this content introduces people to analytical thinking, leveraging use cases and workshops for solutions based on agile techniques such as design thinking. Topics related to data governance are also important for making people aware of data use and security.
Evaluation: Establish methods to measure the impact of the literacy program: both the direct results of the implementation and cultural changes. The latter can be observed when data and analytics are used in meetings to sustain decisions or in the way users express their analytics needs.
Monitoring: A literacy program is not a project with a start and an end date. It is a continuous work seeking to deliver necessary skills to people. Content updates should also be planned for to keep up with technological changes.
The program must have a continuous improvement approach; both individuals and the program itself must advance in analytics knowledge and skills.
People in charge of the program must re-evaluate it frequently (every 3 to 6 months), sharing its successes and failures and identifying opportunities for improvement. The techniques, tools, and methods related to analytics change frequently.
Successfully implementing a data strategy is directly related to and aligned with your business strategy, which seeks to generate competitive advantages in a changing and volatile market. An analytics literacy program will help your organization develop the necessary skills to stay relevant, continue to innovate, and deliver value for the market.
Alex Campos, an enthusiast in digital business agility and big data, is helping companies in Europe adopt analytics to accelerate data-driven culture adoption. Working for Cloudera, a company that provides hybrid and multi-cloud data platform, his role is to help customer to adopt open source tools to support their data strategy. You can contact the author via email or on LinkedIn.