Data Literacy is a hot topic in business today.
As businesses struggle to remain competitive and to do more with less, every enterprise is looking for a way to optimize resources, and to get the most out of every team member. To do that, the business management team must leverage the knowledge and skills of each team member as those skills relate to their role and their responsibilities. Businesses must use team member knowledge gleaned from experience and education and merge that knowledge with data and analytics to make context-based, data-driven decisions.
When a business sets out to take this evolutionary step, it can leave its team members running to catch up and falling behind.
Even among the most data literate corporate environments, the insatiable need for data and business obsession with data-driven initiatives can cause stress and these initiatives can backfire if they are not managed appropriately.
while India businesses have embraced data literacy and encouraged team members to take training and adopt data and analytics to support decisions, this new revolution has placed additional pressure on team members.
The Accenture Report, entitled, ‘The Human Impact of Data Literacy’ reveals numerous flaws in the approach businesses have taken in an effort to inject data into the average business conversation.
These studies indicate that:
- ‘Only 32 percent of business executives surveyed said that they’re able to create measurable value from data, while just 27 percent said their data and analytics projects produce actionable insights.’
- ‘Organizations need to recognize that the exponential growth in data usage has accelerated far beyond the skills and confidence of the employees required to use it. Only 25% of employees felt fully prepared to use data effectively when entering their current role.’
- ‘Despite nearly all employees recognizing data in the workplace as an asset, few are using it to inform decision-making. Only 37 percent of employees trust their decisions more when those decisions are based on data, and almost half (48 percent) frequently defer to making decisions based on gut feeling over data-driven insight.’
Although India businesses have pushed hard to embrace data analytics and data-driven decisions, the push for data literacy has often resulted in the same types of stressors and mixed results noted in research studies.
These studies reveal that India survey respondents reported only ‘46% of respondents in the country being confident with their data literacy skills.’ And, while 53% of India respondents trusted decisions more when they were based on data, 80% frequently deferred to a ‘gut feeling’, rather than data driven insights when making decisions.’
It is important to note that 53% of the India survey respondents said that they believed ‘data literacy training will make them more productive’.
So what does this research tell us and what can the survey of India-based data literacy initiative respondents tell us?
What it tells us that while businesses and team members recognize the value of data-driven, fact-based decisions, team members do not feel that they have been given enough support or training to help them confidently adopt and embrace data analytics and the resulting lack of confidence is causing additional stress among team members. This type of stress can lead to loss of productivity and it can mean that team members will look elsewhere for a career that is not as demanding.
Empowering team members is great and holding them accountable is necessary. But, if a business intends to hold a team member accountable, it must first provide the training and tools needed for the team member to perform well. When a business provides an appropriate environment, mentoring, training and tools, it can expect improved productivity and improved team member satisfaction.
So, what does this mean to businesses that want to engender data literacy among team members and succeed in creating a collaborative environment that optimizes resources, knowledge and skill and allows for team member confidence and increased productivity?
With the right approach, a business can successfully bridge the gap from scattered data knowledge or silos of data acumen to a data literate team that can easily embrace this new world and adopt processes that incorporate data in decisions. Businesses can also ensure that team members will willingly adopt and embrace data analytics and use these tools in their everyday process to accomplish tasks and make better business decisions. Data Literacy does not have to be as complex as you may think. To achieve this goal, a business must nurture a data literate environment and culture and encourage team members at every level and in every function and role. For that, the business will need software and analytical tools and champions and experts working in collaborative and liaison roles to help team members adapt and incorporate analytics and data into everyday processes and activities so every team member can marry knowledge, curiosity, theories and hypotheses with data to make the best decisions.
To educate and support team members in data analytics, the software tools a business provides must allow for augmented analytics: analytics tools that provide guidance and auto-recommendations to help users choose a visualization technique that is appropriate for the type of data they are analyzing, and assisted predictive modeling tools that help users select the right algorithm to complete the analytics they wish to perform. Natural Language Processing (NLP) and machine learning foundations allow team members to ask questions and get answers using natural language so they need not be a data scientist or IT expert to get to the heart of the data, forecast, plan, find opportunities, see trends and patterns, product and share reports and function as Citizen Data Scientists.
Businesses that try to use shortcuts to get to data literacy or not likely to achieve the results they want and will definitely frustrate and discourage their team members in the process. Businesses that plan for and implement a Data literacy and data democratization initiatives with appropriate cultural changes, collaboration among user roles and simple, intuitive, self-serve augmented analytics tools will more quickly and successfully achieve data literacy and improve user adoption.