Organizations face a number of challenges: Many employees are not trained to handle data, and departments often don’t know what data they can access or use, or they do not have the right tools to work with. These factors prevent companies and researchers from becoming aware of the value of data in their daily work and from fully exploiting it. But brand sentiment analysis, social media analytics and image research profit from various inquisitive minds exploring data. Self-service analytics gives researchers the chance to ask their own questions, take different approaches to their data and bring in new perspectives into the analyses. However, empowering all employees to regularly explore their data in greater depth goes beyond providing technology or accessing information. It's about living a culture of data analytics. How can organizations develop and maintain such a culture and promote a new way of “data thinking”?
Eight Steps to an Analytics Culture
Step 1: Data Culture is Based on Trust
An analytical, data driven culture starts with “empowerment”. Employees need to be empowered to explore data on their own and answer their own questions. This includes a certain level of trust that managers must have in their teams when dealing with data. Modern self-service Business Intelligence (BI) tools help to curate data, so everyone is able to access the data they need without compromising sensitive data and governance regulations.
Step 2: Clean Data is the Basis
Regulated data management forms a strong basis for the entire analytics pipeline. This includes a thorough preparation of data before it is distributed throughout the organization. Data from different sources, similar values in different formats and a growing data pool are the biggest challenges to a clean data basis. Good preparation is also extremely valuable – it saves time for the teams who rely on a good data basis and leads to credible and trustworthy results that everyone can work with.
Step 3: Ask Questions, Questions, Questions
A basic understanding of how to use technical tools and analytics methods is essential for data-driven work. What managers sometimes forget when they train their teams with new tools and methods is that they also need to establish a certain way of thinking and approaching things. It’s just as important to be a critical thinker and curious about analytics as it is to be able to work with powerful BI tools. Companies can only see the true value of their data if they keep asking new questions time and time again.
Step 4: Dealing with Data is a Basic Skill
Until just a few years ago, basic knowledge of Office applications was part of every CV, today these skills are simply taken for granted. Instead, analytics skills and BI knowledge are in high demand today. Even though data specialists are still responsible for specialist tasks like data management and preparation, today everyone – from human resources to marketing to product managers – should be able to read, understand, communicate and use data as information. Companies should not only train these skills but make them a top priority when recruiting new staff.
Step 5: Ignore Your Gut Feeling
For a data-driven culture to evolve and for employees to be motivated to think the right way, team leaders and managers have to base their decisions on data-driven responses. Rather than asking teams for their opinions, executives need to ask for recommendations that are supported by data. Moreover, they should expect the same approach from their teams. A clear understanding of the value of data for decisions is the basis for every successful decision- making process or meeting.
Step 6: Enter into a Dialog with your Data
When advanced technological applications are difficult to access, they are often not used. If organizations want their people to work with data, they need to make it as easy as possible for them to access it. Self-service BI applications have already dramatically lowered the access barrier. Natural Language Processing (NLP) – the ability of computers to understand human language – further lowers the entry level professional analysis. Successful systems can transform nuanced language and colloquial expressions seamlessly into queries. When people are given the opportunity to interact with a data visualization in a natural conversation, more and more people of all disciplines will be able to ask questions about their data and get deeper insights. As natural language evolves in the BI industry, it will remove obstacles to analytics adoption and help transform workplaces into data-driven self-service operations.
Step 7: An Analytics Culture Needs Patience (but not too much!)
The implementation of an analysis culture is a long-term process and cannot be done overnight. But organizations should not wait too long. Start with initial projects that can serve as best practice examples and an inspiration to other teams and departments. Encourage your teams to exchange experiences and tips in data communities where they can learn from each other and promote a data culture within their organization.
Step 8: Convince with Data Stories
If you cannot communicate the results of your data analyses properly, you won’t be able to fully exploit the true value of your data. This is the strength of data visualizations. Instead of drawing a single conclusion from data as it is usually done in a static report, today’s methods of data storytelling focus on a conversational, interactive approach to analyzing data. Instead of distributing a finished report to all participants of a meeting, data visualizations enable a joint interactive dialog with the data. This method combines all aspects of a data-driven corporate culture: it promotes a culture of curiosity, of asking questions where employees are given the necessary trust to explore and access important data to gain valuable insights. ■