Apply strategies to increase BI adoption
Take your team on a data journey
Increase team engagement
Train the team so they are not afraid of new tools
Avoid common mistakes when creating dashboards and reports
Show the team how BI reports outclass other tools (e.g. Excel or Power Point)
According to a 2018 poll carried out by G2 Crowd, employees claim that their companies implement Business Intelligence tools with a user adoption rate of only 51%. At the same time, marketing automation tools are said to be adopted at a rate of 71%, CRM products at 77%, and accounting software at 79%.
Why, then, is user adoption of BI so low? After all, efficient data processing is a priority for modern businesses. Chief Information Officers, Chief Technology Officers, and Chief Data Officers all preach the benefits and necessity of Business Intelligence tools for business, but quite often the majority of a given company’s employees are not acquainted with BI software. This is a natural barrier to progress here. Therefore, one of top technical executives’ key tasks is to lower the threshold and demonstrate how easy it is to kick off a BI project.
BI projects should be clearly and properly defined in order to allow each participant – even an unskilled one – to observe their contribution and learn from the process. Therefore, C-level executives should encourage greater autonomy and build a sense of purpose for their employees.
And flexible, agile, and scalable BI applications for content creation and management, infrastructure, and data generation and analysis are obviously better than rigid ones.
Flexible BI projects are often cross-functional, but the teams involved tend to resist change. The effort to successfully carry out such work is a mutual effort of senior management and the rest of the team.
With their leaders, teams need to ensure that data is of a decent quality from the beginning of a project by gathering it in a structured way and/or pre-processing it. After all, BI applications depend on it and successful implementation will drive company revenue.
Creating a culture of using data and making decisions solely based on it will strongly encourage employees to use advanced BI solutions on a daily basis. Spreading data culture will help workers be aware of – and understand – BI products and their features. And they are then more likely to use these products effectively every day.
Rewards for using data to solve problems may be a good starting point for BI leaders. Studies show that even something as simple as verbal praise can increase intrinsic motivation better than financial benefits.
Another idea is to take a startup-style approach towards data and innovative Business Intelligence tools. This approach comprises of experimentation, learning, agile adjustment to dynamically changing situations, using business communication tools, surveys, and listening to workforce ideas and complaints. It may also come down to informal pitches for ideas and voting for the best ones within a focus group of employees. This may create valuable feedback for C-level staff.
A company can also appoint a certain team to play the role of early adopters who can later share their knowledge and experiences with their peers on forums and in trainings. Announcing a successful BI project may also kindle enthusiasm for new data solutions in other teams. Setting up a website for communication purposes can additionally inform the rest of the staff and be part of a training program to help get them started with their own BI-powered projects.
Obviously, providing the inspiration to use BI tools and building enthusiasm accordingly are primarily up to company leaders. This enthusiasm can be seriously dampened when plowing through unstructured or irrelevant data. Therefore, it is the role of the leader to sort out basic relations in datasets and deliver relevant information to each department. Sometimes, looking for anomalies or outliers in the data is easier than handling large volumes of data in search of certain structures or elements. Departments can therefore flag specific products or services instead of being discouraged by messy or difficult data.
BI outcomes may be a bit abstract or obscure in comparison with daily corporate routines. Sometimes it is harder to find immediate benefit in them while you are swamped with work, urgent deadlines, and other pressing priorities. In order to win this fight for employees’ attention, BI tools should send automated notifications or alerts about data anomalies or relevant reports. Searching for information is time and energy intensive, therefore it is immensely useful when BI automatically sends an informative breakdown of key facts.
Another important feature of BI projects is their context. Context gives numbers a practical meaning. Therefore, early discussions about data among departments, including the analytics department, are a must. Getting all employees up and running with BI tools requires the leaders to be engaged on a regular basis.
A database of affinity can help a workforce accumulate the information necessary to be truly useful for users/consumers. This database provides social behavior data and enables the prediction of future consumer behavior, purchases etc. This kind of database is gigantic, so analytics leaders should gather recommendations and hints from other departments about the metrics crucial in their domains. This will allow precise diagnosis of the market and increase engagement while decreasing workload.
BI platforms are flexible but therefore require continuous training and leveraging vendor resources to create FAQs, case studies, blogs, webinars, tutorials etc. These help other users continue getting maximum value after the onboarding training.
Constant training is undoubtedly a must in a modern business, but it does not mean that it has no pitfalls. There are obvious learning gaps, which are deepened when experts or software vendors know their BI systems – and all their advanced features – very well on the theoretical level. This approach may cause business users to be perplexed when presented with real-life data. To counteract this problem, users need to be trained in the context of the data used on a daily basis and learn to apply BI tools to individual business situations. Encourage company workers to bring their own datasets to trainings.
Another possible issue is the timing of trainings. It makes no sense to train an employee for a few months before they start using their knowledge. Experience acquired needs to be immediately put into action in a specific project. Otherwise it is going to be forgotten, and the worker will return to their usual patterns of action involving, for example, Excel spreadsheets.
Finally, do not treat business users like the IT crowd. BI technicians and developers focus on entirely different aspects of the tools, while other employees would like to know how they can help solve their daily tasks better and faster than with “good old spreadsheets.” Average users do not need to understand a BI system’s fancy features. Developers/technicians should understand the needs of non-technical business users.
BI dashboards’ effectiveness depend heavily on User Experience. If every operation takes half a minute, a business user will give up. BI tools are already better than obsolete operations on multiple reports, and they usually do not require logging on to multiple platforms. However, these things can still be an issue in poorly planned BI platforms and developers should bear it in mind. Solutions like Active Directory Groups may significantly reduce inconvenience.
Unattractive design may also lower willingness to work in a BI system. A minimalist, aesthetic design will help users focus on their tasks. Minimalism also requires additional (and often useless) metrics to be hidden or not present at all. Moreover, if there are a lot of necessary metrics, there should be clear rules and options to aggregate or analyze them. In this way, they will be easier for employees to use and would allow for more diverse reports from different users.
Similarly, poor BI design requires manual input of additional datasets and prevents a system from automatically refreshing data. The fewer manual input requirements the better.
It is obvious that old-fashioned spreadsheets are nowadays not cutting-edge professional analysis and reporting tools. They are static, rigid, and vastly inferior to modern, flexible BI tools. They require that all data presentation, processing, imports, consolidation, and aggregation from different sources be done manually or with macros – suboptimally, without automation.
You can also forget about planning, predicting or forecasting in old-school software unless you have the requisite time and skills. BI tools, in turn, often allow data visualization from the very beginning. This way, creating new analyses and predictions is much faster and more intuitive.
Detecting statistical relations in data and presenting them in spreadsheets is very cumbersome and time-consuming. Also, sharing the final report is not very convenient, while in modern BI systems it is just a few clicks away – maybe even on a mobile device.
Finally, compared to BI tools. security is weaker in old spreadsheets, especially regarding access controls and filtering policies.
Your company may have lots of intelligent solutions, but the human factor is just as important. Unconvinced people will shun unknown tools, no matter how innovative or useful they would be.
Therefore, how engaged an entire team is constitutes a key success metric. Consider people’s curiosity about BI tools and if they are asking for more opportunities to discover interesting insights from their data on their own. Encouraging employees to use BI daily will help them grow their technical and business capabilities.