Wise business owners and managers are taking on the challenge of implementing data democracy, digital transformation (Dx), and Citizen Data Scientist initiatives. The reason is simple! These businesses understand the benefits and the advantages of providing augmented analytics access to business users, thereby improving fact-based, timely decisions and improving the planning and forecasting process. These initiatives also optimize resources and allow the business to do more with less – which is something every business needs!
If your business wants to commit to self-serve BI and augmented analytics, it is important to recognize the challenge of user adoption. You can provide the best solution on the market but, if you do not deploy these initiatives in the right way; if you do not make the right choice for solutions and the right approach to improving data literacy, your investment will be for naught. A Gartner Analytics and BI Magic Quadrant customer reference survey found that the average user adoption rate for modern analytics and BI Adoption was estimated at 35%. Among the reasons for poor user adoption were the absence of self-serve BI capabilities, dependence on IT team, inflexibility of data silos, and lack of access to intuitive BI tools for deep dive, predictive and descriptive features.
In this article, we discuss several considerations and important factors that can and will improve user adoption of augmented analytics:
Technology And Features
Embedded BI and Integration APIs
If your business wants to leverage analytics and ensure user adoption rate, lower TCO and improve ROI, it is wise to include Embedded BI and Augmented Analytics in your requirements. Integration APIs provide intuitive, self-serve BI tools from within enterprise applications and public websites, so you can put the power of facts, data and business insight in the hands of business users. With access to self-serve analytics from within familiar enterprise apps, BI adoption and use increases exponentially, so you can leverage all the benefits and advantages of augmented analytics.
Natural Language Processing (NLP)
Solutions that incorporate Natural Language Processing (NLP) allow business users to leverage an analytical tool in an augmented analytics environment where questions can be entered using natural language, BUT NLP without context-capability can still miss the mark. Remember that very team member is also a consumer outside of work. In that capacity, they have learned how to search for something on Google and get the results they need. The tools are designed for human interaction. You ask a question and get an answer. Providing NLP in augmented analytics tools allows team members to use familiar concepts and features to find and analyze data, thereby improving user adoption.
Business Processes And Culture
Committing to Culture Change
The enterprise must be prepared to support user adoption and empowerment. Just teaching the basics of data analytics and the use of tools is not good enough and no matter how simple the solution is to use, your team members will need encouragement to change processes and to commit to the change. Focus on users and managers and build an understanding of the context of data within a particular team or department. Mentor and support users as they make the transition from being data dependent to becoming more autonomous and data literate. To make the transition to self-serve augmented analytics, the business should find and nominate champions within each area of the business and look for opportunities to suggest and support projects that entail data analysis and presentation with the goal of creating actionable outcomes that will encourage the team and individuals to embrace data analytics and use it as part of the process on a daily basis.
Metrics and Evaluation
Tie metrics and data analytics to goals and objectives to encourage analytical deliverables and insight. Courage the collaboration of IT, Citizen Data Scientists and analysts or data scientists to further solidify the organization. Review evaluation criteria and include team assessments based on the consistent use of data to make recommendations and decisions. Managers should routinely ask if team members have used augmented analytics to test theories and hypotheses to improve the validation of business decisions and to get the best outcomes.
These are just a few of the critical considerations and factors your business should include in its solution selection and implementation planning process in order to ensure successful implementation of analytics and to support data democratization and/or a Citizen Data Scientist initiatives. With the right planning and approach, there is no reason to be discouraged by the reported history of poor user adoption. With Embedded BI, Integration APIs, Natural Language Processing and culture, process and metrics transformation, your business can succeed in its implementation of augmented analytics and meet its goals for ROI, TCO and user adoption.
For more information about how Augmented Analytics can support your initiatives, explore our white paper: ‘Integrate Augmented Analytics And Digital Transformation To Achieve Continuous Business Improvement’, and the features of Embedded BI And Integration APIs, Clickless Analytics And Natural Language Processing (NLP).