When a business engages its team members and asks them to take on the role of Citizen Data Scientist, there can be many misunderstandings regarding the new role. One of the most common misunderstandings relates to who does what in the role of analytics. Some common questions include:
If you are a business manager who wants to enable a Citizen Data Scientist environment, but you find yourself up against resistance, it is often because your business users believe that advanced analytics is just too hard for them to learn and that the use of these types of techniques and systems will slow them down and confuse their otherwise familiar processes.
If your business has entrusted you with the responsibility of a new role, namely that of Citizen Data Scientist, you may think of it as extra work or some sort of punishment. Nothing can be further from the truth! As a business technology user, you are in a unique position to combine your market, domain and industry expertise and your professional knowledge with augmented analytics that will support your queries, your problem-solving initiatives and your desire to improve on productivity, performance and revenue.
If your business has designated you a Citizen Data Scientist, you may feel intimidated and unprepared to take on the role. You are probably a business professional with a lot of knowledge in your chosen career and area of focus and none in the field of data science or business analysis.
As a Citizen Data Scientist, your journey may have been rocky. If your business did not plan for the cultural shift, the new workflow and processes, or provide support to help you scale up with intuitive, sophisticated augmented analytics, you probably struggled to find your footing. Perhaps you have yet to find that footing and are still looking for support. No matter where you are on the journey, one thing is for sure, if you don’t have the right tools and techniques to accurately and effectively use data, your Citizen Data Scientist role will not be fulfilled.
When it comes to the new world of analytics, the augmented analytics approach allows business users with no data science background to readily access and use analytics in an intuitive way. There are some important aspects of this approach, including auto machine learning, natural language processing (NLP) and intuitive search analytics.
The use of business intelligence and business analytics is growing in every industry, business function and in companies of every size. 48% of small and medium sized business CIOs responding to a Gartner survey revealed that business intelligence (BI), data and analytics is one of the technology areas that will have the largest amount of new or additional spending.
If your business is trying to incorporate data analytics into the fabric of day-to-day work, you will need to get your users to adopt analytical tools. The way forward is not all that complicated. The solution you choose must take an augmented analytics approach, one that includes simple search analytics, ala Google search. Natural Language Processing (NLP) and NLP Search tools are key to this approach as they allow business users to ask simple questions and get answers. If a user does not have to create complex queries or look to business analysts or data scientists or IT professionals, that user can incorporate data into information sharing, reporting, presentations and recommendations to management.
Smarten announces its Smarten Clickless Analytics, a Natural Language Processing (NLP) Search Analytics tool designed for every business user, no matter their technical expertise or data skills. Natural Language Processing Search Analytics (NLP) is an easy-to-use search analytics technique that allows business users to create complex searches without endless clicks and complex navigation and commands. Using this familiar Google-type Clickless Analytics, users can leverage these tools to discover trends and patterns, receiving results in simple, clear language the user can understand.
You have selected an advanced analytics solution and deployed it across your enterprise, but your business users are not cooperating! One of the primary reasons for poor user adoption of this type of tool is that the tools are too restrictive and too difficult to use. Perhaps IT created the dashboards and they do not meet the needs of your business user, perhaps the business user needs help to engage with the system. Your users may have tried their best to use the system but the results they receive when they search are not accurate or do not meet their requirements. An advanced analytics system that is not intuitive, is complex or requires programming skills just to perform a simple search is not going to work for your business.
