A Transition In Thought
We at A Transition In Thought recognize that no two organizations' analytical needs are the exactly the same and that is why we help build customized educational and deployment programs that meet our individual clients' needs. The following are examples of how we address common problems our customers face.
Prior to the widespread need for most decision makers to understand large amounts of data, it was believed that the best approach this was to provide them with large tables of hard numbers because that is the way it has been done in Accounting field for decades. The reason that this perception, and by extension the entire analytics industry, has changed was in a reevaluation of the needs of different of data consumers.
In the majority of decision making, the ability to spot trends, identify outlying issues, and key gain key information quickly is what is actually important. While analyzing the hard numbers can sometimes bring you the same insights, it is much easier to identify this information with visuals designed for these express purposes. This does not mean that tables of hard numbers don't have their place, because when you have a reason the know the specific values tables are a great tool. Instead, what it means is that your analysts and management need to be trained on how to use these visual tools to better understand the business.
We suggest Tableau Software because it is easy to use, is highly flexible, and has visual analytics best practices designed into the core of the software. We at A Transition In Thought teach our clients how to use this software, teach analysts how to appropriately present their data, and work with management teams to embrace visual analytics. This culminates in a more insight driven work environment because decision makers are able to better understand the data presented to them.
Many organizations reach a point where their staff are performing highly repetitive tasks every month just to get the bare minimum report to decision makers. This paradigm is the product of traditional reporting methodologies where analysts would start with data exported from several systems, manually changed the content of fields to make sure it matches across reports, summarized the data, manually quality checked their work, and then produced a table of summary statistics that drives business.
The fundamental hurdle we address here is that highly skilled analysts are spending all of their time doing data preparation instead of analysis. Our typical suggestion to our clients is a software platform called Alteryx. When properly trained, an analyst with this tool can automate their data processing so that at the press of a button, or at a chosen time of day, all of the manual work will be executed by the program they build. On average this frees up 40 - 60% of analysts' time so that they can focus on other tasks, the most important of which is spending time gaining insights from the data.
Analytics software companies promise the world to their clients. Honestly, the promises that they make are typically fair, the software can usually do all of the amazing things that you have been told. However, what hasn't been made clear is that in the environments where the software has been highly successful, those organizations that the software companies hold up as examples of how their software can be transformative, there are highly motivated individuals with time, the right skill sets, extensive training, and monumental executive support for the effort.
When we come into clients where they expected to have a significantly better outcome from the deployment of new analytics technologies we work with the organization to find out where the issues are. Sometimes, the issues are as simple as IT never being involved in the process, so automation was not set up or planned for. Other times, the staff have been given the software, but provided no time to learn how to us it. Often analysts will have spent their time replicating exactly what they had instead of building better tools that the new technology allows for because they never knew it was a possibility. The final common issue is that middle or upper management have not been given the basic training in visual literacy and by extension make their staff use less than ideal techniques because it is something they understand. These are some of the complicated issues that come with embracing disruptive technology that we teach our clients how to overcome.
Distribution of information to those who need it is often the initial goal of organizations who actively seek out modern analytics. The reason for that is simply because automation and web based platforms are a key selling point of these technologies. The problems start to arise once you begin looking into who your data consumers are and try to figure out what data they need, how to ensure they can only access data intended for them, and making sure it is in a format they can understand.
When creating digital analytics tools you need to manage and govern which data can be accessed and how it is presented. This causes 2 problems for analysts who have never been developers before.
The first issue is potential security gaps, if you are in an environment where data access needs to be restricted, then educating these staff on what can be published and who is allowed to have access to different types of data is extremely important. As portions of this responsibility shift from governance teams and system administrators to the developers, setting up a platform for this education and communication becomes vital.
The second issue relates to how you communicate to different data consumers. Obviously, the technology platform you have selected is how you will technically distribute the information, however, in order to build a tool that will be beneficial to the data consumer there needs to be two more things in place. The developers need a mechanism to communicate directly with the data consumers, because it provides a clear understanding of what information is important and how the data consumer uses it. Without that knowledge both the developer and the data consumer waste time and tools often go unused. The developer also needs to be taught how to design tools that cater to the audience's anticipated level of visual literacy. There may be a complex visual that perfectly displays the information, but if that visual is meaningless to the data consumer including it can be worse than leaving the data out. We help our clients navigate and avoid these issues through developer education, and organizational communication.