KPI analytics is important for every organization, no matter how small or large, no matter what industry or business market. Key performance indicator analytics provides metrics and measures for the business so that the enterprise can define success in an objective manner.
In this article, we will focus on the identification and exploration of data patterns and the trends that data reveals. The business can use this information for forecasting and planning, and to test theories and strategies. Let’s look at the various methods of trend and pattern analysis in more detail so we can better understand the various techniques.
The world of brick and mortar retail has changed drastically in the past twenty years and it continues to change. As we watch large and small retailers go out of business or close locations, we are forced to contemplate the undeniable consequences of online sales. While some retailers have found a comfortable balance between physical stores and online offerings, many continue to struggle with the concept of shifting products, customers and the shopping experience into the virtual world.
If you have been in business for a while, you know that, over time, things can get a little rusty and processes and activities can become rote. One of the greatest risks inherent in the business aging process is the use of outdated technology and, with the rapid pace of change in the market today, you have barely finished updating apps and websites and it seems it is time to take on the task again. We all need to assess our technology and undertake software reengineering in order to remain competitive.
Everything today is about metrics and measurements. Your boss, my boss, every executive wants to see numbers, statistics, graphs and charts that show progress or analyze problems. Modern business intelligence includes tools to establish key performance indicators (KPIs) but, when you are looking at a solution, you need to consider whether the KPI analytics solution allows you to really analyze the issues, problems, opportunities and root causes, or if it simply provides a tool for static monitoring and some pretty graphs, gauges and charts.
This article summarizes our recent article series on the definition, meaning and use of the various algorithms and analytical methods and techniques used in predictive analytics for business users, and in augmented data preparation and augmented data discovery tools.
If your business is looking for a comprehensive augmented advanced analytics solution, what are some of the critical factors to consider? OK, here goes!
Even if you do not work in IT, you have probably heard the term ‘Big Data’. These days, Big Data can cause big problems. If you do not have a plan to manage all that data and to make it easily accessible to your users, the data may as well not exist. If you cannot use that data for predictive analytics, you cannot accurately forecast your business results, plan for growth or compete in the market.
Isn’t it annoying to be out on the road on a business trip or at home trying to get some work done and discover that the tools you use in the office do not translate well to the device you have on the road? It is beyond me how software vendors can expect users to accept poor performance, difficult navigation and inappropriate quick fixes or workarounds that are designed to ‘make it work’ and result in user frustration, lost productivity and business delays.
Self-serve has many meanings. You can pump your own gas, you can serve yourself at a buffet, and sometimes you can even do your own data preparation. You will notice that I said ‘sometimes’. That is because you have to choose the right tool if you want to really participate in self-serve data preparation.
