Is your data analysis strategy providing the most value to your client? While some SAS products, like SAS Intelligent Decisioning, provide better insight into customer interaction, there are steps in the process preventing us from unleashing the full potential of analytics. For example, is our data relevant? Are we using the latest technology? As SAS consultants, we should always be ready to examine our processes and see if we are using technology to the best of our ability, which is what I will address in this blog post.
As you collect the right data? SAS analytics programs collect data from different sources, but clients are looking to fulfil specific objectives, and they need the most relevant data to make it happen. For example, a major retailer wants to see how customers interact with their conversion funnel. Some of us would collect behavioural data to inform clients about the click and conversion rate. But what about missed opportunities? For example, what were customers looking for, but didn’t get? What were the prices that were originally quoted? All this and more can be captured through experiential data. As analysts, our data analytics strategy should always be to deliver the complete picture to our clients.
From collecting raw data for processing to producing comprehensible reports for clients to understand, SAS analysts have so many responsibilities. Hence, we need to make sure that we are using the most efficient processes to complete our work on time. Even the smallest misstep can make a huge difference in our daily work. Sticking with the example of marketing, tagging (the practice of implementing a piece of code into a page’s source, to analytics tools to connect to the server) is a fairly time-consuming process because developers have to create, test and deploy tags. The slowness in the process is further undermined by the fact that the tags need to be redeployed to accommodate website changes. As you can imagine, tagging affects our work processes by undermining speed and productivity. Reexamining these processes and seeking out alternatives will not only make our work easier but will also benefit the clients as well because we can deliver services more efficiently.
Certain industries have developed several channels to measure how customers use their services, for example, marketing and banking. But is data still operating in silos? If so, then the value of the data is completely undermined by its isolated use. Data generates the most value for organisations when it synthesises with other information from different sources to give a complete business picture. Naturally, performing such a task is not easy. However, SAS analytics products are designed to integrate data from different sources, which makes the process easier. An excellent example is SAS 360 Intelligence, which is designed to give marketers a comprehensive view of customer actions.
Having your finger on the pulse of the industry is one of the most important duties a data analyst has. The industry is always changing with new technologies used to improve what analytics can do. In the past, analytics could only describe what is happening, but now it can even predict the future in the form of predictive analytics. AI is expected to change analytics even further thanks to machine learning and natural language processing, which will allow the tech to make decisions without the need for human input. As you can imagine, this will transform how professionals operate.
SAS consultants should always examine their data analysis strategy to make sure they are providing services with the most value possible. Adjusting a strategy includes changing practices or adopting the latest technology to meet client demands. Sometimes, changing practices and incorporating technology is the same, as is the case with on-demand analytics services. On-demand services are made possible thanks to cloud technology and customer interest in analytics services, as and when they need it. On-demand consulting allows consultants, like ours, to provide data analytics services to companies in different parts of the world, making analytics more accessible and convenient than ever before.