The approach to data analysis determines the quality of findings and insight. While technology and expertise are important, there are only two-thirds of the equation. The biggest determining factor is the approach. How do organisations use technology and expertise to accomplish business objectives? Putting together the right strategic initiatives can make the biggest difference for both small and big businesses. Failure to plan properly hurts businesses because they could spend huge sums with little ROI.
What do we mean by a strategic approach to data analysis? It means setting the right business objectives so that selected technical solutions take the team one step closer to accomplishing these goals. Several companies invest in data analytics platforms (and spend thousands of dollars) to completely transform the way their business works.
In such cases, organisations always end up falling short because analytics cannot live up to these transformative expectations. Unfortunately, data analytics was not developed to make sweeping changes across an organisation overnight. What it is built to do is solve specific problems and accomplish objectives using the data generated by the organisation.
To resolve the situation, business leaders and data teams need to agree upon a common objective.
Once this objective is set, data teams can optimise data environments and analytics to generate the necessary insights. Focusing on a specific objective is an important strategic approach to data analysis because it brings data teams and business people together in mutual understanding, instead of leaving them on opposite ends of the understanding spectrum (an all too common occurrence in business).
Furthermore, it gives data analysts a solid idea of what business leaders are looking for, making it easier to map out the data analysis process and ensure each step takes the team one step closer to completing business aims. Data teams have an easier time deciding what to measure, selecting the right measuring techniques and analysis methods. It also becomes easier to achieve the transformative aspirations of the upper echelons of leadership over time.
The best approach to data analysis is maintaining a balance between centralised and decentralised practices. Over-centralising hurts productivity and efficiency, but no centralisation hurts focus. Maintaining a balance between independence and accountability can be challenging, but there are practices to aid, in this regard.
Automation tools can help with data analysis by removing mundane, unnecessary tasks from the data team’s plate, giving them the independence to be more productive. Documentation is an excellent example because data scientists need to document their work to better understand work processes. However, it is a time-consuming task, especially when the engineer has to document every new table or idea.
Automated systems make the entire process easier by creating documents on behalf of the data scientist. Data analysts still need to state the specifics of the new tables, but a large part of the documentation process is out of their hands. This means less time spent in bureaucracy and more time spent on idea generation.
Another option is to set up centralised libraries for common metrics. Be it healthcare or finance, there will be common metrics across several industries. However, there is a problem when data scientists teams create a different methodology to measure the same number.
This not only creates needless work and duplication of processes, it hurts decision-making because different methods can generate different results for the same metric. Data scientists will benefit tremendously from the use of these libraries because they can pull most of their metrics from already established libraries, while still adding additional metrics based on the client’s needs.
Choosing the best approach to data analysis is like chartering a course on a ship. The captain (business leader) can have a compass (tools) and the right crew (data science team), but all of it is for nought without the right systems and objectives in place. Hence, business leaders need to get the process right. The right process can make the difference between timely ROI and no results after years of spending. Hence, it’s important to consider the best approach to data analysis.
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.