What are the objectives and challenges of data consultants? What does a data analyst really do? The job really is about being able to identify ways to increase performance or detect opportunities of growth; turning data into management information. It can be summarized into three primary areas:
- Data collecting and structuring
- Data exploration and analysis
- Reporting and counseling
Exploring data for better understanding and saving time
Once the data analysts get their structured data base they still need to dig into it. They possibly still have an Excel file with thousands of lines which need to be dealt with. At this step one of the biggest challenges is to conduct the right analysis with the relevant data and here a good data exploration “excels”. Exploration with reactive data visualization tools is the best way to save time.
Exploration allows you to understand data sets in its whole, visualization provides talkative insights which enables to target analysis instead of screening Excel files to find potential tendencies. Furthermore, this kind of visualization helps the analysts to get rid of prejudices. In other words, they reduce the risk of neglecting data which they believed to be meaningless. Analyses are much more relevant and will have a stronger impact on data-driven decision making later on.
Analyses of data can be conducted with business intelligence solutions from companies such as Qlik, Tableau, Microsoft, SAP, or others.
How to prepare a report and dashboard effectively?
Data consultants on the other hand act as a middle man. They are required to change raw data into clear and actionable information for businesses which often have no, or few, analytical skills. The product of this work is also known as “management information” or MI in short. To achieve this goal the easiest way to make everyone happy is to report with talkative and graphic dashboards. In that way, people who are no data experts would also be able to visualize preselected information which are of interest for them.
How detailed should the analyses be? One must think about the end-user, the business stakeholder. For example, let’s take an HR director who wishes to use data about his company’s workforce. First, they would provide their data to an analyst who then do their magic and get back to the director with nice data visualizations and editable dashboards but what then?
In many cases, this HR director would be pleased with the graphs and indicators they were provided with, but at some point, all this information will generate new ideas and raise questions. After getting through the first assessment they might ask for more indicators and insights. What happens next? Can they get into the analysis on their own in case they want to dig deeper into the data?
Sometimes business stakeholders are no data handling experts
We should be reminded that one of the main obstacles of data analysis is the lack of skills from business users. Therefore, our HR director won’t go further into any kind of analyses, whether they don’t have the skills or whether they don’t want to spend too much time on it. What they do is that they would often just come back to the data analyst who then has to create new dashboards again and again.
I estimate between 10 to 15 hours per week in businesses being wasted because of this gap between data analysts and business users. Dashboards are practical but not great to explore all of the data.
Without a holistic understanding of the data, operational decisions are more complicated to make and meanwhile resources might be used up with wasteful activities. Too much time is dedicated to manual data analysis and Q&A between data analyst and non-analyst instead of focusing on the consultation, which is where most of the value hides. There is a way to lower this gap and save time though by using dynamic data exploration tools which are easy to use. Then the exploration would become bilateral and the business users would grow to understand the data too. If that was the case, this HR director would be able to make good and timely decisions based on data and facts.
How to improve this?
You have to nurture a data tinkering culture even with the business stakeholders and upper management by making the information easily accessible. A more data-driven company, aware of the importance of data will ultimately perform better than the competition which only relies on manual reporting effort and/or gut feeling.
This article has been provided by Shankar Arul.
Further reading
- What’s the Difference between Data, Information, Knowledge and Wisdom?
- Knowledge Management System: The Art of Increasing Productivity
- Manuel Lima About the History of Human Knowledge [Video]
- Tricia Wang: How Human Data Should Complement Big Data [Video]
YouTube: What does a data analyst actually do? (Dave Elkington)
Photo credit: Egor Slizyak for Strelka Institute / Graphic provided by Shankar Arul
