How important is leveraging data in wealth and asset management companies?
“I think it is very important since it reveals a lot of essential information which is difficult to collect in any other way.
The use of data becomes especially relevant for monitoring performance and investment activity. These processes can be effectively carried out based on data and even better when big data is available for analysis. For financial services, especially those which rely heavily on predictions, multi-structure data and other such influential parameters can make the analysis difficult. However, a lot of progress has been made in the last few years which has vastly improved analysis strategies and technology. In many financial institutions, data is also successfully used for fraud detection.
For wealth and asset management companies, it can also help discover new investment opportunities. For example, this data can be used to analyze the behaviors and preferences of individuals, as well as potential investment opportunities. In addition, this collected data can be used for many projects to improve health care, public safety, and agriculture in developing countries.”
In your experience, which source of data yields the most actionable or useful data?
“All data is useful. It mainly depends on what the data is used for and how carefully it is handled. Combining internal with external data from different sources can help derive more accurate results. It is, however, important how reliable the collected data is.
The saying ‘garbage in, garbage out’ is extremely apt to the data collection and application process. The most crucial point at the beginning of every project is to clearly define the problem you’re looking to solve and the questions that should be answered to solve it. If the question or the formulation of the problem is unclear or even missing, several misunderstandings can occur. For example, inappropriate (external) data might be collected and interpretations of results might be misleading or ambiguous.”
What types of insights are you able to pull from an email’s post-campaign reports?
“Tons of data is collected during an email campaign. We can analyze when emails were sent, if they were opened or if the links in the emails were clicked. We can analyze the actions of the recipients, such as when they opened an email, where they clicked and how long the email was opened for.
From A/B testing campaign data, we can further analyze which kind of emails are more popular by testing things like where an email might need a change in the subject title.
In campaign workflows, we analyze the engagement of contacts throughout the campaign. If clients are tagging their emails, we can analyze which audience is engaging with which topic. Using all this information, we create personas, give recommendations for future email campaigns, enhance contact engagement and improve the styles of emails. If your email marketing campaigns are underperforming, consider creating personas, they’re a great asset!”
What obstacles might one encounter when attempting to pull insights from multiple data pools?
“Many obstacles can occur especially when combining different databases. One problem, for example, is when the data isn’t clean. By “clean” I mean it’s free of missing entries and typos. If the last name or email has a typo in it, the entry will not be recognizable, which can result in misleading campaign results. Similar problems occur when the data is not updated. The cleanliness of the data is important for accurate results.”
How might one overcome said obstacles?
“In many cases, there is no way around updating and preparing the data by hand, correcting typos and finding more information manually. This can be an arduous process in the Big Data world. If data is missing, data can be searched for or clients can be contacted directly to ask for the missing information. Sometimes, for example, the ‘others’ segment in an analysis is too large. In such cases, data analysts need additional information to break it down and produce a high-quality report.
To prevent typos, individuals shouldn’t fill out all fields of a form by hand. Instead, the form should offer drop-down menus for the users to choose from. Another best practice is to let new contacts repeat important information such as email addresses when registering/opting in.”
In your opinion, what will change about data management in the next 5-10 years?
“In my opinion, it can go in two directions. One which is more productive, where data is used to make our daily lives more comfortable. Apps based on big data take over daily tasks, helping with decision making, driving your car, and so on.
The other direction could be that data is misused and that people start to hide their information and avoid using data based applications in order to protect their privacy.
I’m inclined to lean into the first point since many people are not overly concerned with their privacy and are willing to give up privacy for security – or for data protection. They click on “Terms and conditions” without reading them – GPS signals trace where we are and we aren’t bothered by it.
However, to follow this more productive route of data management, the protection of data and the transparent use of the data, should be the most important concern of data analysts.”
If you were to recommend one topic to read up on for someone who’s trying to learn data management, what would it be?
“I would recommend the Coursera specialization course ‘Excel to MySQL: Analytic techniques for Business Specialization’ from Duke University (Audit only). It not only teaches you how to use data analytics tools but also how to manage a project and prepare clear presentations.”