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Digital banking has become one of the most important investments for financial institutions today. From the onset, it seems simple enough to put in place the right tools, gather data, and then use that data to make decisions. Yet, like with anything else, you get what you put into it. In short, if you do not have high-quality, clean data to build a foundation from, you cannot achieve the best possible outcome. For those looking to utilize artificial intelligence, for example, to achieve the most bottom-line benefit from digital banking, it is critical to build a strong foundation of clean data.
A good place to start is understanding the type of data you are already collecting. Every financial institution is gathering data. That data can come from various sources including transaction data, the demographic makeup of your customers, the products they are using, and so on. A step further is to begin collecting data and insights from social media – what are your customers saying and doing through social media connections? This data is basic but important to collect. But, is it all you need?
Also important is to look at the type of data you are collecting. All of the data listed here is valuable and worth investment of time into, but you also need to dig deeper to collect and analyze data that provides specific focus based on your organization’s goals and needs. For example, if the organization’s goal is to develop towards artificial intelligence implementation, you need to have data that provides key information such as consumer beliefs as well as geolocational data. You need information on the behaviors of your clients. That’s a bit more in-depth but highly valuable. Take the time to determine – is the quality of your data truly enough to create a workable solution?
No matter who you partner with or the technology available to you at your fingertips, you cannot get the very best outcome unless you feed in the highest quality of data.
A key component of the process of building towards a digital transformation is not only gathering this type of data but utilizing it in the best possible way. That sounds simple, and it can be from the perspective of the financial institution. Data cleansing or cleaning is the process of gathering all of that raw data and to accomplish two key things. First, it is important to ensure all necessary types of data are within reach (is everything possible available?) Second, it is important to ensure that the most valuable and accessible data is utilized in the analysis process.
In this process, it allows for us to answer some key questions such as:
With this insight available, we can then transform that data into usable information. Getting to this point – gathering the best data and cleansing it – is often the ideal sweet spot. From here, the right tools can take this data and use it to create highly effective and very important results. Transforming this data from a mound of information that looks useless into insights your organization can use it quite critical, in fact.
Remember the goal here is highly beneficial to any financial industry company moving to digital banking. With this data, we can then create many insights related to where your bank should invest, how it can avoid risk, how fraud detection can occur, and how your organization should better reach your customer base. In short, this high-quality data, sourced properly and cleansed properly, becomes quite invaluable to your business’s long-term goals. But, that end result is only as good as the information you put into the process. Understanding this can ultimately enable artificial intelligence efforts to thrive.
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Theo Epstein, named one of the world's greatest leaders by Fortune Magazine and 100 most ...