In the early days of business analysis and underwriting, data was managed with simply a pen and paper and, of course, Excel spreadsheets. As technology has advanced, databases, warehouses, and data lakes have enabled information to be collected, stored, and managed electronically.
But as businesses have rapidly increased the volume, velocity, and variety of data they use to create value for customers, improve operating efficiency, or mitigate risks, the need for more Data Management capabilities has emerged. Businesses also need teams that can create the strong foundations needed to ensure data has the integrity required for use through its lifecycle. Today’s modern data platforms can handle petabytes of information using cutting-edge technologies with advanced machine learning capabilities, but only if the data used to create new insights has solid Data Management principles behind it.
Good Data Management coupled with innovative technology allows businesses to gain valuable insights into their customers and operations that would otherwise be impossible. Those that build the Data Management foundations into their product delivery process will in the longer term stay ahead of the competition.
Choosing the Right Approach to Data Management
All companies have data. But not all companies treat their data the same way. There are various Data Management frameworks to choose from, and each has its own benefits and drawbacks. However, many businesses don’t take the time to properly research and select the right Data Management approach for their needs and objectives.
Before deciding which approach is right for your business, it is worth considering how much data needs to be managed, and the types of data that will comprise most of your system. Is it structured (e.g., customer records) or unstructured (e.g., emails)? How much are you willing to invest in designing and executing your data strategy in terms of skills, technology, and process? The right approach also depends on your organization’s commitment (and culture) to become data-guided.
Given the numerous considerations, it is perhaps unsurprising that things can get extremely complicated without the correct plan, strategy, and management in place.
Start with the “Why”
Many organizations don’t think about creating a Data Management strategy until they are faced with large volumes of disorganized, unstructured data with no home or set route of analysis.
Understandably, planning a Data Management strategy can feel like an overwhelming task to take on.
That’s why I think the best way to create a Data Management strategy is to start with the why. Ask yourself why do I need to improve my customer data quality? Seek out the underlying problem that your business wants to solve (e.g., stop sending an invoice to an incorrect address, or delivering a shipment to the wrong location). Having more control and governance over your customer data will most likely reduce “defects” (aka wrong delivery addresses) by putting in place processes, skills, and technologies to better manage customer data across the enterprise. If you have a clear understanding of the problem you are trying to solve, it is easier to formulate a practical, use-case-driven strategy and obtain the needed buy-in, and potential funding, from leadership.
You’ll find that solving one business problem end to end, from source to consumption, will create the relatively compact pockets of value that others will begin to seek out and want to emulate or be part of. This is the groundwork where you begin to prove value to set yourself up for success in Data Management.
Create a Data Ecosystem
Data is the foundation of any modern business. It’s not just how much data you have, but what type of data and, more importantly, how you manage it. Data sources can quickly become disorganized –scattered among a number of different devices, applications, systems, and networks – all of which makes it impossible to know if the data you need is available, accessible, or actionable.
By considering your Data Management approach, you can help your businesses to consolidate and streamline these different pieces of information, creating meaningful data, and data processes, that can be used to improve a business through increasing income, decreasing cost, or managing risk.