Designing a data transformation that delivers value!
The importance of analytics is no more a hidden secret as most of the companies that are keen to transform and work on streamlining their operation know its importance. But you will be surprised to know that only 20% of the companies have been able to maximize their potential and have been able to successfully implement AA at scale.
Data has been on the list of priorities for CEOs for the last 10 years but the focus on data has shifted due to the expansion in the availability of data along with a decline in the processing and storage cost and increasing regulatory focus on models, governance, quality and much more.
Because of such type of changes in the world of data, nowadays most financial institutions are now involved in different types of transformational programs. These programs have been designed with the main objective of reshaping the business model while focusing on unlocking the power of data.
Keeping all these things in mind, it has become clear that there is a sheer need to design a data transformation that delivers value and this is what we are going to address in this blog post.
Follow this step by step process for better data transformation
Have a clear data strategy
This might sound like the most obvious thing to do while starting a data transformation process but only 30 percent of the banks have a proper data strategy in place. All the other banks have moved towards the difficult journey of coming up with a new data enterprise or building a data lake without having a proper data strategy in place and this is what makes things more difficult.
An ideal data transformation process should always begin with setting clear ambitions in terms of the value it expects to get after the transformation. While working on such type of ambition, organizations should always focus on the scale of improvement all the other firms have been able to achieve. So start by defining the guiding vision for your entire data transformation journey.
Transform the data strategy into tangible use case
If you wish everyone in the organization to stay aligned and committed to the data transformation process then you must identify use cases that can create some value for the firm. For doing so, you have to follow four basic steps.
In the first step, you have to break down the bigger picture of data strategy into main goals that you actually want to achieve. In the second step, you have to identify and make a list of use cases that can have the greatest potential impact. But make sure that those uses cases are aligned with the bigger corporate strategy.
In the third step, the firms need to start working on prioritizing the use cases and the final stage will be to simply mobilize the data capabilities.
Come up with innovative data architecture
For meeting the unique needs of different functions in your organization, you need to come up with innovative data architecture and this is one of the most important steps in the entire data transformation process. With this type of approach, you can harness data monetization opportunities as well.
If you are serious about avoiding any type of data swamp then you must choose an ideal approach towards data ingestion. All the successful banks out there have been able to build a data governance system within their architecture and you must do the same for getting expected results.
Never miss setting up robust data governance for maintaining data quality
Always thinking that technological issues are the real problem behind low data quality is a wrong approach. Upon proper analysis, you may discover that only 20 to 30 percent of the data quality issue was related to the system and the rest is a result of human errors. This is why robust data governance becomes necessary.
There are many financial firms that have successfully implemented a federal-style framework. In this type of framework, the data is grouped into 30 to 40 data domains like pricing data or geographic data. But it is not necessary for you to follow a similar structure since you can come up with your own data governance for maintaining data quality.
Mobilize the organization
The final step in the data transformation process will be to mobilize the whole organization. When you adopt a use-case driven approach, you automatically develop targeted data architecture and data governance
By mobilizing the organization right from the beginning, you can go through rapid implementations, create tangible business values, and even work on capability building.
If you want to avoid any type of issue during the data transformation process then it becomes necessary to use a strategic and planned approach as the journey of data transformation is not easy. But by using the above-mentioned 5 steps strategic approach you can streamline the entire data transformation process.