It pays to modernize your data architecture

In today’s world where data is collected at every interaction, be it over the phone, mobile, PC, sensors, with or without us knowing, it becomes important to have a strategy around data. Traditionally, data has been seen as something to “run the business,” but, in today’s context, it can actually “be the business” if monetized well. An example of Internet of Things (IoT) data in a connect-globecustomer context is the wristband one wears at amusement parks that provides real-time data about customer interaction at all times, and this data can be processed in near real time to push out relevant offers and alerts to enhance the customer experience. The question is: How do organizations prepare themselves to take advantage of data?

The key lies in building a modern data architecture that is open, flexible and scalable, something that can accommodate your existing data assets as well as potential new ones. Before we talk about specific steps to modernize data architecture, let’s look at typical challenges:

  1. Many applications within the organization have been around for 20 or more years. While the usage for some of them is known, it is still not clear who is leveraging the data in each application and for what purpose. How do we find out?
  2. To meet their reporting needs, organizations have built multiple data assets including data warehouses and data marts. Additionally, they have power users collating data from multiple sources and creating reports using MS Excel. Numbers are inconsistent and vary based on who is preparing them and the intended purpose.
  3. Organizations have multiple applications and data assets starting with mainframe-based ones, client-server, Web applications and some newer cloud-based applications, all co-existing. They struggle to find the right people to support the applications, especially the older ones.
  4. Organizations are aware of the new developments in the big data space including NoSQL databases and the Hadoop ecosystem, and have typically embarked on some initiatives to get started on this. The main challenge is around integrating this with the traditional data warehouse technologies.
  5. People, and by extension, their skills, are the biggest assets of any organization. CIOs are concerned about having to find an army of programmers for populating Hadoop-based data repositories. The other big concern is how to leverage existing SQL skills, which people have acquired over the years.

These are valid concerns, and some are more applicable than others based on the context. Nonetheless, given the inevitable need to be able to better monetize data and modernize technology platforms, it is important to have a strategy. I recommend the following approach:

  1. Data asset inventory: Create a complete list of data assets – legacy, data warehouses, data marts, data islands. Identify the data flows between these assets and the usage patterns. It might be particularly hard for some legacy systems, but this serves as the starting point for any consolidation and modernization.
  2. Data asset rationalization: Based on the list of data assets and the usage, it is important to rationalize them. What this means is to identify if the same data is coming from multiple applications, and if so, which is the authoritative source, and which ones can be retired. This is a very important exercise and can help consolidate the number of data assets to a manageable few. In this context, master data management is critical to ensure you have good quality data.
  3. Data lineage: Undertaking a data lineage exercise to identify data flows – creating detailed documentation especially for the legacy applications – is a must. This greatly reduces the risk of dependency on key personnel and also makes it easier to migrate to a future state architecture.
  4. Data infrastructure: Have a big data and cloud strategy in place to bring in newer technologies in a pilot mode. Start with a non-legacy application to understand the technology, and move applications over in conjunction with data asset rationalization. The “data on cloud” is going to be an important component of modern architecture especially when dealing with IoT data.
  5. Data technology: It pays to understand the different options available in a very crowded and rapidly evolving marketplace, and to select the right technologies that fit into your architecture from a technology standpoint as well as a people standpoint. For example, using a data integration tool with big data connectors will eliminate the need for people who can write MapReduce code.

Creating a holistic data strategy in light of changes in the business, and taking a structured approach, will definitely help lay a solid foundation that will be the basis for monetizing data.


The article was originally published in Analytics Magazine on August 27, 2015 and is re-posted here by permission.

Arvind Purushothaman

Arvind Purushothaman leads the Data & Analytics practice at Virtusa. He has over 22 years of experience in this space, and focuses on Data consulting, Data Engineering, Analytics and AI/ML.

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