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

Practice Head and Senior Director – Information Management & Analytics, Virtusa. Arvind has more than 19 years of industry experience, with focus on planning and executing Data Management and Analytics initiatives. He has a comprehensive understanding of the IT industry best practices, technologies, architectures and emerging technologies and his role includes: Designing and overseeing implementation of end-to-end data management initiatives and delivering architectural initiatives that drive revenue and improve efficiency in line with business strategy including technology rationalization in line with emerging technologies. Prior to taking on this role, he was involved in architecting and designing Centers of Excellence (COEs) as well as service delivery functions focused on Information Management encompassing traditional Data Warehousing, Master Data Management and Analytical reporting. Arvind’s previous experience includes stints in organizations such as PwC, Oracle and Sanofi before joining Virtusa. Arvind is a prolific speaker and has represented various industry forums including UNICOM and Gartner BI events. He has also presented a number of webinars on HR Analytics. Arvind graduated from BITS, Pilani, and obtained his MBA from Georgia State University.

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