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Redefining Data Strategy for Next Generation Apps
Today businesses are engaging with customers in a variety of ways, on variety of platforms to make products and services available in ‘Always ON’ mode, be it a B2C or B2B business. Customers and consumers alike are becoming more demanding in terms of the interface they use to interact with businesses – They want an intuitive, light-weight, seamless experience across devices, up-to-date and secure interface to deal with businesses. But that puts businesses in lot of pressure where they have to drive down operational costs, realize new incremental revenue sources to keep growing and at the same time understand customers better. This puts tremendous challenges in developing the next generation applications, especially on data strategies in terms of new data types, volume and variety of data, system in silos, multiple platforms etc.
However, there are three keys strategies that businesses are adopting to tackle these issues –
1. Migrating Proprietary systems to Enterprise open source platforms
The enterprise open source is able to bring good from both the worlds- Support, SLA and confidence from proprietary and agility, features, innovation from community open source. Not all systems can be moved to Enterprise open source for a variety of reasons like legacy applications, vendor support, migrations costs and efforts. But organisations today are willing to find that sweet spot to balance between the portfolio of systems with Proprietary and Enterprise Open Source solutions thereby achieving cost optimization and freeing up the budgets for Next Generation Applications. There are databases like EnterpriseDB Postgres, which are gaining popularity due to ease of switching by providing Compatibility Suits and Migrations Tools.
2. Data Source Consolidation
As businesses embark to understand their customers better, they would ideally like to derive intelligence out of their existing systems and data. However, as the systems are developed in silos – every system using different data source of their own and getting wisdom out of the underlying comprehensive data is becoming challenging. Data source consolidation is made possible by using Data Integration Tools like powerful Pentaho ETL.
3. Choosing Right Data Source for Right Workload
With plethora of options to store the data like RDBMS, NoSQL, Hadoop platforms, the choices are abundant and so is the confusion too. It’s quite usual to find organisations confused about the choices for data storage technologies. There are some specific criteria, however, that can be used as general guideline to choose the correct data source. If the workload requires ACID compliance, (as if MUST criteria), like banking, online transactions RDBMS is the way to go. If workload requires quick, online response, without worrying about consistency across tables, NoSQL suits best. If workload requires huge historical data processing in offline mode to build the models, Hadoop platforms work best.
With the help of these three key strategies, organizations are able to handle challenges posed by demanding Next Gen Application development. Are you one of those organizations?
– Kaustubh Patwardhan | Head – Business Development and Strategy, Singapore
Kaustubh (KP) is the Head of Business Development and Strategy at Ashnik. His role comprises of heading Strategic Partnerships, Channels Management and Business Development for ASEAN. With his expansive experience in IT, he plays a pivotal role in strategic initiatives undertaken by Ashnik. Apart from his usual responsibilities at Ashnik, he is passionate about photography, cricket and other sports. He is also an enthusiastic participant in poetic circles and plays.