5 technical capabilities required in modern enterprise data strategies
Creating a data strategy a decade ago was relatively easy compared to today. Back then, database experts debated the capabilities and performance of relational databases from Oracle, Microsoft and IBM, or whether to use open source databases like MySQL and PostgresSQL. A minority of enterprises explored NoSQL databases including document stores, key-value databases and columnar databases from technologies such as MarkLogic, MongoDB and Apache Cassandra. Organizations moving lots of data between enterprise systems invested in ETL (Extract, Transform and Load) platforms and a small minority invested in data quality or master data management solutions.
Flash forward to today and CIO recognize that data and information is the oil of the 21st century. Having diverse data management options, dependable dataops practices, proactive data governance, advanced analytics, citizen data science programs and maturing machine learning capabilities are all required to deliver competitive and differentiating business capabilities.
I attended the Strata Data Conference in New York last week to see where the new opportunities, trends and challenges lie in CIO creating and executing comprehensive data strategies.
Those challenges became abundantly clear right from the opening keynote where Cloudera’s CMO Mark Hollison cited recently published research conducted with Harvard Business Review. A key finding in the research is that “sixty-nine percent say their organizations need a comprehensive data strategy in order to meet its strategic goals over the next three years, yet only thirty-five percent say their organizations‘ analytics and data management capabilities are on course to meet those goals.”
That’s a sizable gap that illustrates the growing business expectations around data and analytics and the underlying implementation complexities. CIOs looking to close these gaps should consider the following five technical capabilities in their data strategies highlighted at the Strata Data Conference.
According to the same survey, fifty-one percent plan to leverage multiple clouds as part of their data strategy, and only twelve percent have more than seventy-five percent of their data on public clouds. The strategy of consolidating data into centralized data warehouses or data lakes appears to be dated, and the new reality is that CIOs have to be able to manage, integrate and share data stored in multiple public and private clouds.
The good news is that platforms such as Cloudera Data Platform, SAP Data Hub and InfoWorks DataFoundry are designed to help data organizations manage, integrate and govern access to data repositories stored in different big data engines and on different clouds.
I was able to speak to InfoWorks CIO, Buno Pati about working with data in a multi-cloud environment. He informed me, “Establishing a robust and agile foundation for enterprise data operations and orchestration is central to the success of any modern enterprise data strategy. These systems must empower enterprises to launch new analytic use cases rapidly, minimize dependence on highly-specialized talent and seamlessly traverse hybrid and multi-cloud environments with a variety of execution engines and storage systems, e.g. Hadoop, Spark and cloud infrastructure.”
CIOs could probably use a pocket dictionary to help define all the big data platforms that are growing in popularity. While Hadoop was the early winner in big data platforms, enterprises are investing in a mix of them today including Apache Spark, Apache Hive, Snowflake, multiple databases supported on AWS, Azure and Google Cloud Platform, and many others.
