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Breaking Down the Modern Data Warehouse

September 6, 2017 No Comments

Featured article by Chris Hallenbeck, global head of SAP’s Database & Data Management Go-to-Market & Innovation organization

There has been a lot of talk recently about digital transformation for businesses and all the advantages it brings. Indeed, if your business has been modified to better leverage digital technologies, or if yours is a digital-native business that never needed transforming, you might be skeptical about what role a legacy technology like a data warehouse can do for you.

However, the chances are that your business is using both repository for business data, along with a process for bringing that data together to support decision-making and other internal processes. Regardless of whether your environment is built on licensed software or open source, located on-premises or in the cloud, or data residing in Hadoop or MYSQL or NoSQL – you already have a data warehouse.

For many years, the data warehouse, with all its limitations – has been at the heart of most organizations’ efforts to make improvements in these areas. The data warehouse as we know it came of age at a time when memory was expensive, CPUs were scarce, and networks were, by today’s standards, excruciatingly slow. Due to those factors, the “traditional” data warehouse is, in many ways, a bad fit for the reality businesses face today. Consider basic infrastructure, as a solution developed to work within historical limitations can’t easily leverage today’s abundant processing and storage power via commodity hardware and the cloud.

However, data warehouses have come a long way. implemented correctly, a data warehouse can ensure data integrity and security, putting the whole organization on the same page and providing fast answers to complex questions. Now, a new generation of data warehouse is emerging that is architected to meet the demands of modern data infrastructures and the pace and complexity of business in the digital era.

So howdo today’s data warehouses create value? What opportunities is the data warehouse enabling businesses to take advantage of, and how are the data warehouses of today different from those of the past? The criteria for evaluating a data warehouse environment that is optimized for the present and future according to expert Krish Krishnan include:

* Gaining competitive advantage
* Reducing operational and financial risk
* Increasing revenue
* Optimizing core business efficiencies
* Analyzing and predicting trends and behaviors
* Managing brand presence, channels, and reputation
* Managing customer expectations proactively

The first step at creating a modern data warehouse is to understand some of the biggest current challenges for businesses today. This is described as “variety” in the famous “three v’s” of big data. Most data warehousing systems of the past were built on relational databases that were later updated to support other data types. With that heritage, organizations struggle to incorporate machine data, social data, and other data types—basically, anything that wasn’t generated by a transactional system for storage in a relational database—into their data warehouse environment.

Today’s new class of data warehouse solutions lets users bypass the time-consuming ETL process (extracting, transforming and loading data into the data warehouse). Meaning, it’s ready for the data as it is and saves time developing indexes, summarizing, and aggregating the data to eke out acceptable query performance.

Another closely related challenge is the timeframe required for getting a new data warehousing project up and running. A new project doesn’t have to involve creating or migrating an entire EDW. Often the organization just needs a data mart—a subset of the overall organizational dataset to support reporting and analysis for a particular group.

Under Armour, for example, has the capability now to completely transform their business with a modern data warehouse—addressing all the criteria Krishnan cites above, while adding a new one: successfully implementing whole new business models / lines of business—by deploying a real-time solution. Under Armour’s fully integrated in-memory environment enables the company to deliver products to retailers faster and stay on top of supply and demand as it changes by the hour.

Success like this demonstrates that the data warehouse is as relevant now as it ever was. However, it might be architected, and whether it relies on traditional enterprise technologies, big data technologies, or an emerging class of real-time technologies for digital business, the data warehouse has an important role to play both for businesses that were “born digital” and for those that are now taking their first steps towards digital transformation.

The “data warehouses of the future” are changing the game in a wholly different way compared to the data warehouses of day’s past. We are still in the early stages of the modern data warehousing era and it will certainly be interesting to see which time-honored assumptions of the space will fall next.

sydney 150x150 Breaking Down the Modern Data Warehouse

Chris Hallenbeck is the global head of SAP’s Database & Data Management Go-to-Market & Innovation organization. In this role, Chris works closely with customers to help them gain competitive advantage in the digital era, and collaborate with SAP’s development organization to ensure we are designing products and services that give our customers the technical agility to achieve their goals.

Chris joined SAP in 2012 as global head of HANA Solution Management and quickly rose to global head of Database & Data Management (D&DM) Solution Management. From there, he moved to Europe to lead SAP’s European and African (EMEA) Database & Data Management Sales and Centers of Excellence organizations before returning to his product roots.

Chris has more than twenty experience in business technology. Prior to joining SAP, he was CTO of ZoomSystems, a developer of machine to machine software for retailers. He was VP of Products for DemandTec, one of the first successful big data cloud companies (subsequently acquired by IBM) and lead for the global center of excellence for Siebel System’s order management division after they acquired OnLink Technologies, a start-up Chris helped to build.

Chris is a classic T shaped professional with passion and knowledge of most every discipline in software development, cloud delivery, go-to-market and sales, but anchored with great depth in enterprise data management go-to-market. He loves understanding what every team is doing and what make the people on each team successful and passionate, and then figuring out how to make the end-to-end organization run better, make products that customers love, and have fun along the journey.

 

DATA and ANALYTICS 

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