Concepts of debt, asset and leverage with big data

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In the world of finance, a company’s balance sheet is used to gauge the overall health of the business. While balance sheets can be complicated, they really boil down to a few key concepts: assets, revenue, and debt. It can be useful to apply these financial concepts to the world of big data to help companies more effectively manage their risks associated with data and technology.

When thinking about your big data, the concepts of debt, asset, and leverage can help you make the most of your assets without taking on more obligations than you can effectively pay off.

Data As Asset, Data As Debt

Virtually every company today runs on data. No matter the size, industry, or maturity, every company is working to capitalize on their data. The idea that data is a debt is less obvious, but understand your obligations in these terms is essential for effectively managing risk.

Most companies distinguish between two primary categories of data: the data in their applications, like SAP, and the data in their analytical systems, like Teradata. The data that is “born” in applications must be moved into these analytical systems in a complex process that involves numerous technologies and IT resources. This effort has a cost, and anytime new application data is created, a company is taking on a form of debt.

For decades the fundamental model for storing and interacting with data was the same in both environments - the relational model. Because of this similarity, a consistent set of tools, best practices, security, and skills could be applied throughout the end-to-end process. IT teams became well versed in moving data into analytical systems, and these costs became predictable.

Today the world of application data has changed dramatically. New applications are delivered using third party SaaS products, or built on non-relational products like Amazon S3, MongoDB, Elasticsearch, and others. These technologies are the right approach for many applications. But all of these technologies are non-relational, and moving their data into analytical systems incurs more cost than traditional applications. In some cases, dramatically more cost, both in terms of effort as well as time.

How Leveraged Is Your Data?

Most companies choose the technologies for their applications without considering their long term obligations related to data analytics. They incorrectly assume that all data is more or less equal in this regard, when the truth is that their costs varies dramatically from application to application.

The differences, for example, between moving data from an application built on SQL Server and the effort to move data from Elasticsearch are immense. If you don’t plan for your costs effectively, you can end up with surprises, and in finance surprise is rarely a good thing.

Companies need to plan for their end-to-end data costs effectively. In the world of big data, costs can spiral out of control, and as a result companies are unable to capitalize on the untapped value of their application data. Instead, companies can adopt a balanced approach to building new applications, where they understand the costs of moving data into their analytical systems, and they are able to make the right trade-offs to grow their business quickly and efficiently.

All forms of debt carry risk. Smart companies manage their risks effectively, including those associated with data and technology. When it comes to data, most companies today make trade-offs that they do not fully understand, obligating themselves to forms of debt they don’t plan for and don’t effectively manage.

The costs associated with big data debt can be surprisingly high. As with any area of technology, where there are high costs there is opportunity for innovation. We believe that the next major advances in the data technology space will come from effectively aligning the data generated from a diverse application portfolio with the tools used by analysts and data scientists.

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