The path to big data
Big data and the analysis techniques it is used with have become major buzzwords that have led to an extensive hype surrounding cognitive computing. Cloud providers make big promises which suggest that booking an appropriate cloud service or procuring special database management and analysis systems are all that’s needed to be able to handle big data. The Industrial Analytics workstream came to a different conclusion, however.
In order to be able to analyze large amounts of data and then make decisions on the basis of such analyses, companies first need to develop an architecture that makes data available in a manner that actually enables it to be used for modern analytic techniques, such as predictive analytics. The members of the workstream led by Dr. Alexander Hildenbrand summarized their path to big data in a Big Data Guide, which does away with numerous simplifying assumptions.
The guide presents business scenarios and use cases. It also offers an overview of the most important elements of big data and – perhaps the most useful aspect here – a very specific guideline that makes it possible to successfully design big data and advanced analytics processes. The guide supports users by providing them with a big data maturity model that helps them successfully introduce big data systems and implement big data projects.
The Big Data Guide is divided into five sections for Strategy & Roadmap, Governance, Reference Architecture, Infrastructure, and Development, Testing, and Maintenance, each of which is then subdivided into five maturity levels: Ad-Hoc, Repeatable, Defined, Managed, and Optimized. “If an organization categorizes itself as ‘defined’ because, for example, it believes that its big data strategy is fully developed, when in reality the organization is only just beginning to formulate a strategy, then that organization is going to run into big problems,” Hildenbrand explains.
“In such a situation, no assessment can be made as to whether specific use cases fit the strategy, or whether the technology employed is suited to the roadmap, the architecture, and the company’s goals. This will lead to miscalculations and the duplication of effort in the best case, and the cancellation or abandonment of projects in the worst.”
Dr. Alexander Hildenbrand
Big data needs to be trusted.
The Big Data Guide also includes a reference architecture that defines the following:
- Technology decision making approaches
- Product decision making approaches
- Security principles
- Rules for data integration
- Responsibilities, structure of committees, councils, etc.
- Management and control structures; processes
The Big Data Guide addresses five dimensions and contains 13 modules relating to big data and analytics.
The dimensions are as follows: Strategy, Technology, IT Processes and Policies, Security, and Compliance. Each dimension is divided into modules that can also be viewed as work packages.
Hildenbrand stresses the fact that despite the systematic approach described in the Big Data Guide, it’s not always possible to say with certainty whether the use of big data will pay off for a company:
“Big data needs to be trusted. We can predict with relative certainty whether a big data analysis will lead to new knowledge.
However, this knowledge will not pay off for a company unless it is used effectively by sales and marketing teams, for example , or in other departments, and this use leads to higher revenues, lower costs, or a shorter time to market.”
“How companies can begin using big data and analytics in a structured manner” Article on the CBA Lab’s Big Data Guide in the online edition of the magazine Computerwoche