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Getting the business of Big Data ‘right’

Comments from Big Community Content Manager:

Big Data Analytics is no longer just a buzz word, but very much a serious business term with implications that could make or break an enterprise looking to adopt the technology. It isn’t an adoption that is decided by an IT technician or the IT department. Rather it is a business solution with ramifications that run business wide. 

It’s hard to follow the tech industry press and not see stories of companies reporting trouble obtaining value from their Big Data investments. McKinsey notably reported just this in December of last year in an update to their well-regarded 2011 Big Data report.

Since 2011, the pool of skilled people has grown, many companies have developed data science departments, and vendors have created ways for less skilled analysts to interrogate Big Data. Despite all of this, as we all have read and perhaps seen in person, companies struggle.

On the technical side, it’s been widely reported that complexity is getting in the way of business value. So vendors in both the open source and commercial worlds are working hard to streamline the analytics process and maximize the productivity of human resources that drive Big Data projects.

On the business side, vendors are working equally hard to paint visions of high value business outcomes enabled with Big Data technologies. They focus on business challenges which could be addressed if analytics were done differently than they are today. Also in spite of these efforts, struggles continue.

Resolving these dual problems requires business people, IT and analytics pros come together and recognize the peculiarities of the Big Data market.

The difference with Big Data

Line of business leader involvement in Big Data projects is considered today to be the best practice to achieving meaningful business value. If this describes you, then you may immediately be confused about what Big Data represents. You probably don’t care about the “how.”

For you, first recognize that Big Data Analytics is about doing something better than you do today. Such as allocating your marketing budget, deciding how to personalize or orchestrate your marketing, or planning the demand for your products or services. These are activities you do today, so you have an existing frame of reference when being approached with a Big Data use case.

If you work in IT or data science, it’s important to appreciate this perspective. The work streams that businesspeople have developed around the way they measure and drive their processes tend to be familiar and comfortable. This is why almost no completely IT-led Big Data strategies work. Whether it’s an application, a BI tool or third party service, businesspeople need to understand how a Big Data opportunity adds value.

Chief among these are the concepts of data experiments, and test and learn approaches used by Big Data leaders to reap the greatest rewards. Iteration is part of the Big Data value game, which is not easy to appreciate or understand when you are accustomed to a fixed insight, single data point or application’s output.

Platform versus everything else

Simply stated, a Big Data technology solution is one which accounts for the management of many varieties of data sources and also addresses steps leading to analytic model development, end user analysis, and deployment to operational systems. It’s not an application with analytics pre-defined by its creators. It’s not a service where you “pay by the drink” for untimely or incomplete insights. It’s not a BI tool.

Rather, it’s a platform which enables one or many use cases freed of the usual constraints of an application or service. The constraints could be the data employed or the analytics developed. With capabilities accounting for the complete analytics lifecycle available as cloud services, platforms allow businesspeople to think about near term projects, not ploddingly slow IT projects.

In addition to attacking the metrics of today, leaders are moving to leverage platforms to monetize data by creating literal “data products” which have financial value. This is the ultimate benefit of a platform, which can accommodate any data source and allow the development of analytics without any pre-determination.

Making your appeal

If you work in IT or data science and identify with this article, I suggested you engage with someone in the business looking to advance in their careers. If you are a business manager, take this article to heart and think about questions you cannot answer today or would like to.

Data driven decision making is not just the future, it’s the “here and now” for the best and brightest managers in both business and IT roles. Those in the most demand and who earn the most will understand how to capture the business value of Big Data Analytics.

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