All the various “V”s still apply – though we believe value is the essential big idea about big data. But the six “S”s described below offer a compelling foundation for Big Data success.
Embrace the 6 “S”s for Big Data Success
No matter what your customer intelligence goals are, big data and BI environments must be designed to be the following six things:
The investments most likely to pay off in the big data space are those aligned to clear business goals and performance improvements in specific areas. To name just a few of the high-priority areas where big data investments can pay off, these can include:
- Marketing campaigns
- Customer experience
- Operational and process efficiency
Much of the payoff comes through the mixing, combining and contrasting different types of data. Data streams must be effectively integrated, and typically that means using data management platforms effectively.
Of course, infrastructures must be solid and stable and offer sufficient computing capacity to deal with large volumes of data. But there is another element to stability that is worth considering. BI and big data teams should remain focused on solving for specific use cases and fulfilling a defined business case before changing direction or jumping on to the next thing.
In our experience, “shiny object” syndrome leads to distractions or an excessive focus on technical or implementation details rather than fully seizing the business value at hand or achieving the big-picture business objective (see S #1: Strategic).
Much like stability, scalability is critical because the data volumes are massive and users need to easily access and interact with data. In this sense, infrastructure performance is an important technical component of big data or analytics success.
As big as data volumes are today, they will undoubtedly be bigger tomorrow. Likely the growth will be exponential, so environments and infrastructures must be ready.
We’re talking in terms of empowering users with easy-to-use tools that help them do their job and interact with vast data sets, rather than getting overwhelmed by complexity.
Having the right people, skills and teams may be the Big Data best practice. But, according to a 2014 IDG Enterprise survey of 750 IT decision makers, 40% of big data projects are challenged by a skills shortage.