Sixty-four percent of organizations are already investing in or plan to invest in big data soon according to a recent Gartner report.* That equates to a huge number of individuals who now have to research how to embark on a big data deployment. The prevalence and benefits of big data analytics are undeniable, but there are some considerations to keep in mind before jumping in:
Big data projects reap the most benefits when they address specific business needs. Having a use case in mind will help determine what data you need to analyze – social, machine or transactional data. Gartner recommends researching use cases and success stories in other industries; why not get inspired by what's worked for others? Gartner analyst Doug Laney recently shared examples of big data at work in various industries: using big data analytics, the department store Macy's was able to adjust prices in near real time for 73 million items based on demand and inventory; Wal-Mart was able to optimize search results and increase web checkouts by 10 – 15%; and American Express used sophisticated predictive models to analyze historical transactions and forecast potential churn. Once you've identified the analytic need not met by "small" data analysis, you have the first green light for considering big data technology.
2) Use big data tools as complementary to – not a replacement for – your BI platforms
Traditional business intelligence platforms still have a place within your big data ecosystem. Regardless of the underlying big data infrastructure you choose – Hadoop, NoSQL, Teradata or other appliances – you still need an analytic engine like arcplan on top in order to interact with, visualize and distribute your insights. With dynamic visualizations and flexible reporting on an easy to use interface, the right BI platform delivers the last mile for your big data initiatives.
3) Don’t go it alone
Reaping the benefits of big data technology takes special expertise. Data scientists possess a combination of analytical and soft skills that make all the difference in understanding and interpreting big data outputs. The right team of experts will ensure that you're asking the right questions, extracting relevant insight and presenting data in a way that even non-technical folks can understand. As Tom Davenport put it in the Harvard Business Review, "God may have been the first to produce order out of chaos, but data scientists do it too, admittedly on a smaller scale." Ultimately, the success of your big data initiative will be determined by the value of the actionable insight you're able to extract from the data, and data scientists are essential to that equation.
Launching a big data project is a significant undertaking for any organization. It requires strategic planning, the right set of tools, and a qualified team to be successful. With these three components in place, your organization will be positioned to reap the benefits promised by big data analytics.
Other resources to check out:
- Big Data FAQs (a primer from arcplan)
- Big Data: The next frontier for innovation, competition, and productivity (McKinsey report)
- Managing Big Data (TDWI Best Practices report)
- 3 Big Data Approaches Based On Your Available Resources & Infrastructure (article from arcplan)
* Driving Value From Big Data Though Six Emerging Best Practices (October 2013)