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How do you catch the next big wave? When it comes to big data, it’s not the wave everyone’s already riding, but the one on the horizon, rising with the potential to carry your organization to new revenue opportunities, closer customer relationships, and competitive advantage.
The next big wave is in-memory technologies, which accelerate data processing for faster, more accurate decisions. Become an expert on leveraging big data for real-time analytics and responsiveness, learn to tell the story of business benefits, and you could find enthusiastic support in the C suite: 47% of those surveyed by IDG stated that the CEO was the largest supporter of big data efforts.
Big data investments in 2013 continued to rise, with 64% of organizations investing or planning to invest in big data technology compared with 58% in 2012, according to a survey by Gartner. Organizations are spending that money mostly because they have to: the amount of data to be managed is growing exponentially, with an expected increase of 76% within the next 12-18 months. IT must figure out how to scale-out data storage and analytics capabilities or all systems could slow to a crawl.
But you don’t get to be an evangelist by arguing the case for keeping the lights on. To get noticed in the C-suite, you need to shift the focus from the threats of big data growth to the potential for immediate business benefits.
Go beyond making the case for more, bigger databases and storage racks. To truly leverage the value of big data—your organization’s largest asset—you’ll need to include rich new data sources in a large, in-memory database, implement real-time operational analytics to turn that data into insights, and put data and analytics platforms in place that help your organization use those insights to outperform the competition.
All this costs more than just expanding storage capacity. So when you make the case for investing, give your CEO some common use cases that can show the value of investing in big data for real-time operational analytics.
Big Data in Real-World Use Cases
In finance, real-time analysis of big data can make a huge difference in detecting and reducing fraud. For example, a credit card company with real-time analysis can use that second or so between the moment a cashier swipes a card through a reader to detect any anomalous activity or pick up any other signs of fraud, and reject suspicious transactions on the spot—saving the company money. For a bank, real-time analysis of big data is virtually the only way of ensuring that it is always in compliance with extensive regulations meant to govern the amount of risk the bank can take on at any time.
When a bank is considering making a new loan or investment it often has to move quickly to keep ahead of its competitors. Big data analysis makes fast work of evaluating a bank’s risk positions at that moment and reporting on whether or not it can bid for the new business.
In advertising, real-time analysis of big data is fueling the development of a new kind of business: real-time bidding (RTB), also known as programmatic ad buying. This dynamic auction process ingests and analyzes billions of data points in real time to produce fast and accurate bids and more targeted ads. Advertisers and publishers demand RTB to help them focus campaigns, monitor campaign response, make adjustments in real time, and increase conversions.
Leading digital media holding companies such as CPXi have leveraged big data in a consolidated tiered storage architecture that combines historical and real-time data and analyzes it with proprietary complex algorithms to provide highly accurate, up-to-the-minute data models that leverage all data for decision making and ad buying recommendations—in real time, instead of in batches, to take advantage of changes and opportunities as they arise. The result: Better ROI for its clients and an edge over the competition for CPXi.
And in retail, real-time recommendations drive up conversions, length of visit, and purchase amounts, while demand optimization helps cut losses and maximize the ability to take advantage of demand. McKinsey estimates that a retailer maximizing its use of big data could increase its operating margin by more than 60%.
Processing and analyzing a wealth of historical and right-now data helps companies identify the sweet spot of price and product, and ensure that they have enough inventory in the right places at the right times to optimize sales. For example, a retailer can use real-time analytics on huge volumes of data coming in from its website and stores to see that sales of orange clothing are trending down, and respond with immediate discounting to ensure it’s not left with unsold inventory.
Even if the objective is not new revenues, harnessing big data can yield impressive returns through savings. McKinsey estimates that if US healthcare were to leverage big data, the sector could create more than $300 billion in value every year. In the developed economies of Europe, big data could save administrators more than €100 billion in operational efficiency improvements… and even more by using it to reduce fraud and errors and increase the efficiency of tax collection. 
Ready, Aim, Pitch
When your pitch includes new revenue opportunities, improved customer engagement and loyalty, increased sales, and competitive advantage, you’re virtually guaranteed close attention from the C-suite. This is an investment that pays off, and you can prove it with the latest analyst reports, such as one from McKinsey that estimates that companies that inject data and analytics deep into their operations can deliver productivity and profit gains that are 5% to 6% higher than those of the competition.
But make sure your executives understand that having the right analytics infrastructure is the difference between merely dealing with the challenges of big data and leveraging it to seize opportunities. So don’t just pitch the need for more servers and storage capacity. Make a case for real-time analytics with the in-memory database, analytics capability, and tools you need to support real-time leverage of all data assets to deliver value to the business.