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BW Businessworld

Making Big Data Something More Than The 'Next Big Thing'

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The world today is being flooded with digital data, in myriad manifestations and washing over us at such incredible speed that making sense of it is dauntingly difficult. Yet this tidal wave of data-when channeled and filtered by an array of new information technologies-holds untold value for organizations, whether they are small not-for- profits or Fortune 500 companies.

Estimates of the potential benefits of leveraging big data are indeed staggering: productivity-led savings worth $300 billion a year for the US healthcare industry and €250 billion for the European public sector, a 60 per cent potential increase in retailers' operating margins. And technology seems poised to deliver these benefits. One small example: data storage technology has advanced to the point that only $600 is all it takes to purchase storage space that can accommodate the entire world's music!

Some large companies have indeed used emerging technologies to extract significant value from big data. Visa recently announced that increasing from 40 to 200 the number of attributes it analyzes in each credit card transaction has saved 6 cents in every $100 worth of transactions.

But for most businesses, the promise of big data is nowhere close to being fulfilled. For one thing, spending on it is polarized. While the telecommunications, travel, retail, life sciences, and financial services industries are making significant strides in big data technologies, other industries, such as manufacturing and government, are in a wait-and-watch mode.

The lack of major big data initiatives across industries can be seen in the numbers from service providers. In 2012, the global top 20 big data players made less than 1 percent of their total revenues from big data. The total market for big data hardware, software, and services in 2012 was $11.5 billion, whereas the combined overall revenue of those 20 big data players was more than $1.2 trillion.

The disparity between a few success stories and the lack of action elsewhere has created a high level of anxiety within firms that have not yet begun to explore big data. But it is important that they not rush thoughtlessly into the fray. An organization should make a big data investment only if it has well-defined and realizable business objectives.

We offer here nine steps that companies can take to begin turning big data talk into action, buzz into business benefits.
 
Nine Steps To Big Data Value Creation
The barriers to extracting business value from big data can seem daunting. But they can be overcome through a systematic plan, one that breaks down the challenge into a series of nine sequential steps that will enable organizations to take advantage of this valuable and growing asset. We will consider each of these steps individually here.

Step 1: Define responsibilities.
Who collects, who analyzes, and who drives value? The onus of collecting data should be shared by the IT and analytics teams, but analysis must be the sole responsibility of analytics professionals. Similarly, only functional leaders-for example, the Chief Marketing Officer, the Chief Financial Officer, and the Chief Procurement Officer-should be responsible for identifying areas within their respective functions where big data could drive value.
 
Step 2: Get the business functions to ask the right questions.
Senior executives will have an easier time winning buy-in from business functions if they demonstrate how big data might be valuable to them. The ability to ask the right questions is key to succeeding with big data. It also pays to keep in mind that big data is not about data themselves; it is about using data to discover insights that can lead to valuable outcomes.

Step 3: Take stock of all data "worth analysing." Valuable business insight can come from many sources, including social media feeds, activity streams, and "dark data" (data that are currently unused but that have already been captured), machine instrumentation, and operational technology feeds. It is important to explore these sources and to experiment with new ways of capturing information, such as complex-event processing, video search, and text analytics.

Step 4: Select the business functions  best positioned to lead the way.
It is smart to launch big data initiatives in business functions that are most ready to collect and analyze data and for which the potential payback is high. Functions such as marketing, customer service, supply chain management, and finance are poised for maximum growth. If system readiness is not an issue, these are usually the right places to direct initial investments

Step 5: Match big data initiatives with compatible business functions.
Some big data programs can be implemented in a variety of settings, but most are suited to specific functions. For example:

o Customer functions (such as marketing, e-commerce, and customer service) can use big data for targeted advertising that provides personalized offers to consumers based on their socio-demographic characteristics.

o Finance functions (such as finance, risk, and treasury) can use big data for intraday liquidity management, providing real-time monitoring of price movements in relation to positions, to make
trading and rebalancing decisions, and for improved credit risk assessment, through multiple big data-
 
Step 6: Determine whether big data will yield valuable information  unavailable through traditional business analytics.
Making the business case for a big data initiative clearly will be easier if it can be shown that it creates new value. For instance, if a marketing department is currently segmenting customer profiles using standard demographic indicators, would there be additional benefit in analyzing attitudes and preferences (at a granular level) through text and speech analysis?

Step 7: Assess complexities  and prioritize accordingly.
All else being equal, an organization should begin its
big data experimentation with an initiative that is not too demanding. In assessing possibilities, it is helpful to keep in mind the complexity of both the type of data and the type of analysis the data will require.

Step 8: Assess your technology architecture.
An organisation's traditional information architecture may not accommodate massive, high-speed, variable data flows. Many traditional and even state-of-the- art technologies were not designed for today's or tomorrow's level of data volume, velocity, and variety. Even as datasets grow exponentially along those dimensions, the investments required for scaling technologies (such as processors, storage, database management systems, and analytics) to perform efficiently grow even faster. To counter these intractable economics, organizations need to consider a variety of methods to upgrade their infrastructure in support of or in anticipation of big data.

Step 9: Start building a team.
Big data initiatives require multidisciplinary teams of business and technology experts. Every team member- business analyst, programmer, data scientist, and data visualizer-will need to have cross-functional familiarity.

Big data and business analytics expertise should fall within existing functions-for  example, finance, human resources, and marketing-with the aim of furthering the strategic initiatives of those functions.

Conclusion
Big data analytics is not a passing fad. It will be a central means of creating value for the organization of tomorrow-and that is "tomorrow" almost literally. It represents a major change in the way that businesses and other organizations will operate and will require a new mind-set and new capabilities. Given that, many organizations are struggling to know where to start in becoming competent in the realm of big data. A step- by-step approach can make the transition seem less daunting and minimize the stumbles that are bound to occur along the way.


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