In the age of big data, the world’s most successful companies will be data-driven enterprises. That is to say, data will be hardwired into their decision-making through automated processes that enable them to move with great speed and agility. To reach this level of performance, however, many businesses will need to overcome three significant challenges, which in many cases are now holding them back.

The first issue to overcome is to understanding what big data technologies can accomplish and then confidently selecting a solution. Immediately following this challenge comes the large constraint of budgetary pressures. Data volumes may be increasing exponentially but corporate spending on technology is heading in the opposite direction. IT managers are expected to do more with less, often while coping with systems built a decade or more ago that are now reaching the end of their lives. As a result, many enterprises are fast approaching inflexion points beyond which performance will suffer.

The third problem is that most businesses might not realize the breadth of the big data opportunity. They have a rigid approach to new ventures that is based on building a business use case and testing it for potential return on investment. When it comes to big data, however, there are an almost infinite number of possible use cases, many of which are unanticipated. Testing use cases for big data is like being asked to find a needle in a haystack when you’re not sure what a needle looks like – or a haystack, for that matter.

How, then, can these challenges be resolved? Big data technologies offer a different model for managing, maintaining and mobilizing data. Implementing them provides an opportunity to look at the workload currently managed by those traditional – and possibly crumbling – environments in order to find ways to reach better outcomes more quickly and cost-effectively.

Getting a Bigger Picture

Defining what the business outcomes might look like requires taking a step back from testing for value at too granular a level. The new operating model will focus on building the bigger picture – how a large universe of big data types might combine to deliver far more value than the sum of its parts.

For organizations that find this scary, it’s worth noting that cloud-based technologies already make a more experimental approach to IT affordable. One reason many businesses insist on such exhaustive analysis of new investments in technology is that they have traditionally cost millions. In a cloud environment, a pilot study could cost a few thousand dollars.

As big data comes of age, companies that can make these process and technological leaps have an exciting opportunity  to move beyond a framework where information is organized into narrow silos. Instead, this is the moment to begin building a corporate architecture where technology is part of the fabric of the organization, with data that is available to everyone driving innovation and growth.

To move toward that goal, it may be valuable to think about big data at a thematic level – the different types of opportunity that the new technologies might offer. Here are just five:

* Data as a platform: Many businesses recognize that big data technologies have the potential to both improve their competitiveness and to reduce their overall technology costs. The platform concept is an important idea to grasp: It’s about how businesses can utilize data technologies that are scalable, flexible, high-quality and easily accessible on a company-wide basis.

* Rapid deployment of data for analysis: We are now at a stage where tremendous opportunities are available to use data more quickly, with technologies that ingest, organize and analyze data as it comes in. That has an immediately transformative effect on the business’s speed of response and agility. For some time now, data and analytics have been considered real differentiators for businesses. Speed will now be an equally important differentiator.

* Disparate external data shaping context: The business should be able to take a view of its customers and what they’re doing elsewhere, most typically on social media. By combining, in real time, this view with the data that the business already has about its customers, it becomes possible to pursue much more meaningful interactions with them (for example, by making offers that are far more appropriate and valuable in the context of the experience they are currently having).

* Deriving unique value by combining internal data: New technologies enable businesses to combine data across a range of different platforms or departments, or to combine data that it has never been possible to combine before. Doing so not only produces new insights about the business and its customers – all from data already being collected – but also represents a more cost-effective method of working on such information, with less reliance, for example, on data warehousing.

* Discovery as a path to insight: One challenge for businesses will be to work out what to do with the vast array of data they are now amassing. By building new frameworks that use technologies such as machine learning tools, businesses can leverage their data more effectively with directed data discovery that identifies actionable insights in a quicker and more visually interactive manner.

With each of these themes, there are a multitude of examples of how different businesses might put big data to use, which underlines the limitations of approaching these technologies using a rigid, use case framework for testing the advantageousness of investment.

That is not to say that businesses should forget all about value when it comes to big data; rather, an approach that begins with this test in mind may produce a much shorter list of ideas about how to leverage the technologies on offer.

By contrast, the big picture approach will set the scene for what might be described as a supply chain of innovation – a process that continually generates 10 or 20 new ideas for the business that might deliver better performance at lower cost. This is the moment to start thinking about where to prioritize investment, rather than limiting the ambition of the vision at the outset.

Narendra Mulani is the managing director of Accenture Analytics. He graduated from Bombay University in 1978 with a Bachelor of Commerce. He received an MBA in finance in 1982 and a Ph.D. in multivariate statistics in 1985, both from the University of Massachusetts. Prior to joining Accenture, Mulani ran his own consulting company. This column is courtesy of Information Management, a SourceMedia publication.

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