Five Things to Consider Before You Make Big Data Investments in 2013

Manufacturers are investing big in big data technologies to gain an edge in the new year, but there are many factors to consider.

The challenge of capturing the data lies in the speed with which the data moves, the range of data types, and the complexity of managing and processing the data in more meaningful ways that can lead to better decision-making and trend discoveries.

Data sets currently available within your organization that remain unexploited are a lost opportunity.

Todd Johnson is executive vice president and chief operating officer of Saama Te
Todd Johnson is executive vice president and chief operating officer of Saama Technologies, a leading pure-play business analytics services firm.

One of the top five areas of exploration for many CIOs in 2012 was big data. And it's no wonder.

With massive volumes of valuable unstructured data permeating the average organization on a daily basis, manufacturers are in an analytics arms race to help them leverage those untapped data sets for more meaningful business insights and enhanced decision-making capabilities -- something traditional analytics solutions of a pre-big data era simply can't do.

With so much at stake, investment in big data and big data analytics is expected to see enormous growth in the next few years. Gartner, for example, predicts that big data spend will double in the three years between 2012 and 2015.

Furthermore, International Data Corp. (IDC) projected last week that the worldwide big data technology and services market will grow at a 31.7% compound annual growth rate (CAGR) over the next three years -- about seven times the rate of the overall information and communication technology (ICT) market.

As a result, we expect an increasing number of CIOs to put big data at the top of their list of strategic initiatives this year.

To help them as they explore this rich new field, I have put together five things to consider when making their 2013 big data investments.

Five Things to Consider

1. Big data will create new opportunities to understand and manage things differently.

A leading MIT professor once said, “Most great revolutions in science are preceded by revolutions in measurement.”

Business analytics has given us a very clear window into measuring our customers, our business processes, our employees and our suppliers. Thanks to that, we now know what is working well within our organization and what is not.

Success with analytics technology has paved the way for the big data revolution, which promises unprecedented insights into business and provides opportunities to understand and manage things differently. But only those organizations that are comfortable with traditional business analytics will be successful with big data, due to the sheer size and scope of data they would have to manage and analyze. Getting your “foundational” business intelligence infrastructure in order is a key first step.

One of the many intriguing things about big data analysis is it often brings a view to your business that has been traditionally underrepresented in your decision making. Big data promises to bring disruptive change across all industry segments and give the companies an unfair advantage.

According to a McKinsey analysis, a retailer that can effectively derive the right insight from big data has the potential to increase its operating margin by more than 60%.

Discuss this Article 1

CyberH
on Jan 10, 2013

Todd, good article. With the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quick and simple. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery.

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