Big data requires companies to embrace innovation on two levels

Global technology research and consultancy Gartner says CIOs must be aware that innovation needs to go beyond the technology used to manage large data. To achieve maximum value, businesses need to seek and embrace innovation in the form of large data analysis of business issues.

"Big data requires companies to embrace innovation on two levels," says Hung Lehong, vice president of Gartner Research. First, the technology itself is innovative. Secondly, enterprises must be bold in making decision support and analysis. The second reason is not a technical challenge, but a process and change management challenge. Large data technologies bring innovative ways to analyze existing business problems and opportunities. New data sources and new analytics can be upgraded in ways that companies have never used before. ”

The ability of large data to analyze unstructured data from large and different sources can create opportunities for innovation. In most cases, there has been no priority for large data to add value to the enterprise. In the past, it was impossible to run or access these new data. The search for large data technology values requires innovative thinking and willingness to accept and trust these sources and methods. CIOs should view large data projects as innovative projects that require change management efforts. Businesses will need time to trust new data sources and new analytics, and businesses should start with small pilot projects that allow data, analytics and insight to be fully transparent.

However, big data is not just about big external data sources, such as public social network data. A creative CIO mindset can tap into valuable sources of information that exist within the enterprise and have been fully exploited.

"Perhaps CIOs are more inclined to start with internal data sources because most of the internal resources are already managed by it," says Mr Lehong. In many cases, however, these internal data sources are not controlled by it at all. For example, call center records, security camera clips, and operational data from manufacturing devices represent potential internal data sources for investigation, but they are usually not under it control. ”

As a result, CIOs and their teams will need to work with business units to fully understand the data available. With some creative ideas, even some of the data that has been obtained can become richer. Companies that use large data technologies can afford to buy complete raw data and create data sources that provide new insights. However, CIOs will need to make sure that they have clear business goals and results for storing new data.

Internal data has an additional advantage. Because companies already have data, it is a good starting point for launching large data projects and is easier and/or more cost-effective than accessing external data sources. In addition, companies will be more likely to trust internal data than external sources because it comes from its own systems, records, and other assets.

Some enterprises use large data technology to make existing analysis faster. While technology can achieve faster speeds, the business value gained at such speeds often requires a process change.

Gartner's research shows that companies that have implemented rapid analytics early in the process have changed their processes to maximize their profitability. For some companies, the analysis speed provides the ability to include a full week of sales data, such as price/promotion/reduction optimization, when running analysis. In the past, since these optimizations took a day to run, sales figures for Sunday were often not included in the calculations. Now that the optimization is done in minutes, the enterprise can include the entire week's data-so that its optimizations are synchronized with the campaign.

"CIOs must ensure that large data projects that improve the speed of analysis include process reengineering designed to derive maximum benefit from speed," says Mr Lehong. Before starting a big data investment, make sure that the assessment team has a good understanding of how quickly the analysis will lead to improvements in business results, and that it needs to be built into a business case. ”