Like few other business information trends of recent years, “big data” is attracting the interest of people outside the IT department. In fact, many big data systems have been set up independently by business users and analytics teams without IT being involved or aware.
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One of the consequences of this scattered approach is that large demands will be placed on master data management (MDM) and data governance processes to ensure that big data environments are able to live up to overall expectations for providing business benefits and competitive advantages. But it’s very early days for big data MDM and governance initiatives.
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Nick Millman, who heads consulting company Accenture’s process and information management practice in the UK and Ireland, said most businesses are still in the early stages of implementing big data technologies or have yet to even start. But he does see a lot of interest in the big data phenomenon, particularly among corporate executives.
“Big data has captured attention at more senior levels on the business side of the organisation than previous buzzwords around business intelligence and analytics,” he said. “A lot of organisations are defining a strategy of what big data means to them, how they will extract value.”
That could be in the form of using the pools of both structured and unstructured data to help make better strategic decisions or doing more sophisticated analytics in marketing and service operations to improve reaction to customer behaviour, Millman said.
Because of the high interest levels, he added, IT departments should be aware of the implications that big data has for MDM strategy -- particularly as companies look to adopt more coordinated approaches to deploying and using big data tools.
“Big data by its very nature is enterprise data, not limited to one silo or organisation. It necessitates enterprise MDM rather than MDM for a specific function,” Millman said.
As a result, organisations that have effective data management processes “are going to find it easier to extract the most value from big data,” he said. “Those that don’t are going to have the light shone on data governance and data quality issues and will realise that they need to fix some of the fundamentals.”
MDM tools primed for big data
Mike Ferguson, managing director of analyst firm Intelligent Business Strategies, said businesses may find that MDM and data warehousing have in some way prepared them for the onslaught of big data -- to a point, at least. “The tools used to build MDM could also be used to build data warehousing and also be used to provide data for big data environments,” Ferguson said. “But they are very different kinds of data.”
For example, one of the biggest sources of big data is social media. Text data from social networks could offer businesses better insight into their products and what customers think of them, if analysed appropriately. Companies increasingly want to track Twitter, Facebook and other social media outlets to monitor the sentiment toward their brands, ensure they respond rapidly to negative feedback and harvest ideas for future product development.
But one of the challenges for big data MDM efforts will be creating coherence between data collected from social media and existing product and customer data repositories, Millman said.
“If you pick up a customer complaining about some aspect of your product or service on Twitter or Facebook,” he said, “then you will want to be able to get an accurate pinpoint on which product or service, and then make sure that it is routed correctly in the organisation.”
However, because the application of data analytics to social media is also at an early stage, analysts such as Millman have seen few actual examples of MDM processes being applied to this space.
Andy Hayler, president and CEO at analyst firm The Information Difference, said one positive aspect is that social media analytics offers a green field for MDM, unencumbered by earlier implementations.
“There will be a need to define some structure around how the company views social media, to do with customers,” Hayler said. “The problems will be to do with making sense of this unstructured data. Because there is no legacy, there is a chance to get this one right before [users] build their five or six different computing systems; this is the one thing we can start from scratch on.”