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How to explain big data in plain English
You may think everyone knows what big data is by now, but misconceptions remain. Get expert advice for discussing big data in plain terms with colleagues, customers, or any audience
Big data strategy: Must-haves
“The key enabling capabilities for big data derive from old-school, good data management practices of cleaning, storing, validating, and above all, using it to drive business value,” says Joshi of Everest Group.
Some must-haves for organizations seeking to leverage big data include:
- Data access. With more data sources holding valuable data than ever before, the big question is: Can you get to it? “Without the ability to access all the data available, organizations will never have the confidence of knowing whether they have a complete understanding of their customer and business,” Wright says.
- Leadership buy-in. “If the enterprise leadership does not believe in data analytics and doesn’t adopt and embrace it as part of the company’s culture, any effort to make big data usable will either fizzle out or will be in small pockets, leading to sub-optimal results and ROI,” Aggarwal says.
- Data quality. Organizations must be able to ensure that everyone is, at the very least, working with data that is standardized and accurate, Wright says.
- Data ownership. “Many enterprises have big data but do not leverage it for business, which is strange,” Joshi says. “Organization models may need to change so that there are designated owners of such big data initiatives who drive value for business and are held accountable for it.”
- Analytics tools and strategy. “Analytics that can model the data, make sense of the patterns that are occurring, and enable organizations to make educated decisions and predictions is the goal of big data,” Wright explains. There is no one system that magically enables big data. “You will need to deal with many separate tools, and you will sometimes – and sometimes quite regularly – have to try different combinations,” says Ruzicka. “Because big data is rather more a process than a magical tool, it is key to relentlessly iterate.”
- Data skills. “Having data engineers and data scientists in-house is increasingly important; however, sourcing the right external partners can be more efficient and cost-effective,” says Aggarwal.
[ Read our related article: The most important skill to look for in data scientists ]
Advice for beginning big data projects
If your organization is in the early part of its journey with big data, a key to success will be asking the right business questions. Many business leaders are eager to jump right into big data solutions or projects without a clear goal in mind. “The key question I use to engage business leaders is always ‘What questions are you trying to answer?’” says Rodoni.
A good way to delve into that is to consider three areas of potential value:
- How can the organization grow its top line?
- How can it optimize its bottom line?
- And what is the company’s overall mission?
IT and its stakeholders can begin to think about where big data might be brought to bear in each of those areas. “Then, the smallest, most accurate, most timely data sources within or outside of the enterprise have to be identified that can provide the data that can be transformed into information that is relevant for each of those decisions,” Ruzicka says.
Business leaders should understand that having more data from more sources is of little to no value without a plan for how the data will be used and a goal for what they want big data to accomplish, Wright points out. Do they want to predict customer behavior? Map manufacturing trends? Improve sales with better targeting and messaging? Make better hires?
Only then can they create a big data strategy – including people, process, and technology – to achieve those aims. “The most important factors aren’t technical; they’re strategic,” says Rafter.
[ How can automation free up more staff time for innovation? Get the free e-book: Managing IT with Automation. ]