3 Big Data Pitfalls To Avoid - InformationWeek

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3 Big Data Pitfalls To Avoid
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[email protected],
User Rank: Apprentice
2/5/2014 | 4:51:34 PM
Re: Resource drain
Laurianne, one of the ways to address this, and possibly the best way, is by engaging a professional Big Data services team. This can easily cost you less than doing the job in-house when you consider the direct, indirect, and opportunity costs by helping avoid failure to capture mission-critical data, and delayed Big Data implementation. Both can undercut your ability to compete effectively and make you vulnerable to competitors, which leads to missed opportunities, lost revenue, and higher churn. You're absolutely on the mark re: resource drain on your team, in that most IT departments and, more recently, established Data Science teams, are under pressure to maintain daily operations and incorporate new business-enhancing technologies, without increasing staff. By hiring a team of experts, you can keep your staff doing the things they need to do and avoid costs associated with deferring or delaying other tasks.

At Stratecast we have developed 4 questions to ask a Big Data provider to ensure a successful deployment:

1. What is the data strategy?

2. What will the supporting IT systems infrastructure be?

3. What measures are you taking to ensure data protection and compliance?

4. What components and characteristics are you implementing to tune the Big Data platform for optimal performance?

We analyzed many key constructs, models, business drivers, and competitive dynamics--including listings of more than 300 providers, grouped by their areas of expertise, and mapped into the functions they provide within those models--in Stratecast's report The World Moves Fast, and Data is Driving/part 2: Competitive Strategies (BDA 1-04, November 2013).

IW Pick
User Rank: Ninja
2/5/2014 | 4:04:50 PM
Applies to most other IT projects
I can tell you many IT projects suffer from the same pitfalls.  It seems business management can't get their heads around the necessary people resources to apply technology to the processes of the business.  Add a product or service sold by the business to customers and everything is lined up all well and good, but technology not so much.  Existing IT personnel are usually consided a sunk cost readily available for more and more types of projects however a product manager responsible for a product sold by the company is never considered readily available for more and more product types.  Okay, I can get off my horse now.
D. Henschen
D. Henschen,
User Rank: Author
2/5/2014 | 1:27:05 PM
Five "dos" to go with those "don'ts"
Okay, that covers a few things you want to avoid. Here are some "dos" based on a survey by Nemertes Research:

1. Have a separate big data budget. Nemertes finds that there's a strong correlation between having a dedicated big data budget and having success with big data. 60% of respondents say they have separate budgets and another 5% say they're headed in that direction. The remaining 35% do not have separate big data budgets, but those with separate budgets report about 20% higher success rates than those without separate budgets, says Nemertes.

2. Plan to manage the complete data lifecycle. Address everything from acquisition, classification, and management to analysis, visualization and end-of-life disposal, with security and compliance safeguards at every step along the way. The most successful big data practitioners address the complete lifecycle of big data. End-of-life planning is the step that big data practitioners most often ignore, but that can lead to bad decisions handled in a haphazard way.

3. Engage IT. Even if -- correction, particularly if -- you have a separate big data team, involving many people across IT makes good sense because big data initiatives are likely to touch on all parts of the IT infrastructure and organization -- data processing, database management, reporting, storage, networks, app development and so on.   

4. Engage the lines of business. Initial big data projects should have a tight focus and scope to help ensure success, but long-range big data planning and strategy development should cast a wide net across business areas. The more successful respondents in Nemertes' research more often engaged business leaders in big data decision making and guidance.

5. Embrace a shared spending model. The more successful big data practitioners report that their firms have higher levels of technology spending outside of IT's control. About 28% of tech spending is outside of the IT budget at companies reporting big data success whereas only about 4% of spending is handled outside of IT among firms self reporting as less-successful with big data. The non-IT spending is often tied to software-as-a-service applications in sales and marketing areas or infrastructure-as-a-service offerings that might be used for analytic exploration.

For more, read the complete article.
User Rank: Author
2/5/2014 | 12:06:04 PM
Resource drain
The resource drain concern is one I have heard repeatedly -- how do you organize your team to do the new big data project while keeping up with the existing BI needs. Anyone have advice to share here?

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