Essential Guide

Guide to big data analytics tools, trends and best practices

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Five first steps to creating an effective 'big data' analytics program

Choosing the right technology is only half the startup battle on "big data" analytics. Get a list of deployment tips from consultant Lyndsay Wise to help set your organization on the right path.

Organizations embarking on “big data” analytics programs require a strong implementation plan to make sure that

the analytics process works for them. Choosing the technology that will be used is only half the battle when preparing for a big data initiative. Once a company identifies the right database software and analytics tools and begins to put the technology infrastructure in place, it’s ready to move to the next level and develop a real strategy for success.

The importance of effective project management processes to creating a successful big data analytics program also cannot be overstated. The following five tips offer advice on steps that businesses should take to help ensure a smooth deployment:

Focusing on a project’s business goals in the planning stages can help an organization hone in on the exact analytics that are required.

Decide what data to include and what to leave out. By their very nature, big data analytics projects involve large data sets. But that doesn’t mean all of a company’s data sources, or all of the information within a relevant data source, will need to be analyzed. Organizations need to identify the strategic data that will lead to valuable analytical insights. For instance, what combination of information can help pinpoint key customer-retention factors? Or what data is required to uncover hidden patterns in stock market transactions? Focusing on a project’s business goals in the planning stages can help an organization hone in on the exact analytics that are required, after which it can -- and should -- look at the data needed to meet those business goals. In some cases, that indeed will mean including everything. In others, though, it means using only a subset of the big data on hand.

Build effective business rules and then work through the complexity they create. Coping with complexity is the key aspect of most big data analytics initiatives. In order to get the right analytical outputs, it’s essential to include business-focused data owners in the process to make sure that all of the necessary business rules are identified in advance. Once the rules are documented, technical staffers can assess how much complexity they create and the work required to turn the data inputs into relevant and valuable findings. That leads into the next phase of the implementation, as discussed below.

Translate business rules into relevant analytics in a collaborative fashion. Business rules are just the first step in developing effective big data analytics applications. Next, IT or analytics professionals need to create the analytical queries and algorithms required to generate the desired outputs. But that shouldn’t be done in a vacuum. The better and more accurate that queries are in the first place, the less redevelopment will be required. Many projects require continual reiterations due to a lack of communication between the project team and business departments. Ongoing communication and collaboration leads to a much smoother analytics development process.

Have a maintenance plan. In addition to the initial development work, a successful big data analytics initiative requires ongoing attention and updates. Regular query maintenance and keeping on top of changes in business requirements are important, but they represent only one aspect of managing an analytics program. As data volumes continue to increase and business users become more familiar with the analytics process, more questions that they want answered will inevitably arise. The analytics team must be able to keep up with the additional requests in a timely fashion. Also, one of the requirements when evaluating big data analytics hardware and software options is assessing their ability to support iterative development processes in dynamic business environments. An analytics system will retain its value over time if it can adapt to changing requirements.

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Keep your users in mind -- all of them. With interest growing in self-service business intelligence (BI) capabilities, it shouldn’t be shocking that a focus on end users is a key factor in big data analytics programs. Having a robust IT infrastructure that can handle large data sets and both structured and unstructured information is important, of course. But so is developing a system that is usable and easy to interact with, and doing so means taking the varying needs of users into account. Different types of people -- from senior executives to operational workers, business analysts and statisticians -- will be accessing big data analytics applications in one way or another, and their adoption of the tools will help ensure overall project success. That requires different levels of interactivity that match user expectations and the amount of experience they have with analytics tools -- for instance, building dashboards and data visualizations to present findings in an easy-to-understand way to business managers and workers who aren’t inclined to run their own big data analytics queries.

There’s no one way to ensure big data analytics success. But following a set of frameworks and best practices, including the tips outlined above, can help organizations keep their big data initiatives on track. The technical details of a big data installation are quite intensive and need to be looked at and considered in an in-depth manner. That isn’t enough, though: Both the technical aspects and the business factors need to be taken into account to make sure that organizations get the desired outcomes from their big data analytics investments.

ABOUT THE AUTHOR
Lyndsay Wise
is president and founder of WiseAnalytics, an independent analyst firm based in Toronto that focuses on business intelligence, master data management and unstructured data management.

This was first published in March 2012

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Essential Guide

Guide to big data analytics tools, trends and best practices

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