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6 steps to a successful analytics upgrade
With traditional analytics systems maxed out by big data, start piloting the right solutions—before your competitors do.
The secrets to be found in big data can touch every corner of the business. In a big data world, there's a smarter way to do just about everything.
Take sales and marketing: A major power shift has dramatically changed your relationship with your customers. Today's proactive consumers tend to discount—or bypass entirely—traditional advertising and marketing messages. Instead they research their spending decisions on the Internet. When they do use traditional media, they frequently bypass advertising.
It becomes harder to get the right message to the right customer at the right time. But at the core, both the challenge and the solution are fueled by smart use of greater information management resources.
Your customers are in control of the relationship; companies need to be able to micro-tailor their marketing techniques and, in many cases, the products themselves—insight that a cutting-edge analytics tool can provide.
Missing out on the big data promise
Traditional data analytics systems are often no help at mining the increasing amount of data that can provide highly customized messaging and similar insights.
Business and marketing leaders can’t execute on new ideas to generate more revenue because IT can’t support their requests to add new data sources to existing queries. Even the traditional reports that foster the status quo are taking far too long to run, resulting in missed business opportunities.
Breaking the stalemate
The race to draw more value from rising mountains of data has already begun. A next-generation data analytics system is clearly needed, and those who delay will find it increasingly difficult to compete.
The marketplace for analytics systems that can handle big data volume is still developing, but sitting on the sidelines isn’t an option. These six guidelines can help you find the right big data decision analytics system for your organization:
- Run a discrete pilot program. A formal pilot will prove the value of the investment in a powerful way, and let you discover idiosyncrasies in your data or architecture that you’ll need to accommodate before full rollout. The pilot must provide real value in solving a critical data problem without disrupting the day-to-day business.
- Concentrate on a single data issue (or a small cluster of issues) that has momentous business impact. Limiting scope to a single problem will let you arrive at the right solution more quickly, and will prevent unproductive complications. Moreover, a high-impact project will help secure enthusiastic support.
- Work closely with line of business managers and let them drive the requirements. Rely on business leaders to determine when the new solution is achieving the desired results and performance levels. Tap their insight about what the enterprise needs today, and how they expect those needs to evolve.
- Use live data. It may be a pilot but it is not experimental. Get real results whenever possible so you can see how the solution will perform in production.
- Consider scalability issues and infrastructure preferences up front. Architectural flexibility can be critical to an analytics solution. Seasonal data spikes, for example, are a good reason to seek a solution that’s compatible with cloud system architecture or one that can use off-the-shelf hardware.
- Examine the complete workflow. Make sure that the solution you choose fits into your existing business intelligence environment and will not require IT or business users to be retrained in the tools they currently use to interact with data.
Making your data work
By storing massive amounts of data from innumerable sources, organizations possess prodigious business intelligence potential. Yet few are getting the insights they need.
Teasing out these potential insights from all the raw data we store is a game-changing opportunity for those who dare to take action in a nascent marketplace.
To learn more about how next-generation data analysis can be a critical differentiator in today's fast-paced business world, visit the Vertica site.
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