Navigating The Wild West of Data Analytics: 6 Steps To Help You Get There
ADOTAS – Data analytics are top of mind amidst the increasing pressures of big data: from the proliferation of mobile devices and social media channels to the explosion of non-human devices and machine-generated data. According to Forrester Research’s “The State of Digital Customer Experience Technology,” nearly 60% of professionals are making analytic strategies and tools top priorities over the next two years.
I often find companies re-evaluating their analytics solutions when data volumes start to outpace storage capacity. Unfortunately, for far too many companies, their existing database solutions are an expensive, cumbersome and menacing hurdle between a growing pile of data and agile analytics. As data connections and dependencies grow, organizations need new ways to interrogate highly diverse types of fast-moving, high-volume data to drive business value.
Yet, where should you start? As you rethink how to capture and derive business insight from your data, you might want to consider the following checklist to get you on the right path.
1. Identify goals. What does success look like for you? Do you want to acquire new customers? Boost engagement of existing customers? Increase reach and influence of your top brand advocates? To keep big data analysis manageable, it’s critical to map data needs to bigger picture business goals. The one-size-fits-all approach no longer works; big data has created pockets of specialization, where some databases are great for warehousing, while others excel at analytics.
In addition, don’t bite off too much at once; the most effective analytics grow organically. For example, with an “investigative analytics” approach, you can ask a series of quickly changing, iterative questions to figure out why something did or did not happen and how to optimize a particular outcome in the future. Instead of a hardened approach that demands rigid KPIs and canned reports, create a flexible technology foundation that allows for insight into questions you haven’t even contemplated yet.
2. Determine key players. Data analysis is no longer simply the domain of the CIO and IT. There is growing conflict between IT and the business users, but that does not need to be the case. Data drives decision making across marketing, sales, business development… anyone who has a stake in the company’s success. As a result, analytics solutions should offer cross-functional support and ease of use that makes data accessible to the right people. Conduct a thorough review, starting with C-level leadership, to figure out which departments need to be involved. Interview each group to find out how often they will be accessing data and who has ownership of analytics.
3. Set budget range. Scoping out analytics requirements will help point you to the right solution. Determine the budget range, but don’t assume the right answer is on the high end – chances are you don’t need to invest in an army of DBAs. Data analysis can be surprisingly affordable, easy to use and easy to implement. Look for low-touch solutions optimized to deliver fast analysis of large volumes of data, with minimal hardware, administrative effort or customization needed to set-up or change query and reporting parameters. After all, you don’t want to implement an analytic solution that will tax your technology organization’s support staff whenever you need to alter queries.
4. Shift your focus from “what?” to “why?” Today’s powerful analytics call for a new mindset around what’s possible to uncover within your data. How many cars did we sell in North America compared to Asia in 2013, and what will happen in 2014? While the “what” is still important, the “why” is critical to identifying patterns of behavior and consumer insights to capitalize on: Why did we sell more SUVs to an older demographic in the U.S., but to a much younger demographic in Japan? (Maybe it’s linked to conversions from a targeted social media campaign.) Analytics solutions that allow for open-ended data interrogation hold the key to getting deeper, richer insight from social, mobile and machine-generated data sources.
5. Determine technology platform requirements. For many companies trying to stand out amidst the onslaught of social media and online advertising noise, speed, scale and real-time visualization of data are paramount. With the right mix of solutions, businesses are able to analyze months’ worth of data with sub- second response time and realize extraordinary business value from performing deep analysis with queries created on the fly.
6. Continually evolve. While it’s critical to continually evaluate and adapt systems, this does not necessarily mean a wholesale change in strategy or complete overhaul of processes. Since markets, vendors, and the data itself are constantly changing, stay attuned to new technology developments and use cases, setting yourself up to quickly respond and change as needed.
True insight hinges on accessible big data analytics that tell you what people want, when they want it, and through which channel. Hopefully with these steps, you’ll be putting your organization into the best possible position to gain the insight you need from the rising tide of data.
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