We’ve heard a lot about Big Data over the past several years. It’s been one of the biggest topics in IT and practically every enterprise has been clamoring to get on-board the Big Data train. Now that some of the hype has died down and been replaced by the Internet of Things, organizations are stepping back to re-evaluate their processes of capturing and analyzing Big Data. Reaching an impasse, many companies have begun to realize the only way they’re ever going to make any sense of the enormity of the data out there, is through better tools, processes, and smarter strategies for capturing and analyzing in real-time all that’s available.
Big Data is fine and good but it doesn’t do any good without digging into what all the noise or information means. Businesses have been using business intelligence techniques for years to get at what the historical data means in the here and now. But as the amounts of data have grown to ginormous proportions in recent years, new tools have come online to help companies and individuals discern trends, patterns, and predictive insights around that data in order to drive new business value.
One of the key terms you often find in discussions of Big Data today is “advanced analytics.” This really refers to the application category of techniques such as predictive analytics, data mining, big data analytics, which are applied to driving changes and improvements in business practices.
Advanced analytics can seem a bit daunting, especially to newbies. The purpose here is not to get lost in the complexity, but rather to highlight the essentials of how to jump-start your advanced analytics strategy to help solve your fundamental business problems.
1. Don’t try to boil the ocean
Try to start with a simple business problem and show how advanced analytics can be applied to realize a tangible business outcome. Don’t feel the need to be a hero and set up a Hadoop stack in your organization. Instead, develop a modest business case and frame up a simple proof of concept that can show your stakeholders the value of advanced analytics.
2. Identify your business problem
What is the primary question you wish to solve? Look across all lines of your business to see where the pain points are. What are your highest expenditures? What steps need to be taken to improve the bottom line? For instance, you may decide: “We want to identify the top 10,000 customers who are most likely to respond to our mailers.”
3. Understand your data
Start by asking some basic questions: where does the data reside, who owns it, how far back, and in what format? There are also growing numbers of companies on the market that specialize in helping businesses to analyze their existing data, or else to generate new data, in order to derive a 360 degree view of their customer interactions and behavior.
4. Clean your data
Disparate sets of data often contain duplicate, wrong and missing values, and other inconsistencies. Your data sets will need to be validated or “cleansed” to ensure optimal outcomes. Leverage available software tools to make this process less painstaking. Fortunately, there are many tools on the market that help automate the data preparation and cleansing process using graphical ETL (Extract, transform, and load) capabilities.
5. Kickoff your analysis:
This may be as straightforward as using a Real User Monitoring tool from Monitis to get insights on where your web traffic is coming from, what pages visitors are looking at, on what devices, and what the general traffic patterns are looking like. If you’re doing email marketing with a tool like MailChimp, then start by reviewing your email open rate statistics. How about leveraging the growing numbers of predictive analytics tools on the market today. If someone in your company has Big Data expertise, then leverage their skills to help you develop some advanced analytics techniques and modeling. Whatever approach you take to advanced analytics the best advice is to start small, gather some results, and scale up. Small clean data sets are preferable to large ones that have lots of “noise”. Train your data with smaller, but accurate sets, and then scale up from there.
6. Act on your findings
So you’ve achieved some analysis on your basic business problem. Now ask yourself about the basic action your analysis is driving at? If your data shows more email open rates on Wednesday, then try starting your next few email marketing campaigns on a Wednesday to see if your results match the initial findings. If you’re finding that a good number of visitors are using mobile devices to access your website, then take measures to ensure that your business is mobile friendly. Is your website responsive web enabled for different devices? Your initial actions may not lead to the results you expected, and that’s okay. Just keep trying and don’t give up! Also, be sure to get commitment up front from other business stakeholders to ensure that the results of your advanced analytics initiative can be appropriately scaled up within the organization.