Now That We've Found Big Data, What Are We Gonna Do with It?

Big data is a big deal. As more businesses and organizations integrate their workflows with technology, they’re being told that the data they’re producing could be a treasure trove of insight and intelligence.

There’s just one problem: Who or what is going to parse all that data?

In a guest post on TechCrunch, John De Goes, co-founder and CEO of analytics platform Precog, says companies need to build data-driven processes and seek out data-driven solutions.

De Goes suggests that companies hire a data scientist to comb through the data pool to make some actionable recommendations.

He highlights a few real-world insights data scientists can provide for any company:

• If you sell a SaaS application, a data scientist can help you identify the common characteristics of high-revenue users. For example, they make take particular pathways during conversion to a paid account, and they may share particular demographic attributes (gender, income, location, age range, etc.), and use the product in specific ways. All these insights can help you refine advertising, marketing, and product to increase revenue.

• A data scientist can identify to what extent one pricing tier or product is cannibalizing sales from other pricing tiers or products, so you can optimize your pricing strategy and product lines.

• A data scientist can build a predictive model based on historical data that lets you make fairly accurate predictions. For example, you could identify which customers are likely to be female and pregnant (something Target has done), or identify which leads in a sales pipeline are most likely to convert and at what levels.

• A data scientist can help you figure out the right questions to ask about your data. For example, a data scientist might suggest correlating your marketing data to your web log data to your transactional data, to identify the ROI behind marketing campaigns.

Hiring a data scientist will give the company a big data bloodhound, which is essential because the data is useless if it has no direction or shape. Most companies wouldn’t file their taxes without an accountant, so why do some of those same companies think they can forge ahead through big data without a data scientist?

Once big data is attached to business processes, its value in the enterprise becomes clear: By tracking, monitoring and analyzing every facet of operation, companies can maximize revenue, performance and efficiencies.

Is the big deal about big data a little clearer now?