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Three Questions to Ask Your Advanced-Analytics Team

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Here’s something that senior managers should keep in mind as they launch Big Data initiatives: Advanced analytics is mostly about finding relationships between different sets of data. The leader’s first job is to make sure the organization has the tools to do that.

Three simple, high-level questions can help you guide progress on that front — and keep people focused on that central task. In a later post, I’ll propose a second set of questions that arise when the organization is deeper into its Big Data initiatives.

1. How are we going to coordinate multi-channel data?

Businesses operate in more spheres than ever — in-store, in-person, telephonic, web, mobile, and social channels. Collecting data from each of these channels is important, but so is coordinating that data. Say you’re a manager at a consumer retail store — how many web customers also purchase at your brick and mortar stores, and how often?

One solution here is a common cross-channel identifier. At Quovo, we’ve built an investment analysis platform that aggregates investors’ accounts from across multiple custodians and brokerages into one customer profile. This allows investors to easily run analyses on the full picture of their investments — no matter where the data is housed.

Ultimately, that’s the value of a common identifier for any business: a fuller picture of related data under a single listing. In the retail example, a single registration account for web and mobile commerce can help consolidate data from both channels in order to give a better picture of a customer’s online shopping. Even more broadly, a customer loyalty program can help, since it gives consumers a unique ID that they apply to every purchase, regardless of the channel. Drug stores like CVS and Walgreens have been using this system for years to track customer behavior and get a full picture of purchasing patterns, loyalty trends, and lifetime customer value.

A final note: common identifiers are useful for any organization, but may be particularly important for large organizations that manage multiple systems or have grown through acquisitions. In this case, shared identifiers can help to bridge different data sets and systems that otherwise might have trouble “speaking” to each other.

2. How are we going to deal with unstructured data?

If your organization wants to get serious about fully mining the value of data, then addressing unstructured data is a must. Unstructured data is messy, qualitative data (think e-mails, notes, PDF statements, transcripts, legal documents, multimedia, etc.) that doesn’t fit nicely into standardized quantitative formats. It can hold rich insights — a commonly-cited example being doctors’ hand-written clinical notes, which often contain the most important information about patient conditions.

There a few different ways to begin thinking about capturing unstructured data. Your database systems can have room for form fields, comments, or attachments; these allow unstructured sources and files to be appended to records. Metadata and taxonomies are also useful. Metadata is data about data — tagging specific listings or records with descriptions to help categorize otherwise idiosyncratic content. Taxonomies are about organizing data hierarchically through common characteristics. In the example of medical records, you could tag patient records having high levels of cholesterol (this tag would be an example of metadata) and then set up your data governance to be able to drill down into this group by gender, and within gender, by age (the ability to support this increasing granularity within a category is an example of taxonomies).

3. How can we create the data we need from the data we have?

Ultimately, data analytics are only useful if they help you make smarter business decisions — but the data you have may not be as relevant to those decisions as it needs to be. Businesses need to think hard about which variables or combination of variables are the most salient for key business decisions.

Auto insurance providers deal with this issue every day, as I discovered during my work in the sector with LexisNexis. Today, many insurance carriers are piloting telematics programs, which track policyholders’ driving patterns in real time through in-car devices. This telematics data is then entered into actuarial models to predict driving risk (and thus insurance premiums). The idea is that direct driving behavior over time will be more predictive than traditional proxies such as age, credit rating, or geography. While this seems like a logical assumption, the real question isn’t whether driving behavior is more predictive than traditional proxies — but whether driving behavior combined with traditional proxies is most predictive of all.

For insurers, transforming these data into their most usable form may require the creation of new composite variables or scores from the existing data — something like a driving risk score that gives weight to telematics data, geography, and credit score. The beauty of this approach is that it consolidates multiple, unique data streams into one usable metric that speaks directly to a critical business decision — whom to insure, and for how much. What’s the equivalent of a driving score for your organization?

Big Data is complicated stuff, and the three questions discussed here aren’t the end of the road. But they do speak to the strategic mindset that senior managers must keep in order to get the most out of advanced analytics — and generate a rich, layered data context around the messy realities of business.

_____________________

BIG DATA INSIGHT CENTER

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Link: Three Questions to Ask Your Advanced-Analytics Team

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