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When Big Data is Bad Data

Everybody wants to use big data. Together with cloud technology, it’s the hottest topic in boardrooms, the media, and all over the Internet. From gigantic servers and complicated systems, to plain old mobile devices and tiny sensors, big data feeds from multiple sources at an alarming rate, helping us make smarter business decisions and optimize operations.

While all of this sounds great, there is a fundamental flaw in the process that some organizations don’t like to admit – their big data is bad data.

I recently wrote about the feedback loop that you can embed in your CRM system with the goal of increasing adoption rates, maximizing ROI, and breaking down siloes in the organization to permeate a culture of reciprocity. But no matter what your strategic initiatives are, if bad data creeps in, it will misinform, misdirect, and mislead you.

Composite image of businessman standing back to camera hands on head

Data-Driven Pulse

People want to use data as guide posts. Maybe because the market instills these concepts (Big Data, Data-Driven, etc.), or perhaps leaders realize that instincts only take an organization so far. In a survey by Reltio, they explore the use of data in Life Science organizations.

They found that on average 55% of the Life Science organizations surveyed would rate themselves as very data-driven. Further down the infographic is a startling misalignment: 74% are worried that their data is incomplete or missing.

That’s a huge problem when you consider the majority of data is from external sources, which is industry standard. The issue is exacerbated by information siloes and not properly using the existing customer master solution.

Heart of the Organization

Medical design

Every organization needs good data, and we discussed some of the next generation ideas with Master Data Management (MDM) on the DM Radio Broadcast. There truly are some fascinating applications and better ways to manage, track, store, and report enterprise information. Further, the power of interaction and social data will only enhance data sets and give organizations better information on connecting their customer and supplier webs.

These are empty promises unless we solve the data quality problem. Next generation tools are useless when the basics pose as major challenges.

Investing in MDM can give an organization the quality, rigor, and guard rails to approach data the right way, instead of haphazard and isolated data sources.

This is the starting point for any organization to truly get to where they want to be, streamlining information on suppliers and customers.

Pumping Insights

With good data, now is the time to analyze and provide the insights the organization needs to make good decisions.

Don’t put the cart before the horse and analyze bad data.

Furthermore, organizations that incentivize their employees on bad data will have a hard time trying to recoup payments. Take good data, circulate it in the organization, and make it actionable with the following prescription.

Key Performance Indicators (KPIs) answer the age old question “how are we doing?” KPIs have come a long way in their delivery mechanism. Whether an exciting dashboard, a performance scorecard, or a custom presentation, the idea is to decide the criteria that best defines success and measure against it. It needs to be updated frequently and communicated timely.

While KPIs are informative, the next level insight is about supplying the action. Given a piece of information, there needs to be a next step, an interpretation on the data to give direction for the person consuming it. There are various departments that could use benefit from direction, and I’ll highlight the classic example: the sales organization.

Imagine sales people being armed with insights around customer preferences and the ability to make recommendations based on past purchase history, related customers, and social connections.

While this isn’t new for the likes of Amazon, Netflix, and many others, it seems burdensome for many organizations across industries.

Sticking to the Life Sciences example from earlier, sales people could highlight to doctors how therapy adoption can help save their patients based on insurance coverage.

Doctors themselves can leverage patient level data to track the patient journey, recognizing patterns in responses to therapy and similarity to other patients. Hospital administration can make better decisions in protocol development, saving money for both the hospital and out of pocket expenses for their patient.

Back to Basics

While the buzz of predicting and taking action has swept business, it all comes back to getting the little things right. Having accurate information on the customer and knowing when consolidation happens in your supplier network is easy to say, and difficult to put into practice.

Whether you manage it on your own or outsource the process, the goal is to have good, reliable, and clean data. When your data is bad, it affects the entire organization and can’t be ignored. Take care of your heart, the data that is at the center of your organization.

This article was originally published on LinkedIn Pulse.

One thought on “When Big Data is Bad Data

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